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LEMON: Lossless model expansion | Scaling of deep neural networks, especially Transformers, is pivotal for
their surging performance and has further led to the emergence of sophisticated
reasoning capabilities in foundation models. Such scaling generally requires
training large models from scratch with random initialization, failing to
leverage the knowledge acquired by their smaller counterparts, which are
already resource-intensive to obtain. To tackle this inefficiency, we present
$\textbf{L}$ossl$\textbf{E}$ss $\textbf{MO}$del Expansio$\textbf{N}$ (LEMON), a
recipe to initialize scaled models using the weights of their smaller but
pre-trained counterparts. This is followed by model training with an optimized
learning rate scheduler tailored explicitly for the scaled models,
substantially reducing the training time compared to training from scratch.
Notably, LEMON is versatile, ensuring compatibility with various network
structures, including models like Vision Transformers and BERT. Our empirical
results demonstrate that LEMON reduces computational costs by 56.7% for Vision
Transformers and 33.2% for BERT when compared to training from scratch. | [
"Yite Wang",
"Jiahao Su",
"Hanlin Lu",
"Cong Xie",
"Tianyi Liu",
"Jianbo Yuan",
"Haibin Lin",
"Ruoyu Sun",
"Hongxia Yang"
] | 2023-10-12 03:02:41 | http://arxiv.org/abs/2310.07999v1 | http://arxiv.org/pdf/2310.07999v1 | 2310.07999v1 |
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning | This work identifies a simple pre-training mechanism that leads to
representations exhibiting better continual and transfer learning. This
mechanism -- the repeated resetting of weights in the last layer, which we
nickname "zapping" -- was originally designed for a meta-continual-learning
procedure, yet we show it is surprisingly applicable in many settings beyond
both meta-learning and continual learning. In our experiments, we wish to
transfer a pre-trained image classifier to a new set of classes, in a few
shots. We show that our zapping procedure results in improved transfer accuracy
and/or more rapid adaptation in both standard fine-tuning and continual
learning settings, while being simple to implement and computationally
efficient. In many cases, we achieve performance on par with state of the art
meta-learning without needing the expensive higher-order gradients, by using a
combination of zapping and sequential learning. An intuitive explanation for
the effectiveness of this zapping procedure is that representations trained
with repeated zapping learn features that are capable of rapidly adapting to
newly initialized classifiers. Such an approach may be considered a
computationally cheaper type of, or alternative to, meta-learning rapidly
adaptable features with higher-order gradients. This adds to recent work on the
usefulness of resetting neural network parameters during training, and invites
further investigation of this mechanism. | [
"Lapo Frati",
"Neil Traft",
"Jeff Clune",
"Nick Cheney"
] | 2023-10-12 02:52:14 | http://arxiv.org/abs/2310.07996v1 | http://arxiv.org/pdf/2310.07996v1 | 2310.07996v1 |
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics | Background: Missing data is a common challenge in mass spectrometry-based
metabolomics, which can lead to biased and incomplete analyses. The integration
of whole-genome sequencing (WGS) data with metabolomics data has emerged as a
promising approach to enhance the accuracy of data imputation in metabolomics
studies. Method: In this study, we propose a novel method that leverages the
information from WGS data and reference metabolites to impute unknown
metabolites. Our approach utilizes a multi-view variational autoencoder to
jointly model the burden score, polygenetic risk score (PGS), and linkage
disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature
extraction and missing metabolomics data imputation. By learning the latent
representations of both omics data, our method can effectively impute missing
metabolomics values based on genomic information. Results: We evaluate the
performance of our method on empirical metabolomics datasets with missing
values and demonstrate its superiority compared to conventional imputation
techniques. Using 35 template metabolites derived burden scores, PGS and
LD-pruned SNPs, the proposed methods achieved r2-scores > 0.01 for 71.55% of
metabolites. Conclusion: The integration of WGS data in metabolomics imputation
not only improves data completeness but also enhances downstream analyses,
paving the way for more comprehensive and accurate investigations of metabolic
pathways and disease associations. Our findings offer valuable insights into
the potential benefits of utilizing WGS data for metabolomics data imputation
and underscore the importance of leveraging multi-modal data integration in
precision medicine research. | [
"Chen Zhao",
"Kuan-Jui Su",
"Chong Wu",
"Xuewei Cao",
"Qiuying Sha",
"Wu Li",
"Zhe Luo",
"Tian Qin",
"Chuan Qiu",
"Lan Juan Zhao",
"Anqi Liu",
"Lindong Jiang",
"Xiao Zhang",
"Hui Shen",
"Weihua Zhou",
"Hong-Wen Deng"
] | 2023-10-12 02:34:56 | http://arxiv.org/abs/2310.07990v1 | http://arxiv.org/pdf/2310.07990v1 | 2310.07990v1 |
Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications | This letter proposes a novel relaying framework, semantic-forward (SF), for
cooperative communications towards the sixth-generation (6G) wireless networks.
The SF relay extracts and transmits the semantic features, which reduces
forwarding payload, and also improves the network robustness against intra-link
errors. Based on the theoretical basis for cooperative communications with side
information and the turbo principle, we design a joint source-channel coding
algorithm to iteratively exchange the extrinsic information for enhancing the
decoding gains at the destination. Surprisingly, simulation results indicate
that even in bad channel conditions, SF relaying can still effectively improve
the recovered information quality. | [
"Wensheng Lin",
"Yuna Yan",
"Lixin Li",
"Zhu Han",
"Tad Matsumoto"
] | 2023-10-12 02:32:30 | http://arxiv.org/abs/2310.07987v1 | http://arxiv.org/pdf/2310.07987v1 | 2310.07987v1 |
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization | Neural combinatorial optimization (NCO) is a promising learning-based
approach for solving challenging combinatorial optimization problems without
specialized algorithm design by experts. However, most constructive NCO methods
cannot solve problems with large-scale instance sizes, which significantly
diminishes their usefulness for real-world applications. In this work, we
propose a novel Light Encoder and Heavy Decoder (LEHD) model with a strong
generalization ability to address this critical issue. The LEHD model can learn
to dynamically capture the relationships between all available nodes of varying
sizes, which is beneficial for model generalization to problems of various
scales. Moreover, we develop a data-efficient training scheme and a flexible
solution construction mechanism for the proposed LEHD model. By training on
small-scale problem instances, the LEHD model can generate nearly optimal
solutions for the Travelling Salesman Problem (TSP) and the Capacitated Vehicle
Routing Problem (CVRP) with up to 1000 nodes, and also generalizes well to
solve real-world TSPLib and CVRPLib problems. These results confirm our
proposed LEHD model can significantly improve the state-of-the-art performance
for constructive NCO. The code is available at
https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/LEHD. | [
"Fu Luo",
"Xi Lin",
"Fei Liu",
"Qingfu Zhang",
"Zhenkun Wang"
] | 2023-10-12 02:18:50 | http://arxiv.org/abs/2310.07985v1 | http://arxiv.org/pdf/2310.07985v1 | 2310.07985v1 |
RandCom: Random Communication Skipping Method for Decentralized Stochastic Optimization | Distributed optimization methods with random communication skips are gaining
increasing attention due to their proven benefits in accelerating communication
complexity. Nevertheless, existing research mainly focuses on centralized
communication protocols for strongly convex deterministic settings. In this
work, we provide a decentralized optimization method called RandCom, which
incorporates probabilistic local updates. We analyze the performance of RandCom
in stochastic non-convex, convex, and strongly convex settings and demonstrate
its ability to asymptotically reduce communication overhead by the probability
of communication. Additionally, we prove that RandCom achieves linear speedup
as the number of nodes increases. In stochastic strongly convex settings, we
further prove that RandCom can achieve linear speedup with network-independent
stepsizes. Moreover, we apply RandCom to federated learning and provide
positive results concerning the potential for achieving linear speedup and the
suitability of the probabilistic local update approach for non-convex settings. | [
"Luyao Guo",
"Sulaiman A. Alghunaim",
"Kun Yuan",
"Laurent Condat",
"Jinde Cao"
] | 2023-10-12 02:13:48 | http://arxiv.org/abs/2310.07983v1 | http://arxiv.org/pdf/2310.07983v1 | 2310.07983v1 |
Reinforcement Learning of Display Transfer Robots in Glass Flow Control Systems: A Physical Simulation-Based Approach | A flow control system is a critical concept for increasing the production
capacity of manufacturing systems. To solve the scheduling optimization problem
related to the flow control with the aim of improving productivity, existing
methods depend on a heuristic design by domain human experts. Therefore, the
methods require correction, monitoring, and verification by using real
equipment. As system designs increase in complexity, the monitoring time
increases, which decreases the probability of arriving at the optimal design.
As an alternative approach to the heuristic design of flow control systems, the
use of deep reinforcement learning to solve the scheduling optimization problem
has been considered. Although the existing research on reinforcement learning
has yielded excellent performance in some areas, the applicability of the
results to actual FAB such as display and semiconductor manufacturing processes
is not evident so far. To this end, we propose a method to implement a physical
simulation environment and devise a feasible flow control system design using a
transfer robot in display manufacturing through reinforcement learning. We
present a model and parameter setting to build a virtual environment for
different display transfer robots, and training methods of reinforcement
learning on the environment to obtain an optimal scheduling of glass flow
control systems. Its feasibility was verified by using different types of
robots used in the actual process. | [
"Hwajong Lee",
"Chan Kim",
"Seong-Woo Kim"
] | 2023-10-12 02:10:29 | http://arxiv.org/abs/2310.07981v1 | http://arxiv.org/pdf/2310.07981v1 | 2310.07981v1 |
GRASP: Accelerating Shortest Path Attacks via Graph Attention | Recent advances in machine learning (ML) have shown promise in aiding and
accelerating classical combinatorial optimization algorithms. ML-based speed
ups that aim to learn in an end to end manner (i.e., directly output the
solution) tend to trade off run time with solution quality. Therefore,
solutions that are able to accelerate existing solvers while maintaining their
performance guarantees, are of great interest. We consider an APX-hard problem,
where an adversary aims to attack shortest paths in a graph by removing the
minimum number of edges. We propose the GRASP algorithm: Graph Attention
Accelerated Shortest Path Attack, an ML aided optimization algorithm that
achieves run times up to 10x faster, while maintaining the quality of solution
generated. GRASP uses a graph attention network to identify a smaller subgraph
containing the combinatorial solution, thus effectively reducing the input
problem size. Additionally, we demonstrate how careful representation of the
input graph, including node features that correlate well with the optimization
task, can highlight important structure in the optimization solution. | [
"Zohair Shafi",
"Benjamin A. Miller",
"Ayan Chatterjee",
"Tina Eliassi-Rad",
"Rajmonda S. Caceres"
] | 2023-10-12 02:03:10 | http://arxiv.org/abs/2310.07980v2 | http://arxiv.org/pdf/2310.07980v2 | 2310.07980v2 |
Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks | Machine learning (ML) approaches are increasingly being used to accelerate
combinatorial optimization (CO) problems. We look specifically at the Set Cover
Problem (SCP) and propose Graph-SCP, a graph neural network method that can
augment existing optimization solvers by learning to identify a much smaller
sub-problem that contains the solution space. We evaluate the performance of
Graph-SCP on synthetic weighted and unweighted SCP instances with diverse
problem characteristics and complexities, and on instances from the OR Library,
a canonical benchmark for SCP. We show that Graph-SCP reduces the problem size
by 30-70% and achieves run time speedups up to~25x when compared to commercial
solvers (Gurobi). Given a desired optimality threshold, Graph-SCP will improve
upon it or even achieve 100% optimality. This is in contrast to fast greedy
solutions that significantly compromise solution quality to achieve guaranteed
polynomial run time. Graph-SCP can generalize to larger problem sizes and can
be used with other conventional or ML-augmented CO solvers to lead to potential
additional run time improvement. | [
"Zohair Shafi",
"Benjamin A. Miller",
"Tina Eliassi-Rad",
"Rajmonda S. Caceres"
] | 2023-10-12 01:57:27 | http://arxiv.org/abs/2310.07979v1 | http://arxiv.org/pdf/2310.07979v1 | 2310.07979v1 |
Interpretable Diffusion via Information Decomposition | Denoising diffusion models enable conditional generation and density modeling
of complex relationships like images and text. However, the nature of the
learned relationships is opaque making it difficult to understand precisely
what relationships between words and parts of an image are captured, or to
predict the effect of an intervention. We illuminate the fine-grained
relationships learned by diffusion models by noticing a precise relationship
between diffusion and information decomposition. Exact expressions for mutual
information and conditional mutual information can be written in terms of the
denoising model. Furthermore, pointwise estimates can be easily estimated as
well, allowing us to ask questions about the relationships between specific
images and captions. Decomposing information even further to understand which
variables in a high-dimensional space carry information is a long-standing
problem. For diffusion models, we show that a natural non-negative
decomposition of mutual information emerges, allowing us to quantify
informative relationships between words and pixels in an image. We exploit
these new relations to measure the compositional understanding of diffusion
models, to do unsupervised localization of objects in images, and to measure
effects when selectively editing images through prompt interventions. | [
"Xianghao Kong",
"Ollie Liu",
"Han Li",
"Dani Yogatama",
"Greg Ver Steeg"
] | 2023-10-12 01:40:20 | http://arxiv.org/abs/2310.07972v1 | http://arxiv.org/pdf/2310.07972v1 | 2310.07972v1 |
Hyperparameter Adaptive Search for Surrogate Optimization: A Self-Adjusting Approach | Surrogate Optimization (SO) algorithms have shown promise for optimizing
expensive black-box functions. However, their performance is heavily influenced
by hyperparameters related to sampling and surrogate fitting, which poses a
challenge to their widespread adoption. We investigate the impact of
hyperparameters on various SO algorithms and propose a Hyperparameter Adaptive
Search for SO (HASSO) approach. HASSO is not a hyperparameter tuning algorithm,
but a generic self-adjusting SO algorithm that dynamically tunes its own
hyperparameters while concurrently optimizing the primary objective function,
without requiring additional evaluations. The aim is to improve the
accessibility, effectiveness, and convergence speed of SO algorithms for
practitioners. Our approach identifies and modifies the most influential
hyperparameters specific to each problem and SO approach, reducing the need for
manual tuning without significantly increasing the computational burden.
Experimental results demonstrate the effectiveness of HASSO in enhancing the
performance of various SO algorithms across different global optimization test
problems. | [
"Nazanin Nezami",
"Hadis Anahideh"
] | 2023-10-12 01:26:05 | http://arxiv.org/abs/2310.07970v1 | http://arxiv.org/pdf/2310.07970v1 | 2310.07970v1 |
CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip Deformity | A major obstacle when attempting to train a machine learning system to
evaluate facial clefts is the scarcity of large datasets of high-quality,
ethics board-approved patient images. In response, we have built a deep
learning-based cleft lip generator designed to produce an almost unlimited
number of artificial images exhibiting high-fidelity facsimiles of cleft lip
with wide variation. We undertook a transfer learning protocol testing
different versions of StyleGAN-ADA (a generative adversarial network image
generator incorporating adaptive data augmentation (ADA)) as the base model.
Training images depicting a variety of cleft deformities were pre-processed to
adjust for rotation, scaling, color adjustment and background blurring. The ADA
modification of the primary algorithm permitted construction of our new
generative model while requiring input of a relatively small number of training
images. Adversarial training was carried out using 514 unique frontal
photographs of cleft-affected faces to adapt a pre-trained model based on
70,000 normal faces. The Frechet Inception Distance (FID) was used to measure
the similarity of the newly generated facial images to the cleft training
dataset, while Perceptual Path Length (PPL) and the novel Divergence Index of
Severity Histograms (DISH) measures were also used to assess the performance of
the image generator that we dub CleftGAN. We found that StyleGAN3 with
translation invariance (StyleGAN3-t) performed optimally as a base model.
Generated images achieved a low FID reflecting a close similarity to our
training input dataset of genuine cleft images. Low PPL and DISH measures
reflected a smooth and semantically valid interpolation of images through the
transfer learning process and a similar distribution of severity in the
training and generated images, respectively. | [
"Abdullah Hayajneh",
"Erchin Serpedin",
"Mohammad Shaqfeh",
"Graeme Glass",
"Mitchell A. Stotland"
] | 2023-10-12 01:25:21 | http://arxiv.org/abs/2310.07969v1 | http://arxiv.org/pdf/2310.07969v1 | 2310.07969v1 |
Towards Causal Deep Learning for Vulnerability Detection | Deep learning vulnerability detection has shown promising results in recent
years. However, an important challenge that still blocks it from being very
useful in practice is that the model is not robust under perturbation and it
cannot generalize well over the out-of-distribution (OOD) data, e.g., applying
a trained model to unseen projects in real world. We hypothesize that this is
because the model learned non-robust features, e.g., variable names, that have
spurious correlations with labels. When the perturbed and OOD datasets no
longer have the same spurious features, the model prediction fails. To address
the challenge, in this paper, we introduced causality into deep learning
vulnerability detection. Our approach CausalVul consists of two phases. First,
we designed novel perturbations to discover spurious features that the model
may use to make predictions. Second, we applied the causal learning algorithms,
specifically, do-calculus, on top of existing deep learning models to
systematically remove the use of spurious features and thus promote causal
based prediction. Our results show that CausalVul consistently improved the
model accuracy, robustness and OOD performance for all the state-of-the-art
models and datasets we experimented. To the best of our knowledge, this is the
first work that introduces do calculus based causal learning to software
engineering models and shows it's indeed useful for improving the model
accuracy, robustness and generalization. Our replication package is located at
https://figshare.com/s/0ffda320dcb96c249ef2. | [
"Md Mahbubur Rahman",
"Ira Ceka",
"Chengzhi Mao",
"Saikat Chakraborty",
"Baishakhi Ray",
"Wei Le"
] | 2023-10-12 00:51:06 | http://arxiv.org/abs/2310.07958v2 | http://arxiv.org/pdf/2310.07958v2 | 2310.07958v2 |
Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines | Researchers have long touted a vision of the future enabled by a
proliferation of internet-of-things devices, including smart sensors, homes,
and cities. Increasingly, embedding intelligence in such devices involves the
use of deep neural networks. However, their storage and processing requirements
make them prohibitive for cheap, off-the-shelf platforms. Overcoming those
requirements is necessary for enabling widely-applicable smart devices. While
many ways of making models smaller and more efficient have been developed,
there is a lack of understanding of which ones are best suited for particular
scenarios. More importantly for edge platforms, those choices cannot be
analyzed in isolation from cost and user experience. In this work, we
holistically explore how quantization, model scaling, and multi-modality
interact with system components such as memory, sensors, and processors. We
perform this hardware/software co-design from the cost, latency, and
user-experience perspective, and develop a set of guidelines for optimal system
design and model deployment for the most cost-constrained platforms. We
demonstrate our approach using an end-to-end, on-device, biometric user
authentication system using a $20 ESP-EYE board. | [
"Ravit Sharma",
"Wojciech Romaszkan",
"Feiqian Zhu",
"Puneet Gupta",
"Ankur Mehta"
] | 2023-10-11 23:22:30 | http://arxiv.org/abs/2310.07940v2 | http://arxiv.org/pdf/2310.07940v2 | 2310.07940v2 |
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning | Analytical theories suggest that higher-quality data can lead to lower test
errors in models trained on a fixed data budget. Moreover, a model can be
trained on a lower compute budget without compromising performance if a dataset
can be stripped of its redundancies. Coreset selection (or data pruning) seeks
to select a subset of the training data so as to maximize the performance of
models trained on this subset, also referred to as coreset. There are two
dominant approaches: (1) geometry-based data selection for maximizing data
diversity in the coreset, and (2) functions that assign difficulty scores to
samples based on training dynamics. Optimizing for data diversity leads to a
coreset that is biased towards easier samples, whereas, selection by difficulty
ranking omits easy samples that are necessary for the training of deep learning
models. This demonstrates that data diversity and importance scores are two
complementary factors that need to be jointly considered during coreset
selection. We represent a dataset as an undirected graph and propose a novel
pruning algorithm, D2 Pruning, that uses forward and reverse message passing
over this dataset graph for coreset selection. D2 Pruning updates the
difficulty scores of each example by incorporating the difficulty of its
neighboring examples in the dataset graph. Then, these updated difficulty
scores direct a graph-based sampling method to select a coreset that
encapsulates both diverse and difficult regions of the dataset space. We
evaluate supervised and self-supervised versions of our method on various
vision and language datasets. Results show that D2 Pruning improves coreset
selection over previous state-of-the-art methods for up to 70% pruning rates.
Additionally, we find that using D2 Pruning for filtering large multimodal
datasets leads to increased diversity in the dataset and improved
generalization of pretrained models. | [
"Adyasha Maharana",
"Prateek Yadav",
"Mohit Bansal"
] | 2023-10-11 23:01:29 | http://arxiv.org/abs/2310.07931v1 | http://arxiv.org/pdf/2310.07931v1 | 2310.07931v1 |
Enhanced sampling of Crystal Nucleation with Graph Representation Learnt Variables | In this study, we present a graph neural network-based learning approach
using an autoencoder setup to derive low-dimensional variables from features
observed in experimental crystal structures. These variables are then biased in
enhanced sampling to observe state-to-state transitions and reliable
thermodynamic weights. Our approach uses simple convolution and pooling
methods. To verify the effectiveness of our protocol, we examined the
nucleation of various allotropes and polymorphs of iron and glycine from their
molten states. Our graph latent variables when biased in well-tempered
metadynamics consistently show transitions between states and achieve accurate
free energy calculations in agreement with experiments, both of which are
indicators of dependable sampling. This underscores the strength and promise of
our graph neural net variables for improved sampling. The protocol shown here
should be applicable for other systems and with other sampling methods. | [
"Ziyue Zou",
"Pratyush Tiwary"
] | 2023-10-11 22:52:27 | http://arxiv.org/abs/2310.07927v1 | http://arxiv.org/pdf/2310.07927v1 | 2310.07927v1 |
First-Order Dynamic Optimization for Streaming Convex Costs | This paper proposes a set of novel optimization algorithms for solving a
class of convex optimization problems with time-varying streaming cost
function. We develop an approach to track the optimal solution with a bounded
error. Unlike the existing results, our algorithm is executed only by using the
first-order derivatives of the cost function which makes it computationally
efficient for optimization with time-varying cost function. We compare our
algorithms to the gradient descent algorithm and show why gradient descent is
not an effective solution for optimization problems with time-varying cost.
Several examples including solving a model predictive control problem cast as a
convex optimization problem with a streaming time-varying cost function
demonstrate our results. | [
"M. Rostami",
"H. Moradian",
"S. S. Kia"
] | 2023-10-11 22:41:00 | http://arxiv.org/abs/2310.07925v1 | http://arxiv.org/pdf/2310.07925v1 | 2310.07925v1 |
The Expressive Power of Transformers with Chain of Thought | Recent theoretical work has identified surprisingly simple reasoning
problems, such as checking if two nodes in a graph are connected or simulating
finite-state machines, that are provably unsolvable by standard transformers
that answer immediately after reading their input. However, in practice,
transformers' reasoning can be improved by allowing them to use a "chain of
thought" or "scratchpad", i.e., generate and condition on a sequence of
intermediate tokens before answering. Motivated by this, we ask: Does such
intermediate generation fundamentally extend the computational power of a
decoder-only transformer? We show that the answer is yes, but the amount of
increase depends crucially on the amount of intermediate generation. For
instance, we find that transformer decoders with a logarithmic number of
decoding steps (w.r.t. the input length) push the limits of standard
transformers only slightly, while a linear number of decoding steps adds a
clear new ability (under standard complexity conjectures): recognizing all
regular languages. Our results also imply that linear steps keep transformer
decoders within context-sensitive languages, and polynomial steps make them
recognize exactly the class of polynomial-time solvable problems -- the first
exact characterization of a type of transformers in terms of standard
complexity classes. Together, our results provide a nuanced framework for
understanding how the length of a transformer's chain of thought or scratchpad
impacts its reasoning power. | [
"William Merrill",
"Ashish Sabharwal"
] | 2023-10-11 22:35:18 | http://arxiv.org/abs/2310.07923v3 | http://arxiv.org/pdf/2310.07923v3 | 2310.07923v3 |
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning | Interpretable policy learning seeks to estimate intelligible decision
policies from observed actions; however, existing models fall short by forcing
a tradeoff between accuracy and interpretability. This tradeoff limits
data-driven interpretations of human decision-making process. e.g. to audit
medical decisions for biases and suboptimal practices, we require models of
decision processes which provide concise descriptions of complex behaviors.
Fundamentally, existing approaches are burdened by this tradeoff because they
represent the underlying decision process as a universal policy, when in fact
human decisions are dynamic and can change drastically with contextual
information. Thus, we propose Contextualized Policy Recovery (CPR), which
re-frames the problem of modeling complex decision processes as a multi-task
learning problem in which complex decision policies are comprised of
context-specific policies. CPR models each context-specific policy as a linear
observation-to-action mapping, and generates new decision models
$\textit{on-demand}$ as contexts are updated with new observations. CPR is
compatible with fully offline and partially observable decision environments,
and can be tailored to incorporate any recurrent black-box model or
interpretable decision model. We assess CPR through studies on simulated and
real data, achieving state-of-the-art performance on the canonical tasks of
predicting antibiotic prescription in intensive care units ($+22\%$ AUROC vs.
previous SOTA) and predicting MRI prescription for Alzheimer's patients
($+7.7\%$ AUROC vs. previous SOTA). With this improvement in predictive
performance, CPR closes the accuracy gap between interpretable and black-box
methods for policy learning, allowing high-resolution exploration and analysis
of context-specific decision models. | [
"Jannik Deuschel",
"Caleb N. Ellington",
"Benjamin J. Lengerich",
"Yingtao Luo",
"Pascal Friederich",
"Eric P. Xing"
] | 2023-10-11 22:17:37 | http://arxiv.org/abs/2310.07918v1 | http://arxiv.org/pdf/2310.07918v1 | 2310.07918v1 |
A Review of Machine Learning Techniques in Imbalanced Data and Future Trends | For over two decades, detecting rare events has been a challenging task among
researchers in the data mining and machine learning domain. Real-life problems
inspire researchers to navigate and further improve data processing and
algorithmic approaches to achieve effective and computationally efficient
methods for imbalanced learning. In this paper, we have collected and reviewed
258 peer-reviewed papers from archival journals and conference papers in an
attempt to provide an in-depth review of various approaches in imbalanced
learning from technical and application perspectives. This work aims to provide
a structured review of methods used to address the problem of imbalanced data
in various domains and create a general guideline for researchers in academia
or industry who want to dive into the broad field of machine learning using
large-scale imbalanced data. | [
"Elaheh Jafarigol",
"Theodore Trafalis"
] | 2023-10-11 22:14:17 | http://arxiv.org/abs/2310.07917v1 | http://arxiv.org/pdf/2310.07917v1 | 2310.07917v1 |
Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot Learning | In the realm of robotics, numerous downstream robotics tasks leverage machine
learning methods for processing, modeling, or synthesizing data. Often, this
data comprises variables that inherently carry geometric constraints, such as
the unit-norm condition of quaternions representing rigid-body orientations or
the positive definiteness of stiffness and manipulability ellipsoids. Handling
such geometric constraints effectively requires the incorporation of tools from
differential geometry into the formulation of machine learning methods. In this
context, Riemannian manifolds emerge as a powerful mathematical framework to
handle such geometric constraints. Nevertheless, their recent adoption in robot
learning has been largely characterized by a mathematically-flawed
simplification, hereinafter referred to as the ``single tangent space fallacy".
This approach involves merely projecting the data of interest onto a single
tangent (Euclidean) space, over which an off-the-shelf learning algorithm is
applied. This paper provides a theoretical elucidation of various
misconceptions surrounding this approach and offers experimental evidence of
its shortcomings. Finally, it presents valuable insights to promote best
practices when employing Riemannian geometry within robot learning
applications. | [
"Noémie Jaquier",
"Leonel Rozo",
"Tamim Asfour"
] | 2023-10-11 21:16:01 | http://arxiv.org/abs/2310.07902v1 | http://arxiv.org/pdf/2310.07902v1 | 2310.07902v1 |
NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration | Robotic learning for navigation in unfamiliar environments needs to provide
policies for both task-oriented navigation (i.e., reaching a goal that the
robot has located), and task-agnostic exploration (i.e., searching for a goal
in a novel setting). Typically, these roles are handled by separate models, for
example by using subgoal proposals, planning, or separate navigation
strategies. In this paper, we describe how we can train a single unified
diffusion policy to handle both goal-directed navigation and goal-agnostic
exploration, with the latter providing the ability to search novel
environments, and the former providing the ability to reach a user-specified
goal once it has been located. We show that this unified policy results in
better overall performance when navigating to visually indicated goals in novel
environments, as compared to approaches that use subgoal proposals from
generative models, or prior methods based on latent variable models. We
instantiate our method by using a large-scale Transformer-based policy trained
on data from multiple ground robots, with a diffusion model decoder to flexibly
handle both goal-conditioned and goal-agnostic navigation. Our experiments,
conducted on a real-world mobile robot platform, show effective navigation in
unseen environments in comparison with five alternative methods, and
demonstrate significant improvements in performance and lower collision rates,
despite utilizing smaller models than state-of-the-art approaches. For more
videos, code, and pre-trained model checkpoints, see
https://general-navigation-models.github.io/nomad/ | [
"Ajay Sridhar",
"Dhruv Shah",
"Catherine Glossop",
"Sergey Levine"
] | 2023-10-11 21:07:14 | http://arxiv.org/abs/2310.07896v1 | http://arxiv.org/pdf/2310.07896v1 | 2310.07896v1 |
Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs | This paper presents a method to efficiently classify the gastroenterologic
section of images derived from Video Capsule Endoscopy (VCE) studies by
exploring the combination of a Convolutional Neural Network (CNN) for
classification with the time-series analysis properties of a Hidden Markov
Model (HMM). It is demonstrated that successive time-series analysis identifies
and corrects errors in the CNN output. Our approach achieves an accuracy of
$98.04\%$ on the Rhode Island (RI) Gastroenterology dataset. This allows for
precise localization within the gastrointestinal (GI) tract while requiring
only approximately 1M parameters and thus, provides a method suitable for low
power devices | [
"Julia Werner",
"Christoph Gerum",
"Moritz Reiber",
"Jörg Nick",
"Oliver Bringmann"
] | 2023-10-11 21:07:04 | http://arxiv.org/abs/2310.07895v1 | http://arxiv.org/pdf/2310.07895v1 | 2310.07895v1 |
Efficient Integrators for Diffusion Generative Models | Diffusion models suffer from slow sample generation at inference time.
Therefore, developing a principled framework for fast deterministic/stochastic
sampling for a broader class of diffusion models is a promising direction. We
propose two complementary frameworks for accelerating sample generation in
pre-trained models: Conjugate Integrators and Splitting Integrators. Conjugate
integrators generalize DDIM, mapping the reverse diffusion dynamics to a more
amenable space for sampling. In contrast, splitting-based integrators, commonly
used in molecular dynamics, reduce the numerical simulation error by cleverly
alternating between numerical updates involving the data and auxiliary
variables. After extensively studying these methods empirically and
theoretically, we present a hybrid method that leads to the best-reported
performance for diffusion models in augmented spaces. Applied to Phase Space
Langevin Diffusion [Pandey & Mandt, 2023] on CIFAR-10, our deterministic and
stochastic samplers achieve FID scores of 2.11 and 2.36 in only 100 network
function evaluations (NFE) as compared to 2.57 and 2.63 for the best-performing
baselines, respectively. Our code and model checkpoints will be made publicly
available at \url{https://github.com/mandt-lab/PSLD}. | [
"Kushagra Pandey",
"Maja Rudolph",
"Stephan Mandt"
] | 2023-10-11 21:04:42 | http://arxiv.org/abs/2310.07894v1 | http://arxiv.org/pdf/2310.07894v1 | 2310.07894v1 |
ASV Station Keeping under Wind Disturbances using Neural Network Simulation Error Minimization Model Predictive Control | Station keeping is an essential maneuver for Autonomous Surface Vehicles
(ASVs), mainly when used in confined spaces, to carry out surveys that require
the ASV to keep its position or in collaboration with other vehicles where the
relative position has an impact over the mission. However, this maneuver can
become challenging for classic feedback controllers due to the need for an
accurate model of the ASV dynamics and the environmental disturbances. This
work proposes a Model Predictive Controller using Neural Network Simulation
Error Minimization (NNSEM-MPC) to accurately predict the dynamics of the ASV
under wind disturbances. The performance of the proposed scheme under wind
disturbances is tested and compared against other controllers in simulation,
using the Robotics Operating System (ROS) and the multipurpose simulation
environment Gazebo. A set of six tests were conducted by combining two wind
speeds (3 m/s and 6 m/s) and three wind directions (0$^\circ$, 90$^\circ$, and
180$^\circ$). The simulation results clearly show the advantage of the
NNSEM-MPC over the following methods: backstepping controller, sliding mode
controller, simplified dynamics MPC (SD-MPC), neural ordinary differential
equation MPC (NODE-MPC), and knowledge-based NODE MPC (KNODE-MPC). The proposed
NNSEM-MPC approach performs better than the rest in 4 out of the 6 test
conditions, and it is the second best in the 2 remaining test cases, reducing
the mean position and heading error by at least 31\% and 46\% respectively
across all the test cases. In terms of execution speed, the proposed NNSEM-MPC
is at least 36\% faster than the rest of the MPC controllers. The field
experiments on two different ASV platforms showed that ASVs can effectively
keep the station utilizing the proposed method, with a position error as low as
$1.68$ m and a heading error as low as $6.14^{\circ}$ within time windows of at
least $150$s. | [
"Jalil Chavez-Galaviz",
"Jianwen Li",
"Ajinkya Chaudhary",
"Nina Mahmoudian"
] | 2023-10-11 20:55:13 | http://arxiv.org/abs/2310.07892v1 | http://arxiv.org/pdf/2310.07892v1 | 2310.07892v1 |
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks | Feature learning is thought to be one of the fundamental reasons for the
success of deep neural networks. It is rigorously known that in two-layer
fully-connected neural networks under certain conditions, one step of gradient
descent on the first layer followed by ridge regression on the second layer can
lead to feature learning; characterized by the appearance of a separated
rank-one component -- spike -- in the spectrum of the feature matrix. However,
with a constant gradient descent step size, this spike only carries information
from the linear component of the target function and therefore learning
non-linear components is impossible. We show that with a learning rate that
grows with the sample size, such training in fact introduces multiple rank-one
components, each corresponding to a specific polynomial feature. We further
prove that the limiting large-dimensional and large sample training and test
errors of the updated neural networks are fully characterized by these spikes.
By precisely analyzing the improvement in the loss, we demonstrate that these
non-linear features can enhance learning. | [
"Behrad Moniri",
"Donghwan Lee",
"Hamed Hassani",
"Edgar Dobriban"
] | 2023-10-11 20:55:02 | http://arxiv.org/abs/2310.07891v1 | http://arxiv.org/pdf/2310.07891v1 | 2310.07891v1 |
Leader-Follower Neural Networks with Local Error Signals Inspired by Complex Collectives | The collective behavior of a network with heterogeneous, resource-limited
information processing units (e.g., group of fish, flock of birds, or network
of neurons) demonstrates high self-organization and complexity. These emergent
properties arise from simple interaction rules where certain individuals can
exhibit leadership-like behavior and influence the collective activity of the
group. Motivated by the intricacy of these collectives, we propose a neural
network (NN) architecture inspired by the rules observed in nature's collective
ensembles. This NN structure contains workers that encompass one or more
information processing units (e.g., neurons, filters, layers, or blocks of
layers). Workers are either leaders or followers, and we train a
leader-follower neural network (LFNN) by leveraging local error signals and
optionally incorporating backpropagation (BP) and global loss. We investigate
worker behavior and evaluate LFNNs through extensive experimentation. Our LFNNs
trained with local error signals achieve significantly lower error rates than
previous BP-free algorithms on MNIST and CIFAR-10 and even surpass BP-enabled
baselines. In the case of ImageNet, our LFNN-l demonstrates superior
scalability and outperforms previous BP-free algorithms by a significant
margin. | [
"Chenzhong Yin",
"Mingxi Cheng",
"Xiongye Xiao",
"Xinghe Chen",
"Shahin Nazarian",
"Andrei Irimia",
"Paul Bogdan"
] | 2023-10-11 20:47:57 | http://arxiv.org/abs/2310.07885v1 | http://arxiv.org/pdf/2310.07885v1 | 2310.07885v1 |
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research | In this paper, we investigate the practical relevance of explainable
artificial intelligence (XAI) with a special focus on the producing industries
and relate them to the current state of academic XAI research. Our findings are
based on an extensive series of interviews regarding the role and applicability
of XAI along the Machine Learning (ML) lifecycle in current industrial practice
and its expected relevance in the future. The interviews were conducted among a
great variety of roles and key stakeholders from different industry sectors. On
top of that, we outline the state of XAI research by providing a concise review
of the relevant literature. This enables us to provide an encompassing overview
covering the opinions of the surveyed persons as well as the current state of
academic research. By comparing our interview results with the current research
approaches we reveal several discrepancies. While a multitude of different XAI
approaches exists, most of them are centered around the model evaluation phase
and data scientists. Their versatile capabilities for other stages are
currently either not sufficiently explored or not popular among practitioners.
In line with existing work, our findings also confirm that more efforts are
needed to enable also non-expert users' interpretation and understanding of
opaque AI models with existing methods and frameworks. | [
"Thomas Decker",
"Ralf Gross",
"Alexander Koebler",
"Michael Lebacher",
"Ronald Schnitzer",
"Stefan H. Weber"
] | 2023-10-11 20:45:49 | http://arxiv.org/abs/2310.07882v1 | http://arxiv.org/pdf/2310.07882v1 | 2310.07882v1 |
DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks | Content Delivery Networks carry the majority of Internet traffic, and the
increasing demand for video content as a major IP traffic across the Internet
highlights the importance of caching and prefetching optimization algorithms.
Prefetching aims to make data available in the cache before the requester
places its request to reduce access time and improve the Quality of Experience
on the user side. Prefetching is well investigated in operating systems,
compiler instructions, in-memory cache, local storage systems, high-speed
networks, and cloud systems. Traditional prefetching techniques are well
adapted to a particular access pattern, but fail to adapt to sudden variations
or randomization in workloads. This paper explores the use of reinforcement
learning to tackle the changes in user access patterns and automatically adapt
over time. To this end, we propose, DeePref, a Deep Reinforcement Learning
agent for online video content prefetching in Content Delivery Networks.
DeePref is a prefetcher implemented on edge networks and is agnostic to
hardware design, operating systems, and applications. Our results show that
DeePref DRQN, using a real-world dataset, achieves a 17% increase in
prefetching accuracy and a 28% increase in prefetching coverage on average
compared to baseline approaches that use video content popularity as a building
block to statically or dynamically make prefetching decisions. We also study
the possibility of transfer learning of statistical models from one edge
network into another, where unseen user requests from unknown distribution are
observed. In terms of transfer learning, the increase in prefetching accuracy
and prefetching coverage are [$30%$, $10%$], respectively. Our source code will
be available on Github. | [
"Nawras Alkassab",
"Chin-Tser Huang",
"Tania Lorido Botran"
] | 2023-10-11 20:45:46 | http://arxiv.org/abs/2310.07881v1 | http://arxiv.org/pdf/2310.07881v1 | 2310.07881v1 |
TabLib: A Dataset of 627M Tables with Context | It is well-established that large, diverse datasets play a pivotal role in
the performance of modern AI systems for text and image modalities. However,
there are no datasets for tabular data of comparable size and diversity to
those available for text and images. Thus we present "TabLib'', a compilation
of 627 million tables totaling 69 TiB, along with 867B tokens of context.
TabLib was extracted from numerous file formats, including CSV, HTML, SQLite,
PDF, Excel, and others, sourced from GitHub and Common Crawl. The size and
diversity of TabLib offer considerable promise in the table modality,
reminiscent of the original promise of foundational datasets for text and
images, such as The Pile and LAION. | [
"Gus Eggert",
"Kevin Huo",
"Mike Biven",
"Justin Waugh"
] | 2023-10-11 20:34:42 | http://arxiv.org/abs/2310.07875v1 | http://arxiv.org/pdf/2310.07875v1 | 2310.07875v1 |
Refined Mechanism Design for Approximately Structured Priors via Active Regression | We consider the problem of a revenue-maximizing seller with a large number of
items $m$ for sale to $n$ strategic bidders, whose valuations are drawn
independently from high-dimensional, unknown prior distributions. It is
well-known that optimal and even approximately-optimal mechanisms for this
setting are notoriously difficult to characterize or compute, and, even when
they can be found, are often rife with various counter-intuitive properties. In
this paper, following a model introduced recently by Cai and
Daskalakis~\cite{cai2022recommender}, we consider the case that bidders' prior
distributions can be well-approximated by a topic model. We design an active
learning component, responsible for interacting with the bidders and outputting
low-dimensional approximations of their types, and a mechanism design
component, responsible for robustifying mechanisms for the low-dimensional
model to work for the approximate types of the former component. On the active
learning front, we cast our problem in the framework of Randomized Linear
Algebra (RLA) for regression problems, allowing us to import several
breakthrough results from that line of research, and adapt them to our setting.
On the mechanism design front, we remove many restrictive assumptions of prior
work on the type of access needed to the underlying distributions and the
associated mechanisms. To the best of our knowledge, our work is the first to
formulate connections between mechanism design, and RLA for active learning of
regression problems, opening the door for further applications of randomized
linear algebra primitives to mechanism design. | [
"Christos Boutsikas",
"Petros Drineas",
"Marios Mertzanidis",
"Alexandros Psomas",
"Paritosh Verma"
] | 2023-10-11 20:34:17 | http://arxiv.org/abs/2310.07874v1 | http://arxiv.org/pdf/2310.07874v1 | 2310.07874v1 |
QArchSearch: A Scalable Quantum Architecture Search Package | The current era of quantum computing has yielded several algorithms that
promise high computational efficiency. While the algorithms are sound in theory
and can provide potentially exponential speedup, there is little guidance on
how to design proper quantum circuits to realize the appropriate unitary
transformation to be applied to the input quantum state. In this paper, we
present \texttt{QArchSearch}, an AI based quantum architecture search package
with the \texttt{QTensor} library as a backend that provides a principled and
automated approach to finding the best model given a task and input quantum
state. We show that the search package is able to efficiently scale the search
to large quantum circuits and enables the exploration of more complex models
for different quantum applications. \texttt{QArchSearch} runs at scale and high
efficiency on high-performance computing systems using a two-level
parallelization scheme on both CPUs and GPUs, which has been demonstrated on
the Polaris supercomputer. | [
"Ankit Kulshrestha",
"Danylo Lykov",
"Ilya Safro",
"Yuri Alexeev"
] | 2023-10-11 20:00:33 | http://arxiv.org/abs/2310.07858v1 | http://arxiv.org/pdf/2310.07858v1 | 2310.07858v1 |
CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping | Leveraging nearest neighbor retrieval for self-supervised representation
learning has proven beneficial with object-centric images. However, this
approach faces limitations when applied to scene-centric datasets, where
multiple objects within an image are only implicitly captured in the global
representation. Such global bootstrapping can lead to undesirable entanglement
of object representations. Furthermore, even object-centric datasets stand to
benefit from a finer-grained bootstrapping approach. In response to these
challenges, we introduce a novel Cross-Image Object-Level Bootstrapping method
tailored to enhance dense visual representation learning. By employing
object-level nearest neighbor bootstrapping throughout the training, CrIBo
emerges as a notably strong and adequate candidate for in-context learning,
leveraging nearest neighbor retrieval at test time. CrIBo shows
state-of-the-art performance on the latter task while being highly competitive
in more standard downstream segmentation tasks. Our code and pretrained models
will be publicly available upon acceptance. | [
"Tim Lebailly",
"Thomas Stegmüller",
"Behzad Bozorgtabar",
"Jean-Philippe Thiran",
"Tinne Tuytelaars"
] | 2023-10-11 19:57:51 | http://arxiv.org/abs/2310.07855v1 | http://arxiv.org/pdf/2310.07855v1 | 2310.07855v1 |
On the Computational Complexity of Private High-dimensional Model Selection via the Exponential Mechanism | We consider the problem of model selection in a high-dimensional sparse
linear regression model under the differential privacy framework. In
particular, we consider the problem of differentially private best subset
selection and study its utility guarantee. We adopt the well-known exponential
mechanism for selecting the best model, and under a certain margin condition,
we establish its strong model recovery property. However, the exponential
search space of the exponential mechanism poses a serious computational
bottleneck. To overcome this challenge, we propose a Metropolis-Hastings
algorithm for the sampling step and establish its polynomial mixing time to its
stationary distribution in the problem parameters $n,p$, and $s$. Furthermore,
we also establish approximate differential privacy for the final estimates of
the Metropolis-Hastings random walk using its mixing property. Finally, we also
perform some illustrative simulations that echo the theoretical findings of our
main results. | [
"Saptarshi Roy",
"Ambuj Tewari"
] | 2023-10-11 19:53:15 | http://arxiv.org/abs/2310.07852v1 | http://arxiv.org/pdf/2310.07852v1 | 2310.07852v1 |
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains | We characterize the statistical efficiency of knowledge transfer through $n$
samples from a teacher to a probabilistic student classifier with input space
$\mathcal S$ over labels $\mathcal A$. We show that privileged information at
three progressive levels accelerates the transfer. At the first level, only
samples with hard labels are known, via which the maximum likelihood estimator
attains the minimax rate $\sqrt{{|{\mathcal S}||{\mathcal A}|}/{n}}$. The
second level has the teacher probabilities of sampled labels available in
addition, which turns out to boost the convergence rate lower bound to
${{|{\mathcal S}||{\mathcal A}|}/{n}}$. However, under this second data
acquisition protocol, minimizing a naive adaptation of the cross-entropy loss
results in an asymptotically biased student. We overcome this limitation and
achieve the fundamental limit by using a novel empirical variant of the squared
error logit loss. The third level further equips the student with the soft
labels (complete logits) on ${\mathcal A}$ given every sampled input, thereby
provably enables the student to enjoy a rate ${|{\mathcal S}|}/{n}$ free of
$|{\mathcal A}|$. We find any Kullback-Leibler divergence minimizer to be
optimal in the last case. Numerical simulations distinguish the four learners
and corroborate our theory. | [
"Qingyue Zhao",
"Banghua Zhu"
] | 2023-10-11 19:30:08 | http://arxiv.org/abs/2310.07838v2 | http://arxiv.org/pdf/2310.07838v2 | 2310.07838v2 |
Measuring Feature Sparsity in Language Models | Recent works have proposed that activations in language models can be
modelled as sparse linear combinations of vectors corresponding to features of
input text. Under this assumption, these works aimed to reconstruct feature
directions using sparse coding. We develop metrics to assess the success of
these sparse coding techniques and test the validity of the linearity and
sparsity assumptions. We show our metrics can predict the level of sparsity on
synthetic sparse linear activations, and can distinguish between sparse linear
data and several other distributions. We use our metrics to measure levels of
sparsity in several language models. We find evidence that language model
activations can be accurately modelled by sparse linear combinations of
features, significantly more so than control datasets. We also show that model
activations appear to be sparsest in the first and final layers. | [
"Mingyang Deng",
"Lucas Tao",
"Joe Benton"
] | 2023-10-11 19:26:52 | http://arxiv.org/abs/2310.07837v2 | http://arxiv.org/pdf/2310.07837v2 | 2310.07837v2 |
When, Why and How Much? Adaptive Learning Rate Scheduling by Refinement | Learning rate schedules used in practice bear little resemblance to those
recommended by theory. We close much of this theory/practice gap, and as a
consequence are able to derive new problem-adaptive learning rate schedules.
Our key technical contribution is a refined analysis of learning rate schedules
for a wide class of optimization algorithms (including SGD). In contrast to
most prior works that study the convergence of the average iterate, we study
the last iterate, which is what most people use in practice. When considering
only worst-case analysis, our theory predicts that the best choice is the
linear decay schedule: a popular choice in practice that sets the stepsize
proportionally to $1 - t/T$, where $t$ is the current iteration and $T$ is the
total number of steps. To go beyond this worst-case analysis, we use the
observed gradient norms to derive schedules refined for any particular task.
These refined schedules exhibit learning rate warm-up and rapid learning rate
annealing near the end of training. Ours is the first systematic approach to
automatically yield both of these properties. We perform the most comprehensive
evaluation of learning rate schedules to date, evaluating across 10 diverse
deep learning problems, a series of LLMs, and a suite of logistic regression
problems. We validate that overall, the linear-decay schedule matches or
outperforms all commonly used default schedules including cosine annealing, and
that our schedule refinement method gives further improvements. | [
"Aaron Defazio",
"Ashok Cutkosky",
"Harsh Mehta",
"Konstantin Mishchenko"
] | 2023-10-11 19:16:35 | http://arxiv.org/abs/2310.07831v1 | http://arxiv.org/pdf/2310.07831v1 | 2310.07831v1 |
Does Synthetic Data Make Large Language Models More Efficient? | Natural Language Processing (NLP) has undergone transformative changes with
the advent of deep learning methodologies. One challenge persistently
confronting researchers is the scarcity of high-quality, annotated datasets
that drive these models. This paper explores the nuances of synthetic data
generation in NLP, with a focal point on template-based question generation. By
assessing its advantages, including data augmentation potential and the
introduction of structured variety, we juxtapose these benefits against
inherent limitations, such as the risk of overfitting and the constraints posed
by pre-defined templates. Drawing from empirical evaluations, we demonstrate
the impact of template-based synthetic data on the performance of modern
transformer models. We conclude by emphasizing the delicate balance required
between synthetic and real-world data, and the future trajectories of
integrating synthetic data in model training pipelines. The findings aim to
guide NLP practitioners in harnessing synthetic data's potential, ensuring
optimal model performance in diverse applications. | [
"Sia Gholami",
"Marwan Omar"
] | 2023-10-11 19:16:09 | http://arxiv.org/abs/2310.07830v1 | http://arxiv.org/pdf/2310.07830v1 | 2310.07830v1 |
Large Language Models Are Zero-Shot Time Series Forecasters | By encoding time series as a string of numerical digits, we can frame time
series forecasting as next-token prediction in text. Developing this approach,
we find that large language models (LLMs) such as GPT-3 and LLaMA-2 can
surprisingly zero-shot extrapolate time series at a level comparable to or
exceeding the performance of purpose-built time series models trained on the
downstream tasks. To facilitate this performance, we propose procedures for
effectively tokenizing time series data and converting discrete distributions
over tokens into highly flexible densities over continuous values. We argue the
success of LLMs for time series stems from their ability to naturally represent
multimodal distributions, in conjunction with biases for simplicity, and
repetition, which align with the salient features in many time series, such as
repeated seasonal trends. We also show how LLMs can naturally handle missing
data without imputation through non-numerical text, accommodate textual side
information, and answer questions to help explain predictions. While we find
that increasing model size generally improves performance on time series, we
show GPT-4 can perform worse than GPT-3 because of how it tokenizes numbers,
and poor uncertainty calibration, which is likely the result of alignment
interventions such as RLHF. | [
"Nate Gruver",
"Marc Finzi",
"Shikai Qiu",
"Andrew Gordon Wilson"
] | 2023-10-11 19:01:28 | http://arxiv.org/abs/2310.07820v1 | http://arxiv.org/pdf/2310.07820v1 | 2310.07820v1 |
Faithfulness Measurable Masked Language Models | A common approach to explain NLP models, is to use importance measures that
express which tokens are important for a prediction. Unfortunately, such
explanations are often wrong despite being persuasive. Therefore, it is
essential to measure their faithfulness. One such metric is if tokens are truly
important, then masking them should result in worse model performance. However,
token masking introduces out-of-distribution issues and existing solutions are
computationally expensive and employ proxy-models. Furthermore, other metrics
are very limited in scope. In this work, we propose an inherently faithfulness
measurable model that addresses these challenges. This is achieved by using a
novel fine-tuning method that incorporates masking, such that masking tokens
become in-distribution by design. This differs from existing approaches, which
are completely model-agnostic but are inapplicable in practice. We demonstrate
the generality of our approach by applying it to various tasks and validate it
using statistical in-distribution tests. Additionally, because masking is
in-distribution, importance measures which themselves use masking become more
faithful, thus our model becomes more explainable. | [
"Andreas Madsen",
"Siva Reddy",
"Sarath Chandar"
] | 2023-10-11 19:00:40 | http://arxiv.org/abs/2310.07819v1 | http://arxiv.org/pdf/2310.07819v1 | 2310.07819v1 |
Language Models As Semantic Indexers | Semantic identifier (ID) is an important concept in information retrieval
that aims to preserve the semantics of objects such as documents and items
inside their IDs. Previous studies typically adopt a two-stage pipeline to
learn semantic IDs by first procuring embeddings using off-the-shelf text
encoders and then deriving IDs based on the embeddings. However, each step
introduces potential information loss and there is usually an inherent mismatch
between the distribution of embeddings within the latent space produced by text
encoders and the anticipated distribution required for semantic indexing.
Nevertheless, it is non-trivial to design a method that can learn the
document's semantic representations and its hierarchical structure
simultaneously, given that semantic IDs are discrete and sequentially
structured, and the semantic supervision is deficient. In this paper, we
introduce LMINDEXER, a self-supervised framework to learn semantic IDs with a
generative language model. We tackle the challenge of sequential discrete ID by
introducing a semantic indexer capable of generating neural sequential discrete
representations with progressive training and contrastive learning. In response
to the semantic supervision deficiency, we propose to train the model with a
self-supervised document reconstruction objective. The learned semantic indexer
can facilitate various downstream tasks, such as recommendation and retrieval.
We conduct experiments on three tasks including recommendation, product search,
and document retrieval on five datasets from various domains, where LMINDEXER
outperforms competitive baselines significantly and consistently. | [
"Bowen Jin",
"Hansi Zeng",
"Guoyin Wang",
"Xiusi Chen",
"Tianxin Wei",
"Ruirui Li",
"Zhengyang Wang",
"Zheng Li",
"Yang Li",
"Hanqing Lu",
"Suhang Wang",
"Jiawei Han",
"Xianfeng Tang"
] | 2023-10-11 18:56:15 | http://arxiv.org/abs/2310.07815v1 | http://arxiv.org/pdf/2310.07815v1 | 2310.07815v1 |
Explorable Mesh Deformation Subspaces from Unstructured Generative Models | Exploring variations of 3D shapes is a time-consuming process in traditional
3D modeling tools. Deep generative models of 3D shapes often feature continuous
latent spaces that can, in principle, be used to explore potential variations
starting from a set of input shapes. In practice, doing so can be problematic:
latent spaces are high dimensional and hard to visualize, contain shapes that
are not relevant to the input shapes, and linear paths through them often lead
to sub-optimal shape transitions. Furthermore, one would ideally be able to
explore variations in the original high-quality meshes used to train the
generative model, not its lower-quality output geometry. In this paper, we
present a method to explore variations among a given set of landmark shapes by
constructing a mapping from an easily-navigable 2D exploration space to a
subspace of a pre-trained generative model. We first describe how to find a
mapping that spans the set of input landmark shapes and exhibits smooth
variations between them. We then show how to turn the variations in this
subspace into deformation fields, to transfer those variations to high-quality
meshes for the landmark shapes. Our results show that our method can produce
visually-pleasing and easily-navigable 2D exploration spaces for several
different shape categories, especially as compared to prior work on learning
deformation spaces for 3D shapes. | [
"Arman Maesumi",
"Paul Guerrero",
"Vladimir G. Kim",
"Matthew Fisher",
"Siddhartha Chaudhuri",
"Noam Aigerman",
"Daniel Ritchie"
] | 2023-10-11 18:53:57 | http://arxiv.org/abs/2310.07814v1 | http://arxiv.org/pdf/2310.07814v1 | 2310.07814v1 |
Online RL in Linearly $q^π$-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore | We consider online reinforcement learning (RL) in episodic Markov decision
processes (MDPs) under the linear $q^\pi$-realizability assumption, where it is
assumed that the action-values of all policies can be expressed as linear
functions of state-action features. This class is known to be more general than
linear MDPs, where the transition kernel and the reward function are assumed to
be linear functions of the feature vectors. As our first contribution, we show
that the difference between the two classes is the presence of states in
linearly $q^\pi$-realizable MDPs where for any policy, all the actions have
approximately equal values, and skipping over these states by following an
arbitrarily fixed policy in those states transforms the problem to a linear
MDP. Based on this observation, we derive a novel (computationally inefficient)
learning algorithm for linearly $q^\pi$-realizable MDPs that simultaneously
learns what states should be skipped over and runs another learning algorithm
on the linear MDP hidden in the problem. The method returns an
$\epsilon$-optimal policy after $\text{polylog}(H, d)/\epsilon^2$ interactions
with the MDP, where $H$ is the time horizon and $d$ is the dimension of the
feature vectors, giving the first polynomial-sample-complexity online RL
algorithm for this setting. The results are proved for the misspecified case,
where the sample complexity is shown to degrade gracefully with the
misspecification error. | [
"Gellért Weisz",
"András György",
"Csaba Szepesvári"
] | 2023-10-11 18:50:25 | http://arxiv.org/abs/2310.07811v1 | http://arxiv.org/pdf/2310.07811v1 | 2310.07811v1 |
FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learning | Federated learning (FL) is a decentralized machine learning approach where
independent learners process data privately. Its goal is to create a robust and
accurate model by aggregating and retraining local models over multiple rounds.
However, FL faces challenges regarding data heterogeneity and model aggregation
effectiveness. In order to simulate real-world data, researchers use methods
for data partitioning that transform a dataset designated for centralized
learning into a group of sub-datasets suitable for distributed machine learning
with different data heterogeneity. In this paper, we study the currently
popular data partitioning techniques and visualize their main disadvantages:
the lack of precision in the data diversity, which leads to unreliable
heterogeneity indexes, and the inability to incrementally challenge the FL
algorithms. To resolve this problem, we propose a method that leverages entropy
and symmetry to construct 'the most challenging' and controllable data
distributions with gradual difficulty. We introduce a metric to measure data
heterogeneity among the learning agents and a transformation technique that
divides any dataset into splits with precise data diversity. Through a
comparative study, we demonstrate the superiority of our method over existing
FL data partitioning approaches, showcasing its potential to challenge model
aggregation algorithms. Experimental results indicate that our approach
gradually challenges the FL strategies, and the models trained on FedSym
distributions are more distinct. | [
"Ensiye Kiyamousavi",
"Boris Kraychev",
"Ivan Koychev"
] | 2023-10-11 18:39:08 | http://arxiv.org/abs/2310.07807v1 | http://arxiv.org/pdf/2310.07807v1 | 2310.07807v1 |
Generative Modeling with Phase Stochastic Bridges | Diffusion models (DMs) represent state-of-the-art generative models for
continuous inputs. DMs work by constructing a Stochastic Differential Equation
(SDE) in the input space (ie, position space), and using a neural network to
reverse it. In this work, we introduce a novel generative modeling framework
grounded in \textbf{phase space dynamics}, where a phase space is defined as
{an augmented space encompassing both position and velocity.} Leveraging
insights from Stochastic Optimal Control, we construct a path measure in the
phase space that enables efficient sampling. {In contrast to DMs, our framework
demonstrates the capability to generate realistic data points at an early stage
of dynamics propagation.} This early prediction sets the stage for efficient
data generation by leveraging additional velocity information along the
trajectory. On standard image generation benchmarks, our model yields favorable
performance over baselines in the regime of small Number of Function
Evaluations (NFEs). Furthermore, our approach rivals the performance of
diffusion models equipped with efficient sampling techniques, underscoring its
potential as a new tool generative modeling. | [
"Tianrong Chen",
"Jiatao Gu",
"Laurent Dinh",
"Evangelos A. Theodorou",
"Josh Susskind",
"Shuangfei Zhai"
] | 2023-10-11 18:38:28 | http://arxiv.org/abs/2310.07805v2 | http://arxiv.org/pdf/2310.07805v2 | 2310.07805v2 |
Explainable Attention for Few-shot Learning and Beyond | Attention mechanisms have exhibited promising potential in enhancing learning
models by identifying salient portions of input data. This is particularly
valuable in scenarios where limited training samples are accessible due to
challenges in data collection and labeling. Drawing inspiration from human
recognition processes, we posit that an AI baseline's performance could be more
accurate and dependable if it is exposed to essential segments of raw data
rather than the entire input dataset, akin to human perception. However, the
task of selecting these informative data segments, referred to as hard
attention finding, presents a formidable challenge. In situations with few
training samples, existing studies struggle to locate such informative regions
due to the large number of training parameters that cannot be effectively
learned from the available limited samples. In this study, we introduce a novel
and practical framework for achieving explainable hard attention finding,
specifically tailored for few-shot learning scenarios, called FewXAT. Our
approach employs deep reinforcement learning to implement the concept of hard
attention, directly impacting raw input data and thus rendering the process
interpretable for human understanding. Through extensive experimentation across
various benchmark datasets, we demonstrate the efficacy of our proposed method. | [
"Bahareh Nikpour",
"Narges Armanfard"
] | 2023-10-11 18:33:17 | http://arxiv.org/abs/2310.07800v1 | http://arxiv.org/pdf/2310.07800v1 | 2310.07800v1 |
A Transfer-Learning-Based Prognosis Prediction Paradigm that Bridges Data Distribution Shift across EMR Datasets | Due to the limited information about emerging diseases, symptoms are hard to
be noticed and recognized, so that the window for clinical intervention could
be ignored. An effective prognostic model is expected to assist doctors in
making right diagnosis and designing personalized treatment plan, so to
promptly prevent unfavorable outcomes. However, in the early stage of a
disease, limited data collection and clinical experiences, plus the concern out
of privacy and ethics, may result in restricted data availability for
reference, to the extent that even data labels are difficult to mark correctly.
In addition, Electronic Medical Record (EMR) data of different diseases or of
different sources of the same disease can prove to be having serious
cross-dataset feature misalignment problems, greatly mutilating the efficiency
of deep learning models. This article introduces a transfer learning method to
build a transition model from source dataset to target dataset. By way of
constraining the distribution shift of features generated in disparate domains,
domain-invariant features that are exclusively relative to downstream tasks are
captured, so to cultivate a unified domain-invariant encoder across various
task domains to achieve better feature representation. Experimental results of
several target tasks demonstrate that our proposed model outperforms competing
baseline methods and has higher rate of training convergence, especially in
dealing with limited data amount. A multitude of experiences have proven the
efficacy of our method to provide more accurate predictions concerning newly
emergent pandemics and other diseases. | [
"Zhongji Zhang",
"Yuhang Wang",
"Yinghao Zhu",
"Xinyu Ma",
"Tianlong Wang",
"Chaohe Zhang",
"Yasha Wang",
"Liantao Ma"
] | 2023-10-11 18:32:21 | http://arxiv.org/abs/2310.07799v1 | http://arxiv.org/pdf/2310.07799v1 | 2310.07799v1 |
CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving | Benchmarking is a common method for evaluating trajectory prediction models
for autonomous driving. Existing benchmarks rely on datasets, which are biased
towards more common scenarios, such as cruising, and distance-based metrics
that are computed by averaging over all scenarios. Following such a regiment
provides a little insight into the properties of the models both in terms of
how well they can handle different scenarios and how admissible and diverse
their outputs are. There exist a number of complementary metrics designed to
measure the admissibility and diversity of trajectories, however, they suffer
from biases, such as length of trajectories.
In this paper, we propose a new benChmarking paRadIgm for evaluaTing
trajEctoRy predIction Approaches (CRITERIA). Particularly, we propose 1) a
method for extracting driving scenarios at varying levels of specificity
according to the structure of the roads, models' performance, and data
properties for fine-grained ranking of prediction models; 2) A set of new
bias-free metrics for measuring diversity, by incorporating the characteristics
of a given scenario, and admissibility, by considering the structure of roads
and kinematic compliancy, motivated by real-world driving constraints. 3) Using
the proposed benchmark, we conduct extensive experimentation on a
representative set of the prediction models using the large scale Argoverse
dataset. We show that the proposed benchmark can produce a more accurate
ranking of the models and serve as a means of characterizing their behavior. We
further present ablation studies to highlight contributions of different
elements that are used to compute the proposed metrics. | [
"Changhe Chen",
"Mozhgan Pourkeshavarz",
"Amir Rasouli"
] | 2023-10-11 18:28:15 | http://arxiv.org/abs/2310.07794v1 | http://arxiv.org/pdf/2310.07794v1 | 2310.07794v1 |
GenTKG: Generative Forecasting on Temporal Knowledge Graph | The rapid advancements in large language models (LLMs) have ignited interest
in the temporal knowledge graph (tKG) domain, where conventional carefully
designed embedding-based and rule-based models dominate. The question remains
open of whether pre-trained LLMs can understand structured temporal relational
data and replace them as the foundation model for temporal relational
forecasting. Therefore, we bring temporal knowledge forecasting into the
generative setting. However, challenges occur in the huge chasms between
complex temporal graph data structure and sequential natural expressions LLMs
can handle, and between the enormous data sizes of tKGs and heavy computation
costs of finetuning LLMs. To address these challenges, we propose a novel
retrieval augmented generation framework that performs generative forecasting
on tKGs named GenTKG, which combines a temporal logical rule-based retrieval
strategy and lightweight parameter-efficient instruction tuning. Extensive
experiments have shown that GenTKG outperforms conventional methods of temporal
relational forecasting under low computation resources. GenTKG also highlights
remarkable transferability with exceeding performance on unseen datasets
without re-training. Our work reveals the huge potential of LLMs in the tKG
domain and opens a new frontier for generative forecasting on tKGs. | [
"Ruotong Liao",
"Xu Jia",
"Yunpu Ma",
"Volker Tresp"
] | 2023-10-11 18:27:12 | http://arxiv.org/abs/2310.07793v1 | http://arxiv.org/pdf/2310.07793v1 | 2310.07793v1 |
Using Spark Machine Learning Models to Perform Predictive Analysis on Flight Ticket Pricing Data | This paper discusses predictive performance and processes undertaken on
flight pricing data utilizing r2(r-square) and RMSE that leverages a large
dataset, originally from Expedia.com, consisting of approximately 20 million
records or 4.68 gigabytes. The project aims to determine the best models usable
in the real world to predict airline ticket fares for non-stop flights across
the US. Therefore, good generalization capability and optimized processing
times are important measures for the model.
We will discover key business insights utilizing feature importance and
discuss the process and tools used for our analysis. Four regression machine
learning algorithms were utilized: Random Forest, Gradient Boost Tree, Decision
Tree, and Factorization Machines utilizing Cross Validator and Training
Validator functions for assessing performance and generalization capability. | [
"Philip Wong",
"Phue Thant",
"Pratiksha Yadav",
"Ruta Antaliya",
"Jongwook Woo"
] | 2023-10-11 18:20:17 | http://arxiv.org/abs/2310.07787v1 | http://arxiv.org/pdf/2310.07787v1 | 2310.07787v1 |
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling | Real-world applications of contextual bandits often exhibit non-stationarity
due to seasonality, serendipity, and evolving social trends. While a number of
non-stationary contextual bandit learning algorithms have been proposed in the
literature, they excessively explore due to a lack of prioritization for
information of enduring value, or are designed in ways that do not scale in
modern applications with high-dimensional user-specific features and large
action set, or both. In this paper, we introduce a novel non-stationary
contextual bandit algorithm that addresses these concerns. It combines a
scalable, deep-neural-network-based architecture with a carefully designed
exploration mechanism that strategically prioritizes collecting information
with the most lasting value in a non-stationary environment. Through empirical
evaluations on two real-world recommendation datasets, which exhibit pronounced
non-stationarity, we demonstrate that our approach significantly outperforms
the state-of-the-art baselines. | [
"Zheqing Zhu",
"Yueyang Liu",
"Xu Kuang",
"Benjamin Van Roy"
] | 2023-10-11 18:15:55 | http://arxiv.org/abs/2310.07786v2 | http://arxiv.org/pdf/2310.07786v2 | 2310.07786v2 |
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches | Randomized smoothing has recently attracted attentions in the field of
adversarial robustness to provide provable robustness guarantees on smoothed
neural network classifiers. However, existing works show that vanilla
randomized smoothing usually does not provide good robustness performance and
often requires (re)training techniques on the base classifier in order to boost
the robustness of the resulting smoothed classifier. In this work, we propose
two cost-effective approaches to boost the robustness of randomized smoothing
while preserving its clean performance. The first approach introduces a new
robust training method AdvMacerwhich combines adversarial training and
robustness certification maximization for randomized smoothing. We show that
AdvMacer can improve the robustness performance of randomized smoothing
classifiers compared to SOTA baselines, while being 3x faster to train than
MACER baseline. The second approach introduces a post-processing method EsbRS
which greatly improves the robustness certificate based on building model
ensembles. We explore different aspects of model ensembles that has not been
studied by prior works and propose a novel design methodology to further
improve robustness of the ensemble based on our theoretical analysis. | [
"Linbo Liu",
"Trong Nghia Hoang",
"Lam M. Nguyen",
"Tsui-Wei Weng"
] | 2023-10-11 18:06:05 | http://arxiv.org/abs/2310.07780v1 | http://arxiv.org/pdf/2310.07780v1 | 2310.07780v1 |
Feature Learning and Generalization in Deep Networks with Orthogonal Weights | Fully-connected deep neural networks with weights initialized from
independent Gaussian distributions can be tuned to criticality, which prevents
the exponential growth or decay of signals propagating through the network.
However, such networks still exhibit fluctuations that grow linearly with the
depth of the network, which may impair the training of networks with width
comparable to depth. We show analytically that rectangular networks with tanh
activations and weights initialized from the ensemble of orthogonal matrices
have corresponding preactivation fluctuations which are independent of depth,
to leading order in inverse width. Moreover, we demonstrate numerically that,
at initialization, all correlators involving the neural tangent kernel (NTK)
and its descendants at leading order in inverse width -- which govern the
evolution of observables during training -- saturate at a depth of $\sim 20$,
rather than growing without bound as in the case of Gaussian initializations.
We speculate that this structure preserves finite-width feature learning while
reducing overall noise, thus improving both generalization and training speed.
We provide some experimental justification by relating empirical measurements
of the NTK to the superior performance of deep nonlinear orthogonal networks
trained under full-batch gradient descent on the MNIST and CIFAR-10
classification tasks. | [
"Hannah Day",
"Yonatan Kahn",
"Daniel A. Roberts"
] | 2023-10-11 18:00:02 | http://arxiv.org/abs/2310.07765v1 | http://arxiv.org/pdf/2310.07765v1 | 2310.07765v1 |
Self-supervised Representation Learning From Random Data Projectors | Self-supervised representation learning~(SSRL) has advanced considerably by
exploiting the transformation invariance assumption under artificially designed
data augmentations. While augmentation-based SSRL algorithms push the
boundaries of performance in computer vision and natural language processing,
they are often not directly applicable to other data modalities, and can
conflict with application-specific data augmentation constraints. This paper
presents an SSRL approach that can be applied to any data modality and network
architecture because it does not rely on augmentations or masking.
Specifically, we show that high-quality data representations can be learned by
reconstructing random data projections. We evaluate the proposed approach on a
wide range of representation learning tasks that span diverse modalities and
real-world applications. We show that it outperforms multiple state-of-the-art
SSRL baselines. Due to its wide applicability and strong empirical results, we
argue that learning from randomness is a fruitful research direction worthy of
attention and further study. | [
"Yi Sui",
"Tongzi Wu",
"Jesse C. Cresswell",
"Ga Wu",
"George Stein",
"Xiao Shi Huang",
"Xiaochen Zhang",
"Maksims Volkovs"
] | 2023-10-11 18:00:01 | http://arxiv.org/abs/2310.07756v1 | http://arxiv.org/pdf/2310.07756v1 | 2310.07756v1 |
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining | Pretraining auto-regressive large language models (LLMs) with retrieval
demonstrates better perplexity and factual accuracy by leveraging external
databases. However, the size of existing pretrained retrieval-augmented LLM is
still limited (e.g., Retro has 7.5B parameters), which limits the effectiveness
of instruction tuning and zero-shot generalization. In this work, we introduce
Retro 48B, the largest LLM pretrained with retrieval before instruction tuning.
Specifically, we continue to pretrain the 43B GPT model on additional 100
billion tokens using the Retro augmentation method by retrieving from 1.2
trillion tokens. The obtained foundation model, Retro 48B, largely outperforms
the original 43B GPT in terms of perplexity. After instruction tuning on Retro,
InstructRetro demonstrates significant improvement over the instruction tuned
GPT on zero-shot question answering (QA) tasks. Specifically, the average
improvement of InstructRetro is 7% over its GPT counterpart across 8 short-form
QA tasks, and 10% over GPT across 4 challenging long-form QA tasks.
Surprisingly, we find that one can ablate the encoder from InstructRetro
architecture and directly use its decoder backbone, while achieving comparable
results. We hypothesize that pretraining with retrieval makes its decoder good
at incorporating context for QA. Our results highlights the promising direction
to obtain a better GPT decoder for QA through continued pretraining with
retrieval before instruction tuning. | [
"Boxin Wang",
"Wei Ping",
"Lawrence McAfee",
"Peng Xu",
"Bo Li",
"Mohammad Shoeybi",
"Bryan Catanzaro"
] | 2023-10-11 17:59:05 | http://arxiv.org/abs/2310.07713v1 | http://arxiv.org/pdf/2310.07713v1 | 2310.07713v1 |
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models | Large language models (LLMs) exhibit positional bias in how they use context,
which especially complicates listwise ranking. To address this, we propose
permutation self-consistency, a form of self-consistency over ranking list
outputs of black-box LLMs. Our key idea is to marginalize out different list
orders in the prompt to produce an order-independent ranking with less
positional bias. First, given some input prompt, we repeatedly shuffle the list
in the prompt and pass it through the LLM while holding the instructions the
same. Next, we aggregate the resulting sample of rankings by computing the
central ranking closest in distance to all of them, marginalizing out prompt
order biases in the process. Theoretically, we prove the robustness of our
method, showing convergence to the true ranking in the presence of random
perturbations. Empirically, on five list-ranking datasets in sorting and
passage reranking, our approach improves scores from conventional inference by
up to 7-18% for GPT-3.5 and 8-16% for LLaMA v2 (70B), surpassing the previous
state of the art in passage reranking. Our code is at
https://github.com/castorini/perm-sc. | [
"Raphael Tang",
"Xinyu Zhang",
"Xueguang Ma",
"Jimmy Lin",
"Ferhan Ture"
] | 2023-10-11 17:59:02 | http://arxiv.org/abs/2310.07712v1 | http://arxiv.org/pdf/2310.07712v1 | 2310.07712v1 |
Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks | Recurrent neural networks (RNNs) trained on compositional tasks can exhibit
functional modularity, in which neurons can be clustered by activity similarity
and participation in shared computational subtasks. Unlike brains, these RNNs
do not exhibit anatomical modularity, in which functional clustering is
correlated with strong recurrent coupling and spatial localization of
functional clusters. Contrasting with functional modularity, which can be
ephemerally dependent on the input, anatomically modular networks form a robust
substrate for solving the same subtasks in the future. To examine whether it is
possible to grow brain-like anatomical modularity, we apply a recent machine
learning method, brain-inspired modular training (BIMT), to a network being
trained to solve a set of compositional cognitive tasks. We find that
functional and anatomical clustering emerge together, such that functionally
similar neurons also become spatially localized and interconnected. Moreover,
compared to standard $L_1$ or no regularization settings, the model exhibits
superior performance by optimally balancing task performance and network
sparsity. In addition to achieving brain-like organization in RNNs, our
findings also suggest that BIMT holds promise for applications in neuromorphic
computing and enhancing the interpretability of neural network architectures. | [
"Ziming Liu",
"Mikail Khona",
"Ila R. Fiete",
"Max Tegmark"
] | 2023-10-11 17:58:25 | http://arxiv.org/abs/2310.07711v1 | http://arxiv.org/pdf/2310.07711v1 | 2310.07711v1 |
DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language Models | Watermarking techniques offer a promising way to secure data via embedding
covert information into the data. A paramount challenge in the domain lies in
preserving the distribution of original data during watermarking. Our research
extends and refines existing watermarking framework, placing emphasis on the
importance of a distribution-preserving (DiP) watermark. Contrary to the
current strategies, our proposed DiPmark preserves the original token
distribution during watermarking (stealthy), is detectable without access to
the language model API or weights (efficient), and is robust to moderate
changes of tokens (resilient). This is achieved by incorporating a novel
reweight strategy, combined with a hash function that assigns unique
\textit{i.i.d.} ciphers based on the context. The empirical benchmarks of our
approach underscore its stealthiness, efficiency, and resilience, making it a
robust solution for watermarking tasks that demand impeccable quality
preservation. | [
"Yihan Wu",
"Zhengmian Hu",
"Hongyang Zhang",
"Heng Huang"
] | 2023-10-11 17:57:35 | http://arxiv.org/abs/2310.07710v1 | http://arxiv.org/pdf/2310.07710v1 | 2310.07710v1 |
MatFormer: Nested Transformer for Elastic Inference | Transformer models are deployed in a wide range of settings, from
multi-accelerator clusters to standalone mobile phones. The diverse inference
constraints in these scenarios necessitate practitioners to train foundation
models such as PaLM 2, Llama, & ViTs as a series of models of varying sizes.
Due to significant training costs, only a select few model sizes are trained
and supported, limiting more fine-grained control over relevant tradeoffs,
including latency, cost, and accuracy. This work introduces MatFormer, a nested
Transformer architecture designed to offer elasticity in a variety of
deployment constraints. Each Feed Forward Network (FFN) block of a MatFormer
model is jointly optimized with a few nested smaller FFN blocks. This training
procedure allows for the Mix'n'Match of model granularities across layers --
i.e., a trained universal MatFormer model enables extraction of hundreds of
accurate smaller models, which were never explicitly optimized. We empirically
demonstrate MatFormer's effectiveness across different model classes (decoders
& encoders), modalities (language & vision), and scales (up to 2.6B
parameters). We find that a 2.6B decoder-only MatFormer language model (MatLM)
allows us to extract smaller models spanning from 1.5B to 2.6B, each exhibiting
comparable validation loss and one-shot downstream evaluations to their
independently trained counterparts. Furthermore, we observe that smaller
encoders extracted from a universal MatFormer-based ViT (MatViT) encoder
preserve the metric-space structure for adaptive large-scale retrieval.
Finally, we showcase that speculative decoding with the accurate and consistent
submodels extracted from MatFormer can further reduce inference latency. | [
"Devvrit",
"Sneha Kudugunta",
"Aditya Kusupati",
"Tim Dettmers",
"Kaifeng Chen",
"Inderjit Dhillon",
"Yulia Tsvetkov",
"Hannaneh Hajishirzi",
"Sham Kakade",
"Ali Farhadi",
"Prateek Jain"
] | 2023-10-11 17:57:14 | http://arxiv.org/abs/2310.07707v1 | http://arxiv.org/pdf/2310.07707v1 | 2310.07707v1 |
From Scarcity to Efficiency: Improving CLIP Training via Visual-enriched Captions | Web-crawled datasets are pivotal to the success of pre-training
vision-language models, exemplified by CLIP. However, web-crawled AltTexts can
be noisy and potentially irrelevant to images, thereby undermining the crucial
image-text alignment. Existing methods for rewriting captions using large
language models (LLMs) have shown promise on small, curated datasets like CC3M
and CC12M. Nevertheless, their efficacy on massive web-captured captions is
constrained by the inherent noise and randomness in such data. In this study,
we address this limitation by focusing on two key aspects: data quality and
data variety. Unlike recent LLM rewriting techniques, we emphasize exploiting
visual concepts and their integration into the captions to improve data
quality. For data variety, we propose a novel mixed training scheme that
optimally leverages AltTexts alongside newly generated Visual-enriched Captions
(VeC). We use CLIP as one example and adapt the method for CLIP training on
large-scale web-crawled datasets, named VeCLIP. We conduct a comprehensive
evaluation of VeCLIP across small, medium, and large scales of raw data. Our
results show significant advantages in image-text alignment and overall model
performance, underscoring the effectiveness of VeCLIP in improving CLIP
training. For example, VeCLIP achieves a remarkable over 20% improvement in
COCO and Flickr30k retrieval tasks under the 12M setting. For data efficiency,
we also achieve a notable over 3% improvement while using only 14% of the data
employed in the vanilla CLIP and 11% in ALIGN. | [
"Zhengfeng Lai",
"Haotian Zhang",
"Wentao Wu",
"Haoping Bai",
"Aleksei Timofeev",
"Xianzhi Du",
"Zhe Gan",
"Jiulong Shan",
"Chen-Nee Chuah",
"Yinfei Yang",
"Meng Cao"
] | 2023-10-11 17:49:13 | http://arxiv.org/abs/2310.07699v1 | http://arxiv.org/pdf/2310.07699v1 | 2310.07699v1 |
SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation | Explainable AI seeks to bring light to the decision-making processes of
black-box models. Traditional saliency-based methods, while highlighting
influential data segments, often lack semantic understanding. Recent
advancements, such as Concept Activation Vectors (CAVs) and Concept Bottleneck
Models (CBMs), offer concept-based explanations but necessitate human-defined
concepts. However, human-annotated concepts are expensive to attain. This paper
introduces the Concept Bottleneck Surrogate Models (SurroCBM), a novel
framework that aims to explain the black-box models with automatically
discovered concepts. SurroCBM identifies shared and unique concepts across
various black-box models and employs an explainable surrogate model for
post-hoc explanations. An effective training strategy using self-generated data
is proposed to enhance explanation quality continuously. Through extensive
experiments, we demonstrate the efficacy of SurroCBM in concept discovery and
explanation, underscoring its potential in advancing the field of explainable
AI. | [
"Bo Pan",
"Zhenke Liu",
"Yifei Zhang",
"Liang Zhao"
] | 2023-10-11 17:46:59 | http://arxiv.org/abs/2310.07698v1 | http://arxiv.org/pdf/2310.07698v1 | 2310.07698v1 |
Controllable Data Generation Via Iterative Data-Property Mutual Mappings | Deep generative models have been widely used for their ability to generate
realistic data samples in various areas, such as images, molecules, text, and
speech. One major goal of data generation is controllability, namely to
generate new data with desired properties. Despite growing interest in the area
of controllable generation, significant challenges still remain, including 1)
disentangling desired properties with unrelated latent variables, 2)
out-of-distribution property control, and 3) objective optimization for
out-of-distribution property control. To address these challenges, in this
paper, we propose a general framework to enhance VAE-based data generators with
property controllability and ensure disentanglement. Our proposed objective can
be optimized on both data seen and unseen in the training set. We propose a
training procedure to train the objective in a semi-supervised manner by
iteratively conducting mutual mappings between the data and properties. The
proposed framework is implemented on four VAE-based controllable generators to
evaluate its performance on property error, disentanglement, generation
quality, and training time. The results indicate that our proposed framework
enables more precise control over the properties of generated samples in a
short training time, ensuring the disentanglement and keeping the validity of
the generated samples. | [
"Bo Pan",
"Muran Qin",
"Shiyu Wang",
"Yifei Zhang",
"Liang Zhao"
] | 2023-10-11 17:34:56 | http://arxiv.org/abs/2310.07683v1 | http://arxiv.org/pdf/2310.07683v1 | 2310.07683v1 |
Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM | With the proliferation of image-based applications in various domains, the
need for accurate and interpretable image similarity measures has become
increasingly critical. Existing image similarity models often lack
transparency, making it challenging to understand the reasons why two images
are considered similar. In this paper, we propose the concept of explainable
image similarity, where the goal is the development of an approach, which is
capable of providing similarity scores along with visual factual and
counterfactual explanations. Along this line, we present a new framework, which
integrates Siamese Networks and Grad-CAM for providing explainable image
similarity and discuss the potential benefits and challenges of adopting this
approach. In addition, we provide a comprehensive discussion about factual and
counterfactual explanations provided by the proposed framework for assisting
decision making. The proposed approach has the potential to enhance the
interpretability, trustworthiness and user acceptance of image-based systems in
real-world image similarity applications. The implementation code can be found
in https://github.com/ioannislivieris/Grad_CAM_Siamese.git. | [
"Ioannis E. Livieris",
"Emmanuel Pintelas",
"Niki Kiriakidou",
"Panagiotis Pintelas"
] | 2023-10-11 17:21:48 | http://arxiv.org/abs/2310.07678v2 | http://arxiv.org/pdf/2310.07678v2 | 2310.07678v2 |
Composite Backdoor Attacks Against Large Language Models | Large language models (LLMs) have demonstrated superior performance compared
to previous methods on various tasks, and often serve as the foundation models
for many researches and services. However, the untrustworthy third-party LLMs
may covertly introduce vulnerabilities for downstream tasks. In this paper, we
explore the vulnerability of LLMs through the lens of backdoor attacks.
Different from existing backdoor attacks against LLMs, ours scatters multiple
trigger keys in different prompt components. Such a Composite Backdoor Attack
(CBA) is shown to be stealthier than implanting the same multiple trigger keys
in only a single component. CBA ensures that the backdoor is activated only
when all trigger keys appear. Our experiments demonstrate that CBA is effective
in both natural language processing (NLP) and multimodal tasks. For instance,
with $3\%$ poisoning samples against the LLaMA-7B model on the Emotion dataset,
our attack achieves a $100\%$ Attack Success Rate (ASR) with a False Triggered
Rate (FTR) below $2.06\%$ and negligible model accuracy degradation. The unique
characteristics of our CBA can be tailored for various practical scenarios,
e.g., targeting specific user groups. Our work highlights the necessity of
increased security research on the trustworthiness of foundation LLMs. | [
"Hai Huang",
"Zhengyu Zhao",
"Michael Backes",
"Yun Shen",
"Yang Zhang"
] | 2023-10-11 17:21:03 | http://arxiv.org/abs/2310.07676v1 | http://arxiv.org/pdf/2310.07676v1 | 2310.07676v1 |
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples | Learning transparent, interpretable controllers with offline data in
decision-making systems is an essential area of research due to its potential
to reduce the risk of applications in real-world systems. However, in
responsibility-sensitive settings such as healthcare, decision accountability
is of paramount importance, yet has not been adequately addressed by the
literature. This paper introduces the Accountable Offline Controller (AOC) that
employs the offline dataset as the Decision Corpus and performs accountable
control based on a tailored selection of examples, referred to as the Corpus
Subset. ABC operates effectively in low-data scenarios, can be extended to the
strictly offline imitation setting, and displays qualities of both conservation
and adaptability. We assess ABC's performance in both simulated and real-world
healthcare scenarios, emphasizing its capability to manage offline control
tasks with high levels of performance while maintaining accountability.
Keywords: Interpretable Reinforcement Learning, Explainable Reinforcement
Learning, Reinforcement Learning Transparency, Offline Reinforcement Learning,
Batched Control. | [
"Hao Sun",
"Alihan Hüyük",
"Daniel Jarrett",
"Mihaela van der Schaar"
] | 2023-10-11 17:20:32 | http://arxiv.org/abs/2310.07747v1 | http://arxiv.org/pdf/2310.07747v1 | 2310.07747v1 |
Stabilizing Estimates of Shapley Values with Control Variates | Shapley values are among the most popular tools for explaining predictions of
blackbox machine learning models. However, their high computational cost
motivates the use of sampling approximations, inducing a considerable degree of
uncertainty. To stabilize these model explanations, we propose ControlSHAP, an
approach based on the Monte Carlo technique of control variates. Our
methodology is applicable to any machine learning model and requires virtually
no extra computation or modeling effort. On several high-dimensional datasets,
we find it can produce dramatic reductions in the Monte Carlo variability of
Shapley estimates. | [
"Jeremy Goldwasser",
"Giles Hooker"
] | 2023-10-11 17:18:51 | http://arxiv.org/abs/2310.07672v1 | http://arxiv.org/pdf/2310.07672v1 | 2310.07672v1 |
GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media | The proliferation of social media platforms such as Twitter, Instagram, and
Weibo has significantly enhanced the dissemination of false information. This
phenomenon grants both individuals and governmental entities the ability to
shape public opinions, highlighting the need for deploying effective detection
methods. In this paper, we propose GraMuFeN, a model designed to detect fake
content by analyzing both the textual and image content of news. GraMuFeN
comprises two primary components: a text encoder and an image encoder. For
textual analysis, GraMuFeN treats each text as a graph and employs a Graph
Convolutional Neural Network (GCN) as the text encoder. Additionally, the
pre-trained ResNet-152, as a Convolutional Neural Network (CNN), has been
utilized as the image encoder. By integrating the outputs from these two
encoders and implementing a contrastive similarity loss function, GraMuFeN
achieves remarkable results. Extensive evaluations conducted on two publicly
available benchmark datasets for social media news indicate a 10 % increase in
micro F1-Score, signifying improvement over existing state-of-the-art models.
These findings underscore the effectiveness of combining GCN and CNN models for
detecting fake news in multi-modal data, all while minimizing the additional
computational burden imposed by model parameters. | [
"Makan Kananian",
"Fatima Badiei",
"S. AmirAli Gh. Ghahramani"
] | 2023-10-11 17:17:40 | http://arxiv.org/abs/2310.07668v1 | http://arxiv.org/pdf/2310.07668v1 | 2310.07668v1 |
Global Minima, Recoverability Thresholds, and Higher-Order Structure in GNNS | We analyze the performance of graph neural network (GNN) architectures from
the perspective of random graph theory. Our approach promises to complement
existing lenses on GNN analysis, such as combinatorial expressive power and
worst-case adversarial analysis, by connecting the performance of GNNs to
typical-case properties of the training data. First, we theoretically
characterize the nodewise accuracy of one- and two-layer GCNs relative to the
contextual stochastic block model (cSBM) and related models. We additionally
prove that GCNs cannot beat linear models under certain circumstances. Second,
we numerically map the recoverability thresholds, in terms of accuracy, of four
diverse GNN architectures (GCN, GAT, SAGE, and Graph Transformer) under a
variety of assumptions about the data. Sample results of this second analysis
include: heavy-tailed degree distributions enhance GNN performance, GNNs can
work well on strongly heterophilous graphs, and SAGE and Graph Transformer can
perform well on arbitrarily noisy edge data, but no architecture handled
sufficiently noisy feature data well. Finally, we show how both specific
higher-order structures in synthetic data and the mix of empirical structures
in real data have dramatic effects (usually negative) on GNN performance. | [
"Drake Brown",
"Trevor Garrity",
"Kaden Parker",
"Jason Oliphant",
"Stone Carson",
"Cole Hanson",
"Zachary Boyd"
] | 2023-10-11 17:16:33 | http://arxiv.org/abs/2310.07667v1 | http://arxiv.org/pdf/2310.07667v1 | 2310.07667v1 |
Deep Backtracking Counterfactuals for Causally Compliant Explanations | Counterfactuals can offer valuable insights by answering what would have been
observed under altered circumstances, conditional on a factual observation.
Whereas the classical interventional interpretation of counterfactuals has been
studied extensively, backtracking constitutes a less studied alternative the
backtracking principle has emerged as an alternative philosophy where all
causal laws are kept intact. In the present work, we introduce a practical
method for computing backtracking counterfactuals in structural causal models
that consist of deep generative components. To this end, we impose conditions
on the structural assignments that enable the generation of counterfactuals by
solving a tractable constrained optimization problem in the structured latent
space of a causal model. Our formulation also facilitates a comparison with
methods in the field of counterfactual explanations. Compared to these, our
method represents a versatile, modular and causally compliant alternative. We
demonstrate these properties experimentally on a modified version of MNIST and
CelebA. | [
"Klaus-Rudolf Kladny",
"Julius von Kügelgen",
"Bernhard Schölkopf",
"Michael Muehlebach"
] | 2023-10-11 17:11:10 | http://arxiv.org/abs/2310.07665v1 | http://arxiv.org/pdf/2310.07665v1 | 2310.07665v1 |
The First Pathloss Radio Map Prediction Challenge | To foster research and facilitate fair comparisons among recently proposed
pathloss radio map prediction methods, we have launched the ICASSP 2023 First
Pathloss Radio Map Prediction Challenge. In this short overview paper, we
briefly describe the pathloss prediction problem, the provided datasets, the
challenge task and the challenge evaluation methodology. Finally, we present
the results of the challenge. | [
"Çağkan Yapar",
"Fabian Jaensch",
"Ron Levie",
"Gitta Kutyniok",
"Giuseppe Caire"
] | 2023-10-11 17:00:03 | http://arxiv.org/abs/2310.07658v1 | http://arxiv.org/pdf/2310.07658v1 | 2310.07658v1 |
Audio-Visual Neural Syntax Acquisition | We study phrase structure induction from visually-grounded speech. The core
idea is to first segment the speech waveform into sequences of word segments,
and subsequently induce phrase structure using the inferred segment-level
continuous representations. We present the Audio-Visual Neural Syntax Learner
(AV-NSL) that learns phrase structure by listening to audio and looking at
images, without ever being exposed to text. By training on paired images and
spoken captions, AV-NSL exhibits the capability to infer meaningful phrase
structures that are comparable to those derived by naturally-supervised text
parsers, for both English and German. Our findings extend prior work in
unsupervised language acquisition from speech and grounded grammar induction,
and present one approach to bridge the gap between the two topics. | [
"Cheng-I Jeff Lai",
"Freda Shi",
"Puyuan Peng",
"Yoon Kim",
"Kevin Gimpel",
"Shiyu Chang",
"Yung-Sung Chuang",
"Saurabhchand Bhati",
"David Cox",
"David Harwath",
"Yang Zhang",
"Karen Livescu",
"James Glass"
] | 2023-10-11 16:54:57 | http://arxiv.org/abs/2310.07654v1 | http://arxiv.org/pdf/2310.07654v1 | 2310.07654v1 |
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals | Multimodal emotion recognition from physiological signals is receiving an
increasing amount of attention due to the impossibility to control them at will
unlike behavioral reactions, thus providing more reliable information. Existing
deep learning-based methods still rely on extracted handcrafted features, not
taking full advantage of the learning ability of neural networks, and often
adopt a single-modality approach, while human emotions are inherently expressed
in a multimodal way. In this paper, we propose a hypercomplex multimodal
network equipped with a novel fusion module comprising parameterized
hypercomplex multiplications. Indeed, by operating in a hypercomplex domain the
operations follow algebraic rules which allow to model latent relations among
learned feature dimensions for a more effective fusion step. We perform
classification of valence and arousal from electroencephalogram (EEG) and
peripheral physiological signals, employing the publicly available database
MAHNOB-HCI surpassing a multimodal state-of-the-art network. The code of our
work is freely available at https://github.com/ispamm/MHyEEG. | [
"Eleonora Lopez",
"Eleonora Chiarantano",
"Eleonora Grassucci",
"Danilo Comminiello"
] | 2023-10-11 16:45:44 | http://arxiv.org/abs/2310.07648v1 | http://arxiv.org/pdf/2310.07648v1 | 2310.07648v1 |
Rethinking the BERT-like Pretraining for DNA Sequences | With the success of large-scale pretraining in NLP, there is an increasing
trend of applying it to the domain of life sciences. In particular, pretraining
methods based on DNA sequences have garnered growing attention due to their
potential to capture generic information about genes. However, existing
pretraining methods for DNA sequences largely rely on direct adoptions of BERT
pretraining from NLP, lacking a comprehensive understanding and a specifically
tailored approach. To address this research gap, we first conducted a series of
exploratory experiments and gained several insightful observations: 1) In the
fine-tuning phase of downstream tasks, when using K-mer overlapping
tokenization instead of K-mer non-overlapping tokenization, both overlapping
and non-overlapping pretraining weights show consistent performance
improvement.2) During the pre-training process, using K-mer overlapping
tokenization quickly produces clear K-mer embeddings and reduces the loss to a
very low level, while using K-mer non-overlapping tokenization results in less
distinct embeddings and continuously decreases the loss. 3) Using overlapping
tokenization causes the self-attention in the intermediate layers of
pre-trained models to tend to overly focus on certain tokens, reflecting that
these layers are not adequately optimized. In summary, overlapping tokenization
can benefit the fine-tuning of downstream tasks but leads to inadequate
pretraining with fast convergence. To unleash the pretraining potential, we
introduce a novel approach called RandomMask, which gradually increases the
task difficulty of BERT-like pretraining by continuously expanding its mask
boundary, forcing the model to learn more knowledge. RandomMask is simple but
effective, achieving top-tier performance across 26 datasets of 28 datasets
spanning 7 downstream tasks. | [
"Chaoqi Liang",
"Weiqiang Bai",
"Lifeng Qiao",
"Yuchen Ren",
"Jianle Sun",
"Peng Ye",
"Hongliang Yan",
"Xinzhu Ma",
"Wangmeng Zuo",
"Wanli Ouyang"
] | 2023-10-11 16:40:57 | http://arxiv.org/abs/2310.07644v2 | http://arxiv.org/pdf/2310.07644v2 | 2310.07644v2 |
Evaluating Large Language Models at Evaluating Instruction Following | As research in large language models (LLMs) continues to accelerate,
LLM-based evaluation has emerged as a scalable and cost-effective alternative
to human evaluations for comparing the ever increasing list of models. This
paper investigates the efficacy of these "LLM evaluators", particularly in
using them to assess instruction following, a metric that gauges how closely
generated text adheres to the given instruction. We introduce a challenging
meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM
evaluator in discerning instruction-following outputs. The authors manually
curated 419 pairs of outputs, one adhering to instructions while the other
diverging, yet may possess deceptive qualities that mislead an LLM evaluator,
e.g., a more engaging tone. Contrary to existing meta-evaluation, we discover
that different evaluators (i.e., combinations of LLMs and prompts) exhibit
distinct performance on LLMBar and even the highest-scoring ones have
substantial room for improvement. We also present a novel suite of prompting
strategies that further close the gap between LLM and human evaluators. With
LLMBar, we hope to offer more insight into LLM evaluators and foster future
research in developing better instruction-following models. | [
"Zhiyuan Zeng",
"Jiatong Yu",
"Tianyu Gao",
"Yu Meng",
"Tanya Goyal",
"Danqi Chen"
] | 2023-10-11 16:38:11 | http://arxiv.org/abs/2310.07641v1 | http://arxiv.org/pdf/2310.07641v1 | 2310.07641v1 |
Prompt Backdoors in Visual Prompt Learning | Fine-tuning large pre-trained computer vision models is infeasible for
resource-limited users. Visual prompt learning (VPL) has thus emerged to
provide an efficient and flexible alternative to model fine-tuning through
Visual Prompt as a Service (VPPTaaS). Specifically, the VPPTaaS provider
optimizes a visual prompt given downstream data, and downstream users can use
this prompt together with the large pre-trained model for prediction. However,
this new learning paradigm may also pose security risks when the VPPTaaS
provider instead provides a malicious visual prompt. In this paper, we take the
first step to explore such risks through the lens of backdoor attacks.
Specifically, we propose BadVisualPrompt, a simple yet effective backdoor
attack against VPL. For example, poisoning $5\%$ CIFAR10 training data leads to
above $99\%$ attack success rates with only negligible model accuracy drop by
$1.5\%$. In particular, we identify and then address a new technical challenge
related to interactions between the backdoor trigger and visual prompt, which
does not exist in conventional, model-level backdoors. Moreover, we provide
in-depth analyses of seven backdoor defenses from model, prompt, and input
levels. Overall, all these defenses are either ineffective or impractical to
mitigate our BadVisualPrompt, implying the critical vulnerability of VPL. | [
"Hai Huang",
"Zhengyu Zhao",
"Michael Backes",
"Yun Shen",
"Yang Zhang"
] | 2023-10-11 16:25:45 | http://arxiv.org/abs/2310.07632v1 | http://arxiv.org/pdf/2310.07632v1 | 2310.07632v1 |
Deep Reinforcement Learning for Autonomous Cyber Operations: A Survey | The rapid increase in the number of cyber-attacks in recent years raises the
need for principled methods for defending networks against malicious actors.
Deep reinforcement learning (DRL) has emerged as a promising approach for
mitigating these attacks. However, while DRL has shown much potential for
cyber-defence, numerous challenges must be overcome before DRL can be applied
to autonomous cyber-operations (ACO) at scale. Principled methods are required
for environments that confront learners with very high-dimensional state
spaces, large multi-discrete action spaces, and adversarial learning. Recent
works have reported success in solving these problems individually. There have
also been impressive engineering efforts towards solving all three for
real-time strategy games. However, applying DRL to the full ACO problem remains
an open challenge. Here, we survey the relevant DRL literature and
conceptualize an idealised ACO-DRL agent. We provide: i.) A summary of the
domain properties that define the ACO problem; ii.) A comprehensive evaluation
of the extent to which domains used for benchmarking DRL approaches are
comparable to ACO; iii.) An overview of state-of-the-art approaches for scaling
DRL to domains that confront learners with the curse of dimensionality, and;
iv.) A survey and critique of current methods for limiting the exploitability
of agents within adversarial settings from the perspective of ACO. We conclude
with open research questions that we hope will motivate future directions for
researchers and practitioners working on ACO. | [
"Gregory Palmer",
"Chris Parry",
"Daniel J. B. Harrold",
"Chris Willis"
] | 2023-10-11 16:24:14 | http://arxiv.org/abs/2310.07745v1 | http://arxiv.org/pdf/2310.07745v1 | 2310.07745v1 |
Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates | Floods can be very destructive causing heavy damage to life, property, and
livelihoods. Global climate change and the consequent sea-level rise have
increased the occurrence of extreme weather events, resulting in elevated and
frequent flood risk. Therefore, accurate and timely flood forecasting in
coastal river systems is critical to facilitate good flood management. However,
the computational tools currently used are either slow or inaccurate. In this
paper, we propose a Flood prediction tool using Graph Transformer Network
(FloodGTN) for river systems. More specifically, FloodGTN learns the
spatio-temporal dependencies of water levels at different monitoring stations
using Graph Neural Networks (GNNs) and an LSTM. It is currently implemented to
consider external covariates such as rainfall, tide, and the settings of
hydraulic structures (e.g., outflows of dams, gates, pumps, etc.) along the
river. We use a Transformer to learn the attention given to external covariates
in computing water levels. We apply the FloodGTN tool to data from the South
Florida Water Management District, which manages a coastal area prone to
frequent storms and hurricanes. Experimental results show that FloodGTN
outperforms the physics-based model (HEC-RAS) by achieving higher accuracy with
70% improvement while speeding up run times by at least 500x. | [
"Jimeng Shi",
"Vitalii Stebliankin",
"Zhaonan Wang",
"Shaowen Wang",
"Giri Narasimhan"
] | 2023-10-11 16:24:06 | http://arxiv.org/abs/2310.07631v1 | http://arxiv.org/pdf/2310.07631v1 | 2310.07631v1 |
Differentiable Euler Characteristic Transforms for Shape Classification | The Euler Characteristic Transform (ECT) has proven to be a powerful
representation, combining geometrical and topological characteristics of shapes
and graphs. However, the ECT was hitherto unable to learn task-specific
representations. We overcome this issue and develop a novel computational layer
that enables learning the ECT in an end-to-end fashion. Our method DECT is fast
and computationally efficient, while exhibiting performance on a par with more
complex models in both graph and point cloud classification tasks. Moreover, we
show that this seemingly unexpressive statistic still provides the same
topological expressivity as more complex topological deep learning layers
provide. | [
"Ernst Roell",
"Bastian Rieck"
] | 2023-10-11 16:23:07 | http://arxiv.org/abs/2310.07630v1 | http://arxiv.org/pdf/2310.07630v1 | 2310.07630v1 |
Unsupervised Learning of Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations | Satellite-based remote sensing missions have revolutionized our understanding
of the Ocean state and dynamics. Among them, spaceborne altimetry provides
valuable measurements of Sea Surface Height (SSH), which is used to estimate
surface geostrophic currents. However, due to the sensor technology employed,
important gaps occur in SSH observations. Complete SSH maps are produced by the
altimetry community using linear Optimal Interpolations (OI) such as the
widely-used Data Unification and Altimeter Combination System (DUACS). However,
OI is known for producing overly smooth fields and thus misses some
mesostructures and eddies. On the other hand, Sea Surface Temperature (SST)
products have much higher data coverage and SST is physically linked to
geostrophic currents through advection. We design a realistic twin experiment
to emulate the satellite observations of SSH and SST to evaluate interpolation
methods. We introduce a deep learning network able to use SST information, and
a trainable in two settings: one where we have no access to ground truth during
training and one where it is accessible. Our investigation involves a
comparative analysis of the aforementioned network when trained using either
supervised or unsupervised loss functions. We assess the quality of SSH
reconstructions and further evaluate the network's performance in terms of eddy
detection and physical properties. We find that it is possible, even in an
unsupervised setting to use SST to improve reconstruction performance compared
to SST-agnostic interpolations. We compare our reconstructions to DUACS's and
report a decrease of 41\% in terms of root mean squared error. | [
"Theo Archambault",
"Arthur Filoche",
"Anastase Charantonis",
"Dominique Bereziat",
"Sylvie Thiria"
] | 2023-10-11 16:09:09 | http://arxiv.org/abs/2310.07626v1 | http://arxiv.org/pdf/2310.07626v1 | 2310.07626v1 |
PHYDI: Initializing Parameterized Hypercomplex Neural Networks as Identity Functions | Neural models based on hypercomplex algebra systems are growing and
prolificating for a plethora of applications, ranging from computer vision to
natural language processing. Hand in hand with their adoption, parameterized
hypercomplex neural networks (PHNNs) are growing in size and no techniques have
been adopted so far to control their convergence at a large scale. In this
paper, we study PHNNs convergence and propose parameterized hypercomplex
identity initialization (PHYDI), a method to improve their convergence at
different scales, leading to more robust performance when the number of layers
scales up, while also reaching the same performance with fewer iterations. We
show the effectiveness of this approach in different benchmarks and with common
PHNNs with ResNets- and Transformer-based architecture. The code is available
at https://github.com/ispamm/PHYDI. | [
"Matteo Mancanelli",
"Eleonora Grassucci",
"Aurelio Uncini",
"Danilo Comminiello"
] | 2023-10-11 15:56:55 | http://arxiv.org/abs/2310.07612v1 | http://arxiv.org/pdf/2310.07612v1 | 2310.07612v1 |
Survey on Imbalanced Data, Representation Learning and SEP Forecasting | Deep Learning methods have significantly advanced various data-driven tasks
such as regression, classification, and forecasting. However, much of this
progress has been predicated on the strong but often unrealistic assumption
that training datasets are balanced with respect to the targets they contain.
This misalignment with real-world conditions, where data is frequently
imbalanced, hampers the effectiveness of such models in practical applications.
Methods that reconsider that assumption and tackle real-world imbalances have
begun to emerge and explore avenues to address this challenge. One such
promising avenue is representation learning, which enables models to capture
complex data characteristics and generalize better to minority classes. By
focusing on a richer representation of the feature space, these techniques hold
the potential to mitigate the impact of data imbalance. In this survey, we
present deep learning works that step away from the balanced-data assumption,
employing strategies like representation learning to better approximate
real-world imbalances. We also highlight a critical application in SEP
forecasting where addressing data imbalance is paramount for success. | [
"Josias Moukpe"
] | 2023-10-11 15:38:53 | http://arxiv.org/abs/2310.07598v1 | http://arxiv.org/pdf/2310.07598v1 | 2310.07598v1 |
Prospective Side Information for Latent MDPs | In many interactive decision-making settings, there is latent and unobserved
information that remains fixed. Consider, for example, a dialogue system, where
complete information about a user, such as the user's preferences, is not
given. In such an environment, the latent information remains fixed throughout
each episode, since the identity of the user does not change during an
interaction. This type of environment can be modeled as a Latent Markov
Decision Process (LMDP), a special instance of Partially Observed Markov
Decision Processes (POMDPs). Previous work established exponential lower bounds
in the number of latent contexts for the LMDP class. This puts forward a
question: under which natural assumptions a near-optimal policy of an LMDP can
be efficiently learned? In this work, we study the class of LMDPs with {\em
prospective side information}, when an agent receives additional, weakly
revealing, information on the latent context at the beginning of each episode.
We show that, surprisingly, this problem is not captured by contemporary
settings and algorithms designed for partially observed environments. We then
establish that any sample efficient algorithm must suffer at least
$\Omega(K^{2/3})$-regret, as opposed to standard $\Omega(\sqrt{K})$ lower
bounds, and design an algorithm with a matching upper bound. | [
"Jeongyeol Kwon",
"Yonathan Efroni",
"Shie Mannor",
"Constantine Caramanis"
] | 2023-10-11 15:37:31 | http://arxiv.org/abs/2310.07596v1 | http://arxiv.org/pdf/2310.07596v1 | 2310.07596v1 |
Transformers for Green Semantic Communication: Less Energy, More Semantics | Semantic communication aims to transmit meaningful and effective information
rather than focusing on individual symbols or bits, resulting in benefits like
reduced latency, bandwidth usage, and higher throughput compared to traditional
communication. However, semantic communication poses significant challenges due
to the need for universal metrics for benchmarking the joint effects of
semantic information loss and practical energy consumption. This research
presents a novel multi-objective loss function named "Energy-Optimized Semantic
Loss" (EOSL), addressing the challenge of balancing semantic information loss
and energy consumption. Through comprehensive experiments on transformer
models, including CPU and GPU energy usage, it is demonstrated that EOSL-based
encoder model selection can save up to 90\% of energy while achieving a 44\%
improvement in semantic similarity performance during inference in this
experiment. This work paves the way for energy-efficient neural network
selection and the development of greener semantic communication architectures. | [
"Shubhabrata Mukherjee",
"Cory Beard",
"Sejun Song"
] | 2023-10-11 15:35:20 | http://arxiv.org/abs/2310.07592v1 | http://arxiv.org/pdf/2310.07592v1 | 2310.07592v1 |
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality | Multi-Label Text Classification (MLTC) aims to assign the most relevant
labels to each given text. Existing methods demonstrate that label dependency
can help to improve the model's performance. However, the introduction of label
dependency may cause the model to suffer from unwanted prediction bias. In this
study, we attribute the bias to the model's misuse of label dependency, i.e.,
the model tends to utilize the correlation shortcut in label dependency rather
than fusing text information and label dependency for prediction. Motivated by
causal inference, we propose a CounterFactual Text Classifier (CFTC) to
eliminate the correlation bias, and make causality-based predictions.
Specifically, our CFTC first adopts the predict-then-modify backbone to extract
precise label information embedded in label dependency, then blocks the
correlation shortcut through the counterfactual de-bias technique with the help
of the human causal graph. Experimental results on three datasets demonstrate
that our CFTC significantly outperforms the baselines and effectively
eliminates the correlation bias in datasets. | [
"Caoyun Fan",
"Wenqing Chen",
"Jidong Tian",
"Yitian Li",
"Hao He",
"Yaohui Jin"
] | 2023-10-11 15:28:44 | http://arxiv.org/abs/2310.07588v1 | http://arxiv.org/pdf/2310.07588v1 | 2310.07588v1 |
Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer | Data privacy and long-tailed distribution are the norms rather than the
exception in many real-world tasks. This paper investigates a federated
long-tailed learning (Fed-LT) task in which each client holds a locally
heterogeneous dataset; if the datasets can be globally aggregated, they jointly
exhibit a long-tailed distribution. Under such a setting, existing federated
optimization and/or centralized long-tailed learning methods hardly apply due
to challenges in (a) characterizing the global long-tailed distribution under
privacy constraints and (b) adjusting the local learning strategy to cope with
the head-tail imbalance. In response, we propose a method termed
$\texttt{Fed-GraB}$, comprised of a Self-adjusting Gradient Balancer (SGB)
module that re-weights clients' gradients in a closed-loop manner, based on the
feedback of global long-tailed distribution evaluated by a Direct Prior
Analyzer (DPA) module. Using $\texttt{Fed-GraB}$, clients can effectively
alleviate the distribution drift caused by data heterogeneity during the model
training process and obtain a global model with better performance on the
minority classes while maintaining the performance of the majority classes.
Extensive experiments demonstrate that $\texttt{Fed-GraB}$ achieves
state-of-the-art performance on representative datasets such as CIFAR-10-LT,
CIFAR-100-LT, ImageNet-LT, and iNaturalist. | [
"Zikai Xiao",
"Zihan Chen",
"Songshang Liu",
"Hualiang Wang",
"Yang Feng",
"Jin Hao",
"Joey Tianyi Zhou",
"Jian Wu",
"Howard Hao Yang",
"Zuozhu Liu"
] | 2023-10-11 15:28:39 | http://arxiv.org/abs/2310.07587v3 | http://arxiv.org/pdf/2310.07587v3 | 2310.07587v3 |
Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT | Foundation models exhibit significant capabilities in decision-making and
logical deductions. Nonetheless, a continuing discourse persists regarding
their genuine understanding of the world as opposed to mere stochastic mimicry.
This paper meticulously examines a simple transformer trained for Othello,
extending prior research to enhance comprehension of the emergent world model
of Othello-GPT. The investigation reveals that Othello-GPT encapsulates a
linear representation of opposing pieces, a factor that causally steers its
decision-making process. This paper further elucidates the interplay between
the linear world representation and causal decision-making, and their
dependence on layer depth and model complexity. We have made the code public. | [
"Dean S. Hazineh",
"Zechen Zhang",
"Jeffery Chiu"
] | 2023-10-11 15:20:07 | http://arxiv.org/abs/2310.07582v2 | http://arxiv.org/pdf/2310.07582v2 | 2310.07582v2 |
In-Context Unlearning: Language Models as Few Shot Unlearners | Machine unlearning, the study of efficiently removing the impact of specific
training points on the trained model, has garnered increased attention of late,
driven by the need to comply with privacy regulations like the Right to be
Forgotten. Although unlearning is particularly relevant for LLMs in light of
the copyright issues they raise, achieving precise unlearning is
computationally infeasible for very large models. To this end, recent work has
proposed several algorithms which approximate the removal of training data
without retraining the model. These algorithms crucially rely on access to the
model parameters in order to update them, an assumption that may not hold in
practice due to computational constraints or when the LLM is accessed via API.
In this work, we propose a new class of unlearning methods for LLMs we call
''In-Context Unlearning'', providing inputs in context and without having to
update model parameters. To unlearn a particular training instance, we provide
the instance alongside a flipped label and additional correctly labelled
instances which are prepended as inputs to the LLM at inference time. Our
experimental results demonstrate that these contexts effectively remove
specific information from the training set while maintaining performance levels
that are competitive with (or in some cases exceed) state-of-the-art unlearning
methods that require access to the LLM parameters. | [
"Martin Pawelczyk",
"Seth Neel",
"Himabindu Lakkaraju"
] | 2023-10-11 15:19:31 | http://arxiv.org/abs/2310.07579v2 | http://arxiv.org/pdf/2310.07579v2 | 2310.07579v2 |
Analyzing Trendy Twitter Hashtags in the 2022 French Election | Regressions trained to predict the future activity of social media users need
rich features for accurate predictions. Many advanced models exist to generate
such features; however, the time complexities of their computations are often
prohibitive when they run on enormous data-sets. Some studies have shown that
simple semantic network features can be rich enough to use for regressions
without requiring complex computations. We propose a method for using semantic
networks as user-level features for machine learning tasks. We conducted an
experiment using a semantic network of 1037 Twitter hashtags from a corpus of
3.7 million tweets related to the 2022 French presidential election. A
bipartite graph is formed where hashtags are nodes and weighted edges connect
the hashtags reflecting the number of Twitter users that interacted with both
hashtags. The graph is then transformed into a maximum-spanning tree with the
most popular hashtag as its root node to construct a hierarchy amongst the
hashtags. We then provide a vector feature for each user based on this tree. To
validate the usefulness of our semantic feature we performed a regression
experiment to predict the response rate of each user with six emotions like
anger, enjoyment, or disgust. Our semantic feature performs well with the
regression with most emotions having $R^2$ above 0.5. These results suggest
that our semantic feature could be considered for use in further experiments
predicting social media response on big data-sets. | [
"Aamir Mandviwalla",
"Lake Yin",
"Boleslaw K. Szymanski"
] | 2023-10-11 15:17:55 | http://arxiv.org/abs/2310.07576v1 | http://arxiv.org/pdf/2310.07576v1 | 2310.07576v1 |
ROMO: Retrieval-enhanced Offline Model-based Optimization | Data-driven black-box model-based optimization (MBO) problems arise in a
great number of practical application scenarios, where the goal is to find a
design over the whole space maximizing a black-box target function based on a
static offline dataset. In this work, we consider a more general but
challenging MBO setting, named constrained MBO (CoMBO), where only part of the
design space can be optimized while the rest is constrained by the environment.
A new challenge arising from CoMBO is that most observed designs that satisfy
the constraints are mediocre in evaluation. Therefore, we focus on optimizing
these mediocre designs in the offline dataset while maintaining the given
constraints rather than further boosting the best observed design in the
traditional MBO setting. We propose retrieval-enhanced offline model-based
optimization (ROMO), a new derivable forward approach that retrieves the
offline dataset and aggregates relevant samples to provide a trusted
prediction, and use it for gradient-based optimization. ROMO is simple to
implement and outperforms state-of-the-art approaches in the CoMBO setting.
Empirically, we conduct experiments on a synthetic Hartmann (3D) function
dataset, an industrial CIO dataset, and a suite of modified tasks in the
Design-Bench benchmark. Results show that ROMO performs well in a wide range of
constrained optimization tasks. | [
"Mingcheng Chen",
"Haoran Zhao",
"Yuxiang Zhao",
"Hulei Fan",
"Hongqiao Gao",
"Yong Yu",
"Zheng Tian"
] | 2023-10-11 15:04:33 | http://arxiv.org/abs/2310.07560v2 | http://arxiv.org/pdf/2310.07560v2 | 2310.07560v2 |
Smootheness-Adaptive Dynamic Pricing with Nonparametric Demand Learning | We study the dynamic pricing problem where the demand function is
nonparametric and H\"older smooth, and we focus on adaptivity to the unknown
H\"older smoothness parameter $\beta$ of the demand function. Traditionally the
optimal dynamic pricing algorithm heavily relies on the knowledge of $\beta$ to
achieve a minimax optimal regret of
$\widetilde{O}(T^{\frac{\beta+1}{2\beta+1}})$. However, we highlight the
challenge of adaptivity in this dynamic pricing problem by proving that no
pricing policy can adaptively achieve this minimax optimal regret without
knowledge of $\beta$. Motivated by the impossibility result, we propose a
self-similarity condition to enable adaptivity. Importantly, we show that the
self-similarity condition does not compromise the problem's inherent complexity
since it preserves the regret lower bound
$\Omega(T^{\frac{\beta+1}{2\beta+1}})$. Furthermore, we develop a
smoothness-adaptive dynamic pricing algorithm and theoretically prove that the
algorithm achieves this minimax optimal regret bound without the prior
knowledge $\beta$. | [
"Zeqi Ye",
"Hansheng Jiang"
] | 2023-10-11 15:02:13 | http://arxiv.org/abs/2310.07558v1 | http://arxiv.org/pdf/2310.07558v1 | 2310.07558v1 |
Improving Fairness-Accuracy tradeoff with few Test Samples under Covariate Shift | Covariate shift in the test data can significantly downgrade both the
accuracy and the fairness performance of the model. Ensuring fairness across
different sensitive groups in such settings is of paramount importance due to
societal implications like criminal justice. We operate under the unsupervised
regime where only a small set of unlabeled test samples along with a labeled
training set is available. Towards this problem, we make three contributions.
First is a novel composite weighted entropy based objective for prediction
accuracy which is optimized along with a representation matching loss for
fairness. We experimentally verify that optimizing with our loss formulation
outperforms a number of state-of-the-art baselines in the pareto sense with
respect to the fairness-accuracy tradeoff on several standard datasets. Our
second contribution is a new setting we term Asymmetric Covariate Shift that,
to the best of our knowledge, has not been studied before. Asymmetric covariate
shift occurs when distribution of covariates of one group shifts significantly
compared to the other groups and this happens when a dominant group is
over-represented. While this setting is extremely challenging for current
baselines, We show that our proposed method significantly outperforms them. Our
third contribution is theoretical, where we show that our weighted entropy term
along with prediction loss on the training set approximates test loss under
covariate shift. Empirically and through formal sample complexity bounds, we
show that this approximation to the unseen test loss does not depend on
importance sampling variance which affects many other baselines. | [
"Shreyas Havaldar",
"Jatin Chauhan",
"Karthikeyan Shanmugam",
"Jay Nandy",
"Aravindan Raghuveer"
] | 2023-10-11 14:39:51 | http://arxiv.org/abs/2310.07535v1 | http://arxiv.org/pdf/2310.07535v1 | 2310.07535v1 |
Human-Centered Evaluation of XAI Methods | In the ever-evolving field of Artificial Intelligence, a critical challenge
has been to decipher the decision-making processes within the so-called "black
boxes" in deep learning. Over recent years, a plethora of methods have emerged,
dedicated to explaining decisions across diverse tasks. Particularly in tasks
like image classification, these methods typically identify and emphasize the
pivotal pixels that most influence a classifier's prediction. Interestingly,
this approach mirrors human behavior: when asked to explain our rationale for
classifying an image, we often point to the most salient features or aspects.
Capitalizing on this parallel, our research embarked on a user-centric study.
We sought to objectively measure the interpretability of three leading
explanation methods: (1) Prototypical Part Network, (2) Occlusion, and (3)
Layer-wise Relevance Propagation. Intriguingly, our results highlight that
while the regions spotlighted by these methods can vary widely, they all offer
humans a nearly equivalent depth of understanding. This enables users to
discern and categorize images efficiently, reinforcing the value of these
methods in enhancing AI transparency. | [
"Karam Dawoud",
"Wojciech Samek",
"Peter Eisert",
"Sebastian Lapuschkin",
"Sebastian Bosse"
] | 2023-10-11 14:39:12 | http://arxiv.org/abs/2310.07534v2 | http://arxiv.org/pdf/2310.07534v2 | 2310.07534v2 |
Provable Advantage of Parameterized Quantum Circuit in Function Approximation | Understanding the power of parameterized quantum circuits (PQCs) in
accomplishing machine learning tasks is one of the most important questions in
quantum machine learning. In this paper, we analyze the expressivity of PQCs
through the lens of function approximation. Previously established universal
approximation theorems for PQCs are mainly nonconstructive, leading us to the
following question: How large do the PQCs need to be to approximate the target
function up to a given error? We exhibit explicit constructions of data
re-uploading PQCs for approximating continuous and smooth functions and
establish quantitative approximation error bounds in terms of the width, the
depth and the number of trainable parameters of the PQCs. To achieve this, we
utilize techniques from quantum signal processing and linear combinations of
unitaries to construct PQCs that implement multivariate polynomials. We
implement global and local approximation techniques using Bernstein polynomials
and local Taylor expansion and analyze their performances in the quantum
setting. We also compare our proposed PQCs to nearly optimal deep neural
networks in approximating high-dimensional smooth functions, showing that the
ratio between model sizes of PQC and deep neural networks is exponentially
small with respect to the input dimension. This suggests a potentially novel
avenue for showcasing quantum advantages in quantum machine learning. | [
"Zhan Yu",
"Qiuhao Chen",
"Yuling Jiao",
"Yinan Li",
"Xiliang Lu",
"Xin Wang",
"Jerry Zhijian Yang"
] | 2023-10-11 14:29:11 | http://arxiv.org/abs/2310.07528v1 | http://arxiv.org/pdf/2310.07528v1 | 2310.07528v1 |
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning | Posterior sampling allows the exploitation of prior knowledge of the
environment's transition dynamics to improve the sample efficiency of
reinforcement learning. The prior is typically specified as a class of
parametric distributions, a task that can be cumbersome in practice, often
resulting in the choice of uninformative priors. In this work, we propose a
novel posterior sampling approach in which the prior is given as a (partial)
causal graph over the environment's variables. The latter is often more natural
to design, such as listing known causal dependencies between biometric features
in a medical treatment study. Specifically, we propose a hierarchical Bayesian
procedure, called C-PSRL, simultaneously learning the full causal graph at the
higher level and the parameters of the resulting factored dynamics at the lower
level. For this procedure, we provide an analysis of its Bayesian regret, which
explicitly connects the regret rate with the degree of prior knowledge. Our
numerical evaluation conducted in illustrative domains confirms that C-PSRL
strongly improves the efficiency of posterior sampling with an uninformative
prior while performing close to posterior sampling with the full causal graph. | [
"Mirco Mutti",
"Riccardo De Santi",
"Marcello Restelli",
"Alexander Marx",
"Giorgia Ramponi"
] | 2023-10-11 14:16:04 | http://arxiv.org/abs/2310.07518v1 | http://arxiv.org/pdf/2310.07518v1 | 2310.07518v1 |
A Unified Remote Sensing Anomaly Detector Across Modalities and Scenes via Deviation Relationship Learning | Remote sensing anomaly detector can find the objects deviating from the
background as potential targets. Given the diversity in earth anomaly types, a
unified anomaly detector across modalities and scenes should be cost-effective
and flexible to new earth observation sources and anomaly types. However, the
current anomaly detectors are limited to a single modality and single scene,
since they aim to learn the varying background distribution. Motivated by the
universal anomaly deviation pattern, in that anomalies exhibit deviations from
their local context, we exploit this characteristic to build a unified anomaly
detector. Firstly, we reformulate the anomaly detection task as an undirected
bilayer graph based on the deviation relationship, where the anomaly score is
modeled as the conditional probability, given the pattern of the background and
normal objects. The learning objective is then expressed as a conditional
probability ranking problem. Furthermore, we design an instantiation of the
reformulation in the data, architecture, and optimization aspects. Simulated
spectral and spatial anomalies drive the instantiated architecture. The model
is optimized directly for the conditional probability ranking. The proposed
model was validated in five modalities including the hyperspectral, visible
light, synthetic aperture radar (SAR), infrared and low light to show its
unified detection ability. | [
"Jingtao Li",
"Xinyu Wang",
"Hengwei Zhao",
"Liangpei Zhang",
"Yanfei Zhong"
] | 2023-10-11 14:07:05 | http://arxiv.org/abs/2310.07511v1 | http://arxiv.org/pdf/2310.07511v1 | 2310.07511v1 |
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation | Given a real-world dataset, data condensation (DC) aims to synthesize a
significantly smaller dataset that captures the knowledge of this dataset for
model training with high performance. Recent works propose to enhance DC with
data parameterization, which condenses data into parameterized data containers
rather than pixel space. The intuition behind data parameterization is to
encode shared features of images to avoid additional storage costs. In this
paper, we recognize that images share common features in a hierarchical way due
to the inherent hierarchical structure of the classification system, which is
overlooked by current data parameterization methods. To better align DC with
this hierarchical nature and encourage more efficient information sharing
inside data containers, we propose a novel data parameterization architecture,
Hierarchical Memory Network (HMN). HMN stores condensed data in a three-tier
structure, representing the dataset-level, class-level, and instance-level
features. Another helpful property of the hierarchical architecture is that HMN
naturally ensures good independence among images despite achieving information
sharing. This enables instance-level pruning for HMN to reduce redundant
information, thereby further minimizing redundancy and enhancing performance.
We evaluate HMN on four public datasets (SVHN, CIFAR10, CIFAR100, and
Tiny-ImageNet) and compare HMN with eight DC baselines. The evaluation results
show that our proposed method outperforms all baselines, even when trained with
a batch-based loss consuming less GPU memory. | [
"Haizhong Zheng",
"Jiachen Sun",
"Shutong Wu",
"Bhavya Kailkhura",
"Zhuoqing Mao",
"Chaowei Xiao",
"Atul Prakash"
] | 2023-10-11 14:02:11 | http://arxiv.org/abs/2310.07506v1 | http://arxiv.org/pdf/2310.07506v1 | 2310.07506v1 |
Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT Sensing | In the domain of Federated Learning (FL) systems, recent cutting-edge methods
heavily rely on ideal conditions convergence analysis. Specifically, these
approaches assume that the training datasets on IoT devices possess similar
attributes to the global data distribution. However, this approach fails to
capture the full spectrum of data characteristics in real-time sensing FL
systems. In order to overcome this limitation, we suggest a new approach system
specifically designed for IoT networks with real-time sensing capabilities. Our
approach takes into account the generalization gap due to the user's data
sampling process. By effectively controlling this sampling process, we can
mitigate the overfitting issue and improve overall accuracy. In particular, We
first formulate an optimization problem that harnesses the sampling process to
concurrently reduce overfitting while maximizing accuracy. In pursuit of this
objective, our surrogate optimization problem is adept at handling energy
efficiency while optimizing the accuracy with high generalization. To solve the
optimization problem with high complexity, we introduce an online reinforcement
learning algorithm, named Sample-driven Control for Federated Learning (SCFL)
built on the Soft Actor-Critic (A2C) framework. This enables the agent to
dynamically adapt and find the global optima even in changing environments. By
leveraging the capabilities of SCFL, our system offers a promising solution for
resource allocation in FL systems with real-time sensing capabilities. | [
"Minh Ngoc Luu",
"Minh-Duong Nguyen",
"Ebrahim Bedeer",
"Van Duc Nguyen",
"Dinh Thai Hoang",
"Diep N. Nguyen",
"Quoc-Viet Pham"
] | 2023-10-11 13:50:28 | http://arxiv.org/abs/2310.07497v1 | http://arxiv.org/pdf/2310.07497v1 | 2310.07497v1 |
Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance | Through Ecological Momentary Assessment (EMA) studies, a number of
time-series data is collected across multiple individuals, continuously
monitoring various items of emotional behavior. Such complex data is commonly
analyzed in an individual level, using personalized models. However, it is
believed that additional information of similar individuals is likely to
enhance these models leading to better individuals' description. Thus,
clustering is investigated with an aim to group together the most similar
individuals, and subsequently use this information in group-based models in
order to improve individuals' predictive performance. More specifically, two
model-based clustering approaches are examined, where the first is using
model-extracted parameters of personalized models, whereas the second is
optimized on the model-based forecasting performance. Both methods are then
analyzed using intrinsic clustering evaluation measures (e.g. Silhouette
coefficients) as well as the performance of a downstream forecasting scheme,
where each forecasting group-model is devoted to describe all individuals
belonging to one cluster. Among these, clustering based on performance shows
the best results, in terms of all examined evaluation measures. As another
level of evaluation, those group-models' performance is compared to three
baseline scenarios, the personalized, the all-in-one group and the random
group-based concept. According to this comparison, the superiority of
clustering-based methods is again confirmed, indicating that the utilization of
group-based information could be effectively enhance the overall performance of
all individuals' data. | [
"Mandani Ntekouli",
"Gerasimos Spanakis",
"Lourens Waldorp",
"Anne Roefs"
] | 2023-10-11 13:39:04 | http://arxiv.org/abs/2310.07491v1 | http://arxiv.org/pdf/2310.07491v1 | 2310.07491v1 |
KwaiYiiMath: Technical Report | Recent advancements in large language models (LLMs) have demonstrated
remarkable abilities in handling a variety of natural language processing (NLP)
downstream tasks, even on mathematical tasks requiring multi-step reasoning. In
this report, we introduce the KwaiYiiMath which enhances the mathematical
reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT)
and Reinforced Learning from Human Feedback (RLHF), including on both English
and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale
Chinese primary school mathematics test set (named KMath), consisting of 188
examples to evaluate the correctness of the problem-solving process generated
by the models. Empirical studies demonstrate that KwaiYiiMath can achieve
state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with
the similar size models, respectively. | [
"Jiayi Fu",
"Lei Lin",
"Xiaoyang Gao",
"Pengli Liu",
"Zhengzong Chen",
"Zhirui Yang",
"Shengnan Zhang",
"Xue Zheng",
"Yan Li",
"Yuliang Liu",
"Xucheng Ye",
"Yiqiao Liao",
"Chao Liao",
"Bin Chen",
"Chengru Song",
"Junchen Wan",
"Zijia Lin",
"Fuzheng Zhang",
"Zhongyuan Wang",
"Di Zhang",
"Kun Gai"
] | 2023-10-11 13:35:05 | http://arxiv.org/abs/2310.07488v2 | http://arxiv.org/pdf/2310.07488v2 | 2310.07488v2 |
Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes | This work focuses on the conservation of quantities such as Hamiltonians,
mass, and momentum when solution fields of partial differential equations are
approximated with nonlinear parametrizations such as deep networks. The
proposed approach builds on Neural Galerkin schemes that are based on the
Dirac--Frenkel variational principle to train nonlinear parametrizations
sequentially in time. We first show that only adding constraints that aim to
conserve quantities in continuous time can be insufficient because the
nonlinear dependence on the parameters implies that even quantities that are
linear in the solution fields become nonlinear in the parameters and thus are
challenging to discretize in time. Instead, we propose Neural Galerkin schemes
that compute at each time step an explicit embedding onto the manifold of
nonlinearly parametrized solution fields to guarantee conservation of
quantities. The embeddings can be combined with standard explicit and implicit
time integration schemes. Numerical experiments demonstrate that the proposed
approach conserves quantities up to machine precision. | [
"Paul Schwerdtner",
"Philipp Schulze",
"Jules Berman",
"Benjamin Peherstorfer"
] | 2023-10-11 13:32:04 | http://arxiv.org/abs/2310.07485v1 | http://arxiv.org/pdf/2310.07485v1 | 2310.07485v1 |
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