title
stringlengths 9
208
| abstract
stringlengths 280
2.36k
| authors
sequence | published
stringlengths 19
19
| url
stringlengths 33
33
| pdf_url
stringlengths 33
33
| arxiv_id
stringlengths 12
12
|
|---|---|---|---|---|---|---|
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning | Advance Persistent Threats (APTs), adopted by most delicate attackers, are
becoming increasing common and pose great threat to various enterprises and
institutions. Data provenance analysis on provenance graphs has emerged as a
common approach in APT detection. However, previous works have exhibited
several shortcomings: (1) requiring attack-containing data and a priori
knowledge of APTs, (2) failing in extracting the rich contextual information
buried within provenance graphs and (3) becoming impracticable due to their
prohibitive computation overhead and memory consumption.
In this paper, we introduce MAGIC, a novel and flexible self-supervised APT
detection approach capable of performing multi-granularity detection under
different level of supervision. MAGIC leverages masked graph representation
learning to model benign system entities and behaviors, performing efficient
deep feature extraction and structure abstraction on provenance graphs. By
ferreting out anomalous system behaviors via outlier detection methods, MAGIC
is able to perform both system entity level and batched log level APT
detection. MAGIC is specially designed to handle concept drift with a model
adaption mechanism and successfully applies to universal conditions and
detection scenarios. We evaluate MAGIC on three widely-used datasets, including
both real-world and simulated attacks. Evaluation results indicate that MAGIC
achieves promising detection results in all scenarios and shows enormous
advantage over state-of-the-art APT detection approaches in performance
overhead. | [
"Zian Jia",
"Yun Xiong",
"Yuhong Nan",
"Yao Zhang",
"Jinjing Zhao",
"Mi Wen"
] | 2023-10-15 13:27:06 | http://arxiv.org/abs/2310.09831v1 | http://arxiv.org/pdf/2310.09831v1 | 2310.09831v1 |
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning | Vertical Federated Learning (VFL) has emerged as a collaborative training
paradigm that allows participants with different features of the same group of
users to accomplish cooperative training without exposing their raw data or
model parameters. VFL has gained significant attention for its research
potential and real-world applications in recent years, but still faces
substantial challenges, such as in defending various kinds of data inference
and backdoor attacks. Moreover, most of existing VFL projects are
industry-facing and not easily used for keeping track of the current research
progress. To address this need, we present an extensible and lightweight VFL
framework VFLAIR (available at https://github.com/FLAIR-THU/VFLAIR), which
supports VFL training with a variety of models, datasets and protocols, along
with standardized modules for comprehensive evaluations of attacks and defense
strategies. We also benchmark 11 attacks and 8 defenses performance under
different communication and model partition settings and draw concrete insights
and recommendations on the choice of defense strategies for different practical
VFL deployment scenario. | [
"Tianyuan Zou",
"Zixuan Gu",
"Yu He",
"Hideaki Takahashi",
"Yang Liu",
"Guangnan Ye",
"Ya-Qin Zhang"
] | 2023-10-15 13:18:31 | http://arxiv.org/abs/2310.09827v1 | http://arxiv.org/pdf/2310.09827v1 | 2310.09827v1 |
Optimizing K-means for Big Data: A Comparative Study | This paper presents a comparative analysis of different optimization
techniques for the K-means algorithm in the context of big data. K-means is a
widely used clustering algorithm, but it can suffer from scalability issues
when dealing with large datasets. The paper explores different approaches to
overcome these issues, including parallelization, approximation, and sampling
methods. The authors evaluate the performance of these techniques on various
benchmark datasets and compare them in terms of speed, quality of clustering,
and scalability according to the LIMA dominance criterion. The results show
that different techniques are more suitable for different types of datasets and
provide insights into the trade-offs between speed and accuracy in K-means
clustering for big data. Overall, the paper offers a comprehensive guide for
practitioners and researchers on how to optimize K-means for big data
applications. | [
"Ravil Mussabayev",
"Rustam Mussabayev"
] | 2023-10-15 12:35:27 | http://arxiv.org/abs/2310.09819v1 | http://arxiv.org/pdf/2310.09819v1 | 2310.09819v1 |
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates | Byzantine robustness is an essential feature of algorithms for certain
distributed optimization problems, typically encountered in
collaborative/federated learning. These problems are usually huge-scale,
implying that communication compression is also imperative for their
resolution. These factors have spurred recent algorithmic and theoretical
developments in the literature of Byzantine-robust learning with compression.
In this paper, we contribute to this research area in two main directions.
First, we propose a new Byzantine-robust method with compression --
Byz-DASHA-PAGE -- and prove that the new method has better convergence rate
(for non-convex and Polyak-Lojasiewicz smooth optimization problems), smaller
neighborhood size in the heterogeneous case, and tolerates more Byzantine
workers under over-parametrization than the previous method with SOTA
theoretical convergence guarantees (Byz-VR-MARINA). Secondly, we develop the
first Byzantine-robust method with communication compression and error feedback
-- Byz-EF21 -- along with its bidirectional compression version -- Byz-EF21-BC
-- and derive the convergence rates for these methods for non-convex and
Polyak-Lojasiewicz smooth case. We test the proposed methods and illustrate our
theoretical findings in the numerical experiments. | [
"Ahmad Rammal",
"Kaja Gruntkowska",
"Nikita Fedin",
"Eduard Gorbunov",
"Peter Richtárik"
] | 2023-10-15 11:22:34 | http://arxiv.org/abs/2310.09804v1 | http://arxiv.org/pdf/2310.09804v1 | 2310.09804v1 |
Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networks | Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural
Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have
made remarkable progress in many physical scenarios, especially in
communication applications. Despite achieving great success, the privacy issue
of such models has also received considerable attention. Previous studies have
shown that given a well-fitted target GNN, the attacker can reconstruct the
sensitive training graph of this model via model inversion attacks, leading to
significant privacy worries for the AI service provider. We advocate that the
vulnerability comes from the target GNN itself and the prior knowledge about
the shared properties in real-world graphs. Inspired by this, we propose a
novel model inversion attack method on HomoGNNs and HeteGNNs, namely HomoGMI
and HeteGMI. Specifically, HomoGMI and HeteGMI are gradient-descent-based
optimization methods that aim to maximize the cross-entropy loss on the target
GNN and the $1^{st}$ and $2^{nd}$-order proximities on the reconstructed graph.
Notably, to the best of our knowledge, HeteGMI is the first attempt to perform
model inversion attacks on HeteGNNs. Extensive experiments on multiple
benchmarks demonstrate that the proposed method can achieve better performance
than the competitors. | [
"Renyang Liu",
"Wei Zhou",
"Jinhong Zhang",
"Xiaoyuan Liu",
"Peiyuan Si",
"Haoran Li"
] | 2023-10-15 11:16:14 | http://arxiv.org/abs/2310.09800v1 | http://arxiv.org/pdf/2310.09800v1 | 2310.09800v1 |
FLrce: Efficient Federated Learning with Relationship-based Client Selection and Early-Stopping Strategy | Federated learning (FL) achieves great popularity in broad areas as a
powerful interface to offer intelligent services to customers while maintaining
data privacy. Nevertheless, FL faces communication and computation bottlenecks
due to limited bandwidth and resource constraints of edge devices. To
comprehensively address the bottlenecks, the technique of dropout is
introduced, where resource-constrained edge devices are allowed to
collaboratively train a subset of the global model parameters. However, dropout
impedes the learning efficiency of FL under unbalanced local data
distributions. As a result, FL requires more rounds to achieve appropriate
accuracy, consuming more communication and computation resources. In this
paper, we present FLrce, an efficient FL framework with a relationship-based
client selection and early-stopping strategy. FLrce accelerates the FL process
by selecting clients with more significant effects, enabling the global model
to converge to a high accuracy in fewer rounds. FLrce also leverages an early
stopping mechanism to terminate FL in advance to save communication and
computation resources. Experiment results show that FLrce increases the
communication and computation efficiency by 6% to 73.9% and 20% to 79.5%,
respectively, while maintaining competitive accuracy. | [
"Ziru Niu",
"Hai Dong",
"A. Kai Qin",
"Tao Gu"
] | 2023-10-15 10:13:44 | http://arxiv.org/abs/2310.09789v1 | http://arxiv.org/pdf/2310.09789v1 | 2310.09789v1 |
Dynamic Link Prediction for New Nodes in Temporal Graph Networks | Modelling temporal networks for dynamic link prediction of new nodes has many
real-world applications, such as providing relevant item recommendations to new
customers in recommender systems and suggesting appropriate posts to new users
on social platforms. Unlike old nodes, new nodes have few historical links,
which poses a challenge for the dynamic link prediction task. Most existing
dynamic models treat all nodes equally and are not specialized for new nodes,
resulting in suboptimal performances. In this paper, we consider dynamic link
prediction of new nodes as a few-shot problem and propose a novel model based
on the meta-learning principle to effectively mitigate this problem.
Specifically, we develop a temporal encoder with a node-level span memory to
obtain a new node embedding, and then we use a predictor to determine whether
the new node generates a link. To overcome the few-shot challenge, we
incorporate the encoder-predictor into the meta-learning paradigm, which can
learn two types of implicit information during the formation of the temporal
network through span adaptation and node adaptation. The acquired implicit
information can serve as model initialisation and facilitate rapid adaptation
to new nodes through a fine-tuning process on just a few links. Experiments on
three publicly available datasets demonstrate the superior performance of our
model compared to existing state-of-the-art methods. | [
"Xiaobo Zhu",
"Yan Wu",
"Qinhu Zhang",
"Zhanheng Chen",
"Ying He"
] | 2023-10-15 09:54:18 | http://arxiv.org/abs/2310.09787v1 | http://arxiv.org/pdf/2310.09787v1 | 2310.09787v1 |
Notes on Applicability of Explainable AI Methods to Machine Learning Models Using Features Extracted by Persistent Homology | Data analysis that uses the output of topological data analysis as input for
machine learning algorithms has been the subject of extensive research. This
approach offers a means of capturing the global structure of data. Persistent
homology (PH), a common methodology within the field of TDA, has found
wide-ranging applications in machine learning. One of the key reasons for the
success of the PH-ML pipeline lies in the deterministic nature of feature
extraction conducted through PH. The ability to achieve satisfactory levels of
accuracy with relatively simple downstream machine learning models, when
processing these extracted features, underlines the pipeline's superior
interpretability. However, it must be noted that this interpretation has
encountered issues. Specifically, it fails to accurately reflect the feasible
parameter region in the data generation process, and the physical or chemical
constraints that restrict this process. Against this backdrop, we explore the
potential application of explainable AI methodologies to this PH-ML pipeline.
We apply this approach to the specific problem of predicting gas adsorption in
metal-organic frameworks and demonstrate that it can yield suggestive results.
The codes to reproduce our results are available at
https://github.com/naofumihama/xai_ph_ml | [
"Naofumi Hama"
] | 2023-10-15 08:56:15 | http://arxiv.org/abs/2310.09780v1 | http://arxiv.org/pdf/2310.09780v1 | 2310.09780v1 |
Pseudo-Bayesian Optimization | Bayesian Optimization is a popular approach for optimizing expensive
black-box functions. Its key idea is to use a surrogate model to approximate
the objective and, importantly, quantify the associated uncertainty that allows
a sequential search of query points that balance exploitation-exploration.
Gaussian process (GP) has been a primary candidate for the surrogate model,
thanks to its Bayesian-principled uncertainty quantification power and modeling
flexibility. However, its challenges have also spurred an array of alternatives
whose convergence properties could be more opaque. Motivated by these, we study
in this paper an axiomatic framework that elicits the minimal requirements to
guarantee black-box optimization convergence that could apply beyond GP-related
methods. Moreover, we leverage the design freedom in our framework, which we
call Pseudo-Bayesian Optimization, to construct empirically superior
algorithms. In particular, we show how using simple local regression, and a
suitable "randomized prior" construction to quantify uncertainty, not only
guarantees convergence but also consistently outperforms state-of-the-art
benchmarks in examples ranging from high-dimensional synthetic experiments to
realistic hyperparameter tuning and robotic applications. | [
"Haoxian Chen",
"Henry Lam"
] | 2023-10-15 07:55:28 | http://arxiv.org/abs/2310.09766v1 | http://arxiv.org/pdf/2310.09766v1 | 2310.09766v1 |
DropMix: Better Graph Contrastive Learning with Harder Negative Samples | While generating better negative samples for contrastive learning has been
widely studied in the areas of CV and NLP, very few work has focused on
graph-structured data. Recently, Mixup has been introduced to synthesize hard
negative samples in graph contrastive learning (GCL). However, due to the
unsupervised learning nature of GCL, without the help of soft labels, directly
mixing representations of samples could inadvertently lead to the information
loss of the original hard negative and further adversely affect the quality of
the newly generated harder negative. To address the problem, in this paper, we
propose a novel method DropMix to synthesize harder negative samples, which
consists of two main steps. Specifically, we first select some hard negative
samples by measuring their hardness from both local and global views in the
graph simultaneously. After that, we mix hard negatives only on partial
representation dimensions to generate harder ones and decrease the information
loss caused by Mixup. We conduct extensive experiments to verify the
effectiveness of DropMix on six benchmark datasets. Our results show that our
method can lead to better GCL performance. Our data and codes are publicly
available at https://github.com/Mayueq/DropMix-Code. | [
"Yueqi Ma",
"Minjie Chen",
"Xiang Li"
] | 2023-10-15 07:45:30 | http://arxiv.org/abs/2310.09764v1 | http://arxiv.org/pdf/2310.09764v1 | 2310.09764v1 |
When can transformers reason with abstract symbols? | We investigate the capabilities of transformer large language models (LLMs)
on relational reasoning tasks involving abstract symbols. Such tasks have long
been studied in the neuroscience literature as fundamental building blocks for
more complex abilities in programming, mathematics, and verbal reasoning. For
(i) regression tasks, we prove that transformers generalize when trained, but
require astonishingly large quantities of training data. For (ii)
next-token-prediction tasks with symbolic labels, we show an "inverse scaling
law": transformers fail to generalize as their embedding dimension increases.
For both settings (i) and (ii), we propose subtle transformer modifications
which can reduce the amount of data needed by adding two trainable parameters
per head. | [
"Enric Boix-Adsera",
"Omid Saremi",
"Emmanuel Abbe",
"Samy Bengio",
"Etai Littwin",
"Joshua Susskind"
] | 2023-10-15 06:45:38 | http://arxiv.org/abs/2310.09753v1 | http://arxiv.org/pdf/2310.09753v1 | 2310.09753v1 |
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting | Multivariate time series forecasting plays a pivotal role in contemporary web
technologies. In contrast to conventional methods that involve creating
dedicated models for specific time series application domains, this research
advocates for a unified model paradigm that transcends domain boundaries.
However, learning an effective cross-domain model presents the following
challenges. First, various domains exhibit disparities in data characteristics,
e.g., the number of variables, posing hurdles for existing models that impose
inflexible constraints on these factors. Second, the model may encounter
difficulties in distinguishing data from various domains, leading to suboptimal
performance in our assessments. Third, the diverse convergence rates of time
series domains can also result in compromised empirical performance. To address
these issues, we propose UniTime for effective cross-domain time series
learning. Concretely, UniTime can flexibly adapt to data with varying
characteristics. It also uses domain instructions and a Language-TS Transformer
to offer identification information and align two modalities. In addition,
UniTime employs masking to alleviate domain convergence speed imbalance issues.
Our extensive experiments demonstrate the effectiveness of UniTime in advancing
state-of-the-art forecasting performance and zero-shot transferability. | [
"Xu Liu",
"Junfeng Hu",
"Yuan Li",
"Shizhe Diao",
"Yuxuan Liang",
"Bryan Hooi",
"Roger Zimmermann"
] | 2023-10-15 06:30:22 | http://arxiv.org/abs/2310.09751v1 | http://arxiv.org/pdf/2310.09751v1 | 2310.09751v1 |
Private Synthetic Data Meets Ensemble Learning | When machine learning models are trained on synthetic data and then deployed
on real data, there is often a performance drop due to the distribution shift
between synthetic and real data. In this paper, we introduce a new ensemble
strategy for training downstream models, with the goal of enhancing their
performance when used on real data. We generate multiple synthetic datasets by
applying a differential privacy (DP) mechanism several times in parallel and
then ensemble the downstream models trained on these datasets. While each
synthetic dataset might deviate more from the real data distribution, they
collectively increase sample diversity. This may enhance the robustness of
downstream models against distribution shifts. Our extensive experiments reveal
that while ensembling does not enhance downstream performance (compared with
training a single model) for models trained on synthetic data generated by
marginal-based or workload-based DP mechanisms, our proposed ensemble strategy
does improve the performance for models trained using GAN-based DP mechanisms
in terms of both accuracy and calibration of downstream models. | [
"Haoyuan Sun",
"Navid Azizan",
"Akash Srivastava",
"Hao Wang"
] | 2023-10-15 04:24:42 | http://arxiv.org/abs/2310.09729v1 | http://arxiv.org/pdf/2310.09729v1 | 2310.09729v1 |
SVM based Multiclass Classifier for Gait phase Classification using Shank IMU Sensor | In this study, a gait phase classification method based on SVM multiclass
classification is introduced, with a focus on the precise identification of the
stance and swing phases, which are further subdivided into seven phases. Data
from individual IMU sensors, such as Shank Acceleration X, Y, Z, Shank Gyro X,
and Knee Angles, are used as features in this classification model. The
suggested technique successfully classifies the various gait phases with a
significant accuracy of about 90.3%. Gait phase classification is crucial,
especially in the domains of exoskeletons and prosthetics, where accurate
identification of gait phases enables seamless integration with assistive
equipment, improving mobility, stability, and energy economy. This study
extends the study of gait and offers an effective method for correctly
identifying gait phases from Shank IMU sensor data, with potential applications
in biomechanical research, exoskeletons, rehabilitation, and prosthetics. | [
"Aswadh Khumar G S",
"Barath Kumar JK"
] | 2023-10-15 04:23:08 | http://arxiv.org/abs/2310.09728v1 | http://arxiv.org/pdf/2310.09728v1 | 2310.09728v1 |
Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games | This work studies an independent natural policy gradient (NPG) algorithm for
the multi-agent reinforcement learning problem in Markov potential games. It is
shown that, under mild technical assumptions and the introduction of the
suboptimality gap, the independent NPG method with an oracle providing exact
policy evaluation asymptotically reaches an $\epsilon$-Nash Equilibrium (NE)
within $\mathcal{O}(1/\epsilon)$ iterations. This improves upon the previous
best result of $\mathcal{O}(1/\epsilon^2)$ iterations and is of the same order,
$\mathcal{O}(1/\epsilon)$, that is achievable for the single-agent case.
Empirical results for a synthetic potential game and a congestion game are
presented to verify the theoretical bounds. | [
"Youbang Sun",
"Tao Liu",
"Ruida Zhou",
"P. R. Kumar",
"Shahin Shahrampour"
] | 2023-10-15 04:10:44 | http://arxiv.org/abs/2310.09727v1 | http://arxiv.org/pdf/2310.09727v1 | 2310.09727v1 |
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings | In this paper, we propose a hierarchical contrastive learning framework,
HiCL, which considers local segment-level and global sequence-level
relationships to improve training efficiency and effectiveness. Traditional
methods typically encode a sequence in its entirety for contrast with others,
often neglecting local representation learning, leading to challenges in
generalizing to shorter texts. Conversely, HiCL improves its effectiveness by
dividing the sequence into several segments and employing both local and global
contrastive learning to model segment-level and sequence-level relationships.
Further, considering the quadratic time complexity of transformers over input
tokens, HiCL boosts training efficiency by first encoding short segments and
then aggregating them to obtain the sequence representation. Extensive
experiments show that HiCL enhances the prior top-performing SNCSE model across
seven extensively evaluated STS tasks, with an average increase of +0.2%
observed on BERT-large and +0.44% on RoBERTa-large. | [
"Zhuofeng Wu",
"Chaowei Xiao",
"VG Vinod Vydiswaran"
] | 2023-10-15 03:14:33 | http://arxiv.org/abs/2310.09720v1 | http://arxiv.org/pdf/2310.09720v1 | 2310.09720v1 |
Efficient and Effective Multi-View Subspace Clustering for Large-scale Data | Recent multi-view subspace clustering achieves impressive results utilizing
deep networks, where the self-expressive correlation is typically modeled by a
fully connected (FC) layer. However, they still suffer from two limitations: i)
it is under-explored to extract a unified representation from multiple views
that simultaneously satisfy minimal sufficiency and discriminability. ii) the
parameter scale of the FC layer is quadratic to the number of samples,
resulting in high time and memory costs that significantly degrade their
feasibility in large-scale datasets. In light of this, we propose a novel deep
framework termed Efficient and Effective Large-scale Multi-View Subspace
Clustering (E$^2$LMVSC). Specifically, to enhance the quality of the unified
representation, a soft clustering assignment similarity constraint is devised
for explicitly decoupling consistent, complementary, and superfluous
information across multi-view data. Then, following information bottleneck
theory, a sufficient yet minimal unified feature representation is obtained.
Moreover, E$^2$LMVSC employs the maximal coding rate reduction principle to
promote intra-cluster aggregation and inter-cluster separability within the
unified representation. Finally, the self-expressive coefficients are learned
by a Relation-Metric Net instead of a parameterized FC layer for greater
efficiency. Extensive experiments show that E$^2$LMVSC yields comparable
results to existing methods and achieves state-of-the-art clustering
performance in large-scale multi-view datasets. | [
"Yuxiu Lin",
"Hui Liu",
"Ren Wang",
"Gongguan Chen",
"Caiming Zhang"
] | 2023-10-15 03:08:25 | http://arxiv.org/abs/2310.09718v1 | http://arxiv.org/pdf/2310.09718v1 | 2310.09718v1 |
New Advances in Body Composition Assessment with ShapedNet: A Single Image Deep Regression Approach | We introduce a novel technique called ShapedNet to enhance body composition
assessment. This method employs a deep neural network capable of estimating
Body Fat Percentage (BFP), performing individual identification, and enabling
localization using a single photograph. The accuracy of ShapedNet is validated
through comprehensive comparisons against the gold standard method, Dual-Energy
X-ray Absorptiometry (DXA), utilizing 1273 healthy adults spanning various
ages, sexes, and BFP levels. The results demonstrate that ShapedNet outperforms
in 19.5% state of the art computer vision-based approaches for body fat
estimation, achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and Mean
Absolute Error (MAE) of 1.42. The study evaluates both gender-based and
Gender-neutral approaches, with the latter showcasing superior performance. The
method estimates BFP with 95% confidence within an error margin of 4.01% to
5.81%. This research advances multi-task learning and body composition
assessment theory through ShapedNet. | [
"Navar Medeiros M. Nascimento",
"Pedro Cavalcante de Sousa Junior",
"Pedro Yuri Rodrigues Nunes",
"Suane Pires Pinheiro da Silva",
"Luiz Lannes Loureiro",
"Victor Zaban Bittencourt",
"Valden Luis Matos Capistrano Junior",
"Pedro Pedrosa Rebouças Filho"
] | 2023-10-15 02:30:27 | http://arxiv.org/abs/2310.09709v1 | http://arxiv.org/pdf/2310.09709v1 | 2310.09709v1 |
SGA: A Graph Augmentation Method for Signed Graph Neural Networks | Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns
in real-world signed graphs containing positive and negative links. However,
three key challenges hinder current SGNN-based signed graph representation
learning: sparsity in signed graphs leaves latent structures undiscovered,
unbalanced triangles pose representation difficulties for SGNN models, and
real-world signed graph datasets often lack supplementary information like node
labels and features. These constraints limit the potential of SGNN-based
representation learning. We address these issues with data augmentation
techniques. Despite many graph data augmentation methods existing for unsigned
graphs, none are tailored for signed graphs. Our paper introduces the novel
Signed Graph Augmentation framework (SGA), comprising three main components.
First, we employ the SGNN model to encode the signed graph, extracting latent
structural information for candidate augmentation structures. Second, we
evaluate these candidate samples (edges) and select the most beneficial ones
for modifying the original training set. Third, we propose a novel augmentation
perspective that assigns varying training difficulty to training samples,
enabling the design of a new training strategy. Extensive experiments on six
real-world datasets (Bitcoin-alpha, Bitcoin-otc, Epinions, Slashdot, Wiki-elec,
and Wiki-RfA) demonstrate that SGA significantly improves performance across
multiple benchmarks. Our method outperforms baselines by up to 22.2% in AUC for
SGCN on Wiki-RfA, 33.3% in F1-binary, 48.8% in F1-micro, and 36.3% in F1-macro
for GAT on Bitcoin-alpha in link sign prediction. | [
"Zeyu Zhang",
"Shuyan Wan",
"Sijie Wang",
"Xianda Zheng",
"Xinrui Zhang",
"Kaiqi Zhao",
"Jiamou Liu",
"Dong Hao"
] | 2023-10-15 02:19:07 | http://arxiv.org/abs/2310.09705v1 | http://arxiv.org/pdf/2310.09705v1 | 2310.09705v1 |
Spike-based Neuromorphic Computing for Next-Generation Computer Vision | Neuromorphic Computing promises orders of magnitude improvement in energy
efficiency compared to traditional von Neumann computing paradigm. The goal is
to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy
intelligent system by learning and emulating brain functionality which can be
realized through innovation in different abstraction layers including material,
device, circuit, architecture and algorithm. As the energy consumption in
complex vision tasks keep increasing exponentially due to larger data set and
resource-constrained edge devices become increasingly ubiquitous, spike-based
neuromorphic computing approaches can be viable alternative to deep
convolutional neural network that is dominating the vision field today. In this
book chapter, we introduce neuromorphic computing, outline a few representative
examples from different layers of the design stack (devices, circuits and
algorithms) and conclude with a few exciting applications and future research
directions that seem promising for computer vision in the near future. | [
"Md Sakib Hasan",
"Catherine D. Schuman",
"Zhongyang Zhang",
"Tauhidur Rahman",
"Garrett S. Rose"
] | 2023-10-15 01:05:35 | http://arxiv.org/abs/2310.09692v1 | http://arxiv.org/pdf/2310.09692v1 | 2310.09692v1 |
When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations | We study the fairness of dimensionality reduction methods for
recommendations. We focus on the established method of principal component
analysis (PCA), which identifies latent components and produces a low-rank
approximation via the leading components while discarding the trailing
components. Prior works have defined notions of "fair PCA"; however, these
definitions do not answer the following question: what makes PCA unfair? We
identify two underlying mechanisms of PCA that induce unfairness at the item
level. The first negatively impacts less popular items, due to the fact that
less popular items rely on trailing latent components to recover their values.
The second negatively impacts the highly popular items, since the leading PCA
components specialize in individual popular items instead of capturing
similarities between items. To address these issues, we develop a
polynomial-time algorithm, Item-Weighted PCA, a modification of PCA that uses
item-specific weights in the objective. On a stylized class of matrices, we
prove that Item-Weighted PCA using a specific set of weights minimizes a
popularity-normalized error metric. Our evaluations on real-world datasets show
that Item-Weighted PCA not only improves overall recommendation quality by up
to $0.1$ item-level AUC-ROC but also improves on both popular and less popular
items. | [
"David Liu",
"Jackie Baek",
"Tina Eliassi-Rad"
] | 2023-10-15 00:22:12 | http://arxiv.org/abs/2310.09687v1 | http://arxiv.org/pdf/2310.09687v1 | 2310.09687v1 |
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems | Column generation (CG) is a vital method to solve large-scale problems by
dynamically generating variables. It has extensive applications in common
combinatorial optimization, such as vehicle routing and scheduling problems,
where each iteration step requires solving an NP-hard constrained shortest path
problem. Although some heuristic methods for acceleration already exist, they
are not versatile enough to solve different problems. In this work, we propose
a reinforcement learning-based hyper-heuristic framework, dubbed RLHH, to
enhance the performance of CG. RLHH is a selection module embedded in CG to
accelerate convergence and get better integer solutions. In each CG iteration,
the RL agent selects a low-level heuristic to construct a reduced network only
containing the edges with a greater chance of being part of the optimal
solution. In addition, we specify RLHH to solve two typical combinatorial
optimization problems: Vehicle Routing Problem with Time Windows (VRPTW) and
Bus Driver Scheduling Problem (BDSP). The total cost can be reduced by up to
27.9\% in VRPTW and 15.4\% in BDSP compared to the best lower-level heuristic
in our tested scenarios, within equivalent or even less computational time. The
proposed RLHH is the first RL-based CG method that outperforms traditional
approaches in terms of solution quality, which can promote the application of
CG in combinatorial optimization. | [
"Kuan Xu",
"Li Shen",
"Lindong Liu"
] | 2023-10-15 00:05:50 | http://arxiv.org/abs/2310.09686v1 | http://arxiv.org/pdf/2310.09686v1 | 2310.09686v1 |
Generative artificial intelligence for de novo protein design | Engineering new molecules with desirable functions and properties has the
potential to extend our ability to engineer proteins beyond what nature has so
far evolved. Advances in the so-called "de novo" design problem have recently
been brought forward by developments in artificial intelligence. Generative
architectures, such as language models and diffusion processes, seem adept at
generating novel, yet realistic proteins that display desirable properties and
perform specified functions. State-of-the-art design protocols now achieve
experimental success rates nearing 20%, thus widening the access to de novo
designed proteins. Despite extensive progress, there are clear field-wide
challenges, for example in determining the best in silico metrics to prioritise
designs for experimental testing, and in designing proteins that can undergo
large conformational changes or be regulated by post-translational
modifications and other cellular processes. With an increase in the number of
models being developed, this review provides a framework to understand how
these tools fit into the overall process of de novo protein design. Throughout,
we highlight the power of incorporating biochemical knowledge to improve
performance and interpretability. | [
"Adam Winnifrith",
"Carlos Outeiral",
"Brian Hie"
] | 2023-10-15 00:02:22 | http://arxiv.org/abs/2310.09685v1 | http://arxiv.org/pdf/2310.09685v1 | 2310.09685v1 |
Efficient Model-Agnostic Multi-Group Equivariant Networks | Constructing model-agnostic group equivariant networks, such as equitune
(Basu et al., 2023b) and its generalizations (Kim et al., 2023), can be
computationally expensive for large product groups. We address this by
providing efficient model-agnostic equivariant designs for two related
problems: one where the network has multiple inputs each with potentially
different groups acting on them, and another where there is a single input but
the group acting on it is a large product group. For the first design, we
initially consider a linear model and characterize the entire equivariant space
that satisfies this constraint. This characterization gives rise to a novel
fusion layer between different channels that satisfies an invariance-symmetry
(IS) constraint, which we call an IS layer. We then extend this design beyond
linear models, similar to equitune, consisting of equivariant and IS layers. We
also show that the IS layer is a universal approximator of invariant-symmetric
functions. Inspired by the first design, we use the notion of the IS property
to design a second efficient model-agnostic equivariant design for large
product groups acting on a single input. For the first design, we provide
experiments on multi-image classification where each view is transformed
independently with transformations such as rotations. We find equivariant
models are robust to such transformations and perform competitively otherwise.
For the second design, we consider three applications: language
compositionality on the SCAN dataset to product groups; fairness in natural
language generation from GPT-2 to address intersectionality; and robust
zero-shot image classification with CLIP. Overall, our methods are simple and
general, competitive with equitune and its variants, while also being
computationally more efficient. | [
"Razan Baltaji",
"Sourya Basu",
"Lav R. Varshney"
] | 2023-10-14 22:24:26 | http://arxiv.org/abs/2310.09675v1 | http://arxiv.org/pdf/2310.09675v1 | 2310.09675v1 |
Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning | Automatic coding of International Classification of Diseases (ICD) is a
multi-label text categorization task that involves extracting disease or
procedure codes from clinical notes. Despite the application of
state-of-the-art natural language processing (NLP) techniques, there are still
challenges including limited availability of data due to privacy constraints
and the high variability of clinical notes caused by different writing habits
of medical professionals and various pathological features of patients. In this
work, we investigate the semi-structured nature of clinical notes and propose
an automatic algorithm to segment them into sections. To address the
variability issues in existing ICD coding models with limited data, we
introduce a contrastive pre-training approach on sections using a soft
multi-label similarity metric based on tree edit distance. Additionally, we
design a masked section training strategy to enable ICD coding models to locate
sections related to ICD codes. Extensive experimental results demonstrate that
our proposed training strategies effectively enhance the performance of
existing ICD coding methods. | [
"Chang Lu",
"Chandan K. Reddy",
"Ping Wang",
"Yue Ning"
] | 2023-10-14 22:07:13 | http://arxiv.org/abs/2310.09672v1 | http://arxiv.org/pdf/2310.09672v1 | 2310.09672v1 |
Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devices | Seismic full waveform inversion (FWI) is a widely used technique in
geophysics for inferring subsurface structures from seismic data. And
InversionNet is one of the most successful data-driven machine learning models
that is applied to seismic FWI. However, the high computing costs to run
InversionNet have made it challenging to be efficiently deployed on edge
devices that are usually resource-constrained. Therefore, we propose to employ
the structured pruning algorithm to get a lightweight version of InversionNet,
which can make an efficient inference on edge devices. And we also made a
prototype with Raspberry Pi to run the lightweight InversionNet. Experimental
results show that the pruned InversionNet can achieve up to 98.2 % reduction in
computing resources with moderate model performance degradation. | [
"Zhepeng Wang",
"Isaacshubhanand Putla",
"Weiwen Jiang",
"Youzuo Lin"
] | 2023-10-14 21:19:15 | http://arxiv.org/abs/2310.09667v2 | http://arxiv.org/pdf/2310.09667v2 | 2310.09667v2 |
A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization | Federated learning (FL) is emerging as a sought-after distributed machine
learning architecture, offering the advantage of model training without direct
exposure of raw data. With advancements in network infrastructure, FL has been
seamlessly integrated into edge computing. However, the limited resources on
edge devices introduce security vulnerabilities to FL in the context. While
blockchain technology promises to bolster security, practical deployment on
resource-constrained edge devices remains a challenge. Moreover, the
exploration of FL with multiple aggregators in edge computing is still new in
the literature. Addressing these gaps, we introduce the Blockchain-empowered
Heterogeneous Multi-Aggregator Federated Learning Architecture (BMA-FL). We
design a novel light-weight Byzantine consensus mechanism, namely PBCM, to
enable secure and fast model aggregation and synchronization in BMA-FL. We also
dive into the heterogeneity problem in BMA-FL that the aggregators are
associated with varied number of connected trainers with Non-IID data
distributions and diverse training speed. We proposed a multi-agent deep
reinforcement learning algorithm to help aggregators decide the best training
strategies. The experiments on real-word datasets demonstrate the efficiency of
BMA-FL to achieve better models faster than baselines, showing the efficacy of
PBCM and proposed deep reinforcement learning algorithm. | [
"Xiao Li",
"Weili Wu"
] | 2023-10-14 20:47:30 | http://arxiv.org/abs/2310.09665v1 | http://arxiv.org/pdf/2310.09665v1 | 2310.09665v1 |
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation | Hypergraphs, with their capacity to depict high-order relationships, have
emerged as a significant extension of traditional graphs. Although Graph Neural
Networks (GNNs) have remarkable performance in graph representation learning,
their extension to hypergraphs encounters challenges due to their intricate
structures. Furthermore, current hypergraph transformers, a special variant of
GNN, utilize semantic feature-based self-attention, ignoring topological
attributes of nodes and hyperedges. To address these challenges, we propose a
Topology-guided Hypergraph Transformer Network (THTN). In this model, we first
formulate a hypergraph from a graph while retaining its structural essence to
learn higher-order relations within the graph. Then, we design a simple yet
effective structural and spatial encoding module to incorporate the topological
and spatial information of the nodes into their representation. Further, we
present a structure-aware self-attention mechanism that discovers the important
nodes and hyperedges from both semantic and structural viewpoints. By
leveraging these two modules, THTN crafts an improved node representation,
capturing both local and global topological expressions. Extensive experiments
conducted on node classification tasks demonstrate that the performance of the
proposed model consistently exceeds that of the existing approaches. | [
"Khaled Mohammed Saifuddin",
"Mehmet Emin Aktas",
"Esra Akbas"
] | 2023-10-14 20:08:54 | http://arxiv.org/abs/2310.09657v1 | http://arxiv.org/pdf/2310.09657v1 | 2310.09657v1 |
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space | Recent advances in tabular data generation have greatly enhanced synthetic
data quality. However, extending diffusion models to tabular data is
challenging due to the intricately varied distributions and a blend of data
types of tabular data. This paper introduces TABSYN, a methodology that
synthesizes tabular data by leveraging a diffusion model within a variational
autoencoder (VAE) crafted latent space. The key advantages of the proposed
TABSYN include (1) Generality: the ability to handle a broad spectrum of data
types by converting them into a single unified space and explicitly capture
inter-column relations; (2) Quality: optimizing the distribution of latent
embeddings to enhance the subsequent training of diffusion models, which helps
generate high-quality synthetic data, (3) Speed: much fewer number of reverse
steps and faster synthesis speed than existing diffusion-based methods.
Extensive experiments on six datasets with five metrics demonstrate that TABSYN
outperforms existing methods. Specifically, it reduces the error rates by 86%
and 67% for column-wise distribution and pair-wise column correlation
estimations compared with the most competitive baselines. | [
"Hengrui Zhang",
"Jiani Zhang",
"Balasubramaniam Srinivasan",
"Zhengyuan Shen",
"Xiao Qin",
"Christos Faloutsos",
"Huzefa Rangwala",
"George Karypis"
] | 2023-10-14 19:59:03 | http://arxiv.org/abs/2310.09656v1 | http://arxiv.org/pdf/2310.09656v1 | 2310.09656v1 |
Lexical Entrainment for Conversational Systems | Conversational agents have become ubiquitous in assisting with daily tasks,
and are expected to possess human-like features. One such feature is lexical
entrainment (LE), a phenomenon in which speakers in human-human conversations
tend to naturally and subconsciously align their lexical choices with those of
their interlocutors, leading to more successful and engaging conversations. As
an example, if a digital assistant replies 'Your appointment for Jinling Noodle
Pub is at 7 pm' to the question 'When is my reservation for Jinling Noodle Bar
today?', it may feel as though the assistant is trying to correct the speaker,
whereas a response of 'Your reservation for Jinling Noodle Bar is at 7 pm'
would likely be perceived as more positive. This highlights the importance of
LE in establishing a shared terminology for maximum clarity and reducing
ambiguity in conversations. However, we demonstrate in this work that current
response generation models do not adequately address this crucial humanlike
phenomenon. To address this, we propose a new dataset, named MULTIWOZ-ENTR, and
a measure for LE for conversational systems. Additionally, we suggest a way to
explicitly integrate LE into conversational systems with two new tasks, a LE
extraction task and a LE generation task. We also present two baseline
approaches for the LE extraction task, which aim to detect LE expressions from
dialogue contexts. | [
"Zhengxiang Shi",
"Procheta Sen",
"Aldo Lipani"
] | 2023-10-14 19:47:37 | http://arxiv.org/abs/2310.09651v1 | http://arxiv.org/pdf/2310.09651v1 | 2310.09651v1 |
Multimodal Federated Learning in Healthcare: a review | Recent advancements in multimodal machine learning have empowered the
development of accurate and robust AI systems in the medical domain, especially
within centralized database systems. Simultaneously, Federated Learning (FL)
has progressed, providing a decentralized mechanism where data need not be
consolidated, thereby enhancing the privacy and security of sensitive
healthcare data. The integration of these two concepts supports the ongoing
progress of multimodal learning in healthcare while ensuring the security and
privacy of patient records within local data-holding agencies. This paper
offers a concise overview of the significance of FL in healthcare and outlines
the current state-of-the-art approaches to Multimodal Federated Learning (MMFL)
within the healthcare domain. It comprehensively examines the existing
challenges in the field, shedding light on the limitations of present models.
Finally, the paper outlines potential directions for future advancements in the
field, aiming to bridge the gap between cutting-edge AI technology and the
imperative need for patient data privacy in healthcare applications. | [
"Jacob Thrasher",
"Alina Devkota",
"Prasiddha Siwakotai",
"Rohit Chivukula",
"Pranav Poudel",
"Chaunbo Hu",
"Binod Bhattarai",
"Prashnna Gyawali"
] | 2023-10-14 19:43:06 | http://arxiv.org/abs/2310.09650v1 | http://arxiv.org/pdf/2310.09650v1 | 2310.09650v1 |
DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization | The widespread practice of fine-tuning pretrained large language models
(LLMs) on domain-specific data faces two major challenges in memory and
privacy. First, as the size of LLMs continue to grow, encompassing billions of
parameters, the memory demands of gradient-based training methods via
backpropagation become prohibitively high. Second, given the tendency of LLMs
to memorize and disclose sensitive training data, the privacy of fine-tuning
data must be respected. To this end, we explore the potential of zeroth-order
methods in differentially private optimization for fine-tuning LLMs.
Zeroth-order methods, which rely solely on forward passes, substantially reduce
memory consumption during training. However, directly combining them with
standard differential privacy mechanism poses dimension-dependent complexity.
To bridge the gap, we introduce DPZero, a novel differentially private
zeroth-order algorithm with nearly dimension-independent rates. Our theoretical
analysis reveals that its complexity hinges primarily on the problem's
intrinsic dimension and exhibits only a logarithmic dependence on the ambient
dimension. This renders DPZero a highly practical option for real-world LLMs
deployments. | [
"Liang Zhang",
"Kiran Koshy Thekumparampil",
"Sewoong Oh",
"Niao He"
] | 2023-10-14 18:42:56 | http://arxiv.org/abs/2310.09639v1 | http://arxiv.org/pdf/2310.09639v1 | 2310.09639v1 |
Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved Accuracy | This report focuses on enhancing a binary code comment quality classification
model by integrating generated code and comment pairs, to improve model
accuracy. The dataset comprises 9048 pairs of code and comments written in the
C programming language, each annotated as "Useful" or "Not Useful."
Additionally, code and comment pairs are generated using a Large Language Model
Architecture, and these generated pairs are labeled to indicate their utility.
The outcome of this effort consists of two classification models: one utilizing
the original dataset and another incorporating the augmented dataset with the
newly generated code comment pairs and labels. | [
"Rohith Arumugam S",
"Angel Deborah S"
] | 2023-10-14 18:19:06 | http://arxiv.org/abs/2310.11467v1 | http://arxiv.org/pdf/2310.11467v1 | 2310.11467v1 |
Generative Adversarial Training for Text-to-Speech Synthesis Based on Raw Phonetic Input and Explicit Prosody Modelling | We describe an end-to-end speech synthesis system that uses generative
adversarial training. We train our Vocoder for raw phoneme-to-audio conversion,
using explicit phonetic, pitch and duration modeling. We experiment with
several pre-trained models for contextualized and decontextualized word
embeddings and we introduce a new method for highly expressive character voice
matching, based on discreet style tokens. | [
"Tiberiu Boros",
"Stefan Daniel Dumitrescu",
"Ionut Mironica",
"Radu Chivereanu"
] | 2023-10-14 18:15:51 | http://arxiv.org/abs/2310.09636v1 | http://arxiv.org/pdf/2310.09636v1 | 2310.09636v1 |
Landslide Topology Uncovers Failure Movements | The death toll and monetary damages from landslides continue to rise despite
advancements in predictive modeling. The predictive capability of these models
is limited as landslide databases used in training and assessing the models
often have crucial information missing, such as underlying failure types. Here,
we present an approach for identifying failure types based on their movements,
e.g., slides and flows by leveraging 3D landslide topology. We observe
topological proxies reveal prevalent signatures of mass movement mechanics
embedded in the landslide's morphology or shape, such as detecting coupled
movement styles within complex landslides. We find identical failure types
exhibit similar topological properties, and by using them as predictors, we can
identify failure types in historic and event-specific landslide databases
(including multi-temporal) from various geomorphological and climatic contexts
such as Italy, the US Pacific Northwest region, Denmark, Turkey, and China with
80 to 94 % accuracy. To demonstrate the real-world application of the method,
we implement it in two undocumented datasets from China and publicly release
the datasets. These new insights can considerably improve the performance of
landslide predictive models and impact assessments. Moreover, our work
introduces a new paradigm for studying landslide shapes to understand
underlying processes through the lens of landslide topology. | [
"Kamal Rana",
"Kushanav Bhuyan",
"Joaquin Vicente Ferrer",
"Fabrice Cotton",
"Ugur Ozturk",
"Filippo Catani",
"Nishant Malik"
] | 2023-10-14 17:53:55 | http://arxiv.org/abs/2310.09631v1 | http://arxiv.org/pdf/2310.09631v1 | 2310.09631v1 |
Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood | This research project aims to develop a real-time traffic sign detection
system using the YOLOv5 architecture and deploy it for efficient traffic sign
recognition during a drive in a suburban neighborhood. The project's primary
objectives are to train the YOLOv5 model on a diverse dataset of traffic sign
images and deploy the model on a suitable hardware platform capable of
real-time inference. The project will involve collecting a comprehensive
dataset of traffic sign images. By leveraging the trained YOLOv5 model, the
system will detect and classify traffic signs from a real-time camera on a
dashboard inside a vehicle. The performance of the deployed system will be
evaluated based on its accuracy in detecting traffic signs, real-time
processing speed, and overall reliability. During a case study in a suburban
neighborhood, the system demonstrated a notable 96% accuracy in detecting
traffic signs. This research's findings have the potential to improve road
safety and traffic management by providing timely and accurate real-time
information about traffic signs and can pave the way for further research into
autonomous driving. | [
"Harish Loghashankar",
"Hieu Nguyen"
] | 2023-10-14 17:52:28 | http://arxiv.org/abs/2310.09630v1 | http://arxiv.org/pdf/2310.09630v1 | 2310.09630v1 |
Federated Battery Diagnosis and Prognosis | Battery diagnosis, prognosis and health management models play a critical
role in the integration of battery systems in energy and mobility fields.
However, large-scale deployment of these models is hindered by a myriad of
challenges centered around data ownership, privacy, communication, and
processing. State-of-the-art battery diagnosis and prognosis methods require
centralized collection of data, which further aggravates these challenges. Here
we propose a federated battery prognosis model, which distributes the
processing of battery standard current-voltage-time-usage data in a
privacy-preserving manner. Instead of exchanging raw standard
current-voltage-time-usage data, our model communicates only the model
parameters, thus reducing communication load and preserving data
confidentiality. The proposed model offers a paradigm shift in battery health
management through privacy-preserving distributed methods for battery data
processing and remaining lifetime prediction. | [
"Nur Banu Altinpulluk",
"Deniz Altinpulluk",
"Paritosh Ramanan",
"Noah Paulson",
"Feng Qiu",
"Susan Babinec",
"Murat Yildirim"
] | 2023-10-14 17:46:50 | http://arxiv.org/abs/2310.09628v1 | http://arxiv.org/pdf/2310.09628v1 | 2310.09628v1 |
ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models | As large language models are integrated into society, robustness toward a
suite of prompts is increasingly important to maintain reliability in a
high-variance environment.Robustness evaluations must comprehensively
encapsulate the various settings in which a user may invoke an intelligent
system. This paper proposes ASSERT, Automated Safety Scenario Red Teaming,
consisting of three methods -- semantically aligned augmentation, target
bootstrapping, and adversarial knowledge injection. For robust safety
evaluation, we apply these methods in the critical domain of AI safety to
algorithmically generate a test suite of prompts covering diverse robustness
settings -- semantic equivalence, related scenarios, and adversarial. We
partition our prompts into four safety domains for a fine-grained analysis of
how the domain affects model performance. Despite dedicated safeguards in
existing state-of-the-art models, we find statistically significant performance
differences of up to 11% in absolute classification accuracy among semantically
related scenarios and error rates of up to 19% absolute error in zero-shot
adversarial settings, raising concerns for users' physical safety. | [
"Alex Mei",
"Sharon Levy",
"William Yang Wang"
] | 2023-10-14 17:10:28 | http://arxiv.org/abs/2310.09624v1 | http://arxiv.org/pdf/2310.09624v1 | 2310.09624v1 |
Machine Learning for Urban Air Quality Analytics: A Survey | The increasing air pollution poses an urgent global concern with far-reaching
consequences, such as premature mortality and reduced crop yield, which
significantly impact various aspects of our daily lives. Accurate and timely
analysis of air pollution is crucial for understanding its underlying
mechanisms and implementing necessary precautions to mitigate potential
socio-economic losses. Traditional analytical methodologies, such as
atmospheric modeling, heavily rely on domain expertise and often make
simplified assumptions that may not be applicable to complex air pollution
problems. In contrast, Machine Learning (ML) models are able to capture the
intrinsic physical and chemical rules by automatically learning from a large
amount of historical observational data, showing great promise in various air
quality analytical tasks. In this article, we present a comprehensive survey of
ML-based air quality analytics, following a roadmap spanning from data
acquisition to pre-processing, and encompassing various analytical tasks such
as pollution pattern mining, air quality inference, and forecasting. Moreover,
we offer a systematic categorization and summary of existing methodologies and
applications, while also providing a list of publicly available air quality
datasets to ease the research in this direction. Finally, we identify several
promising future research directions. This survey can serve as a valuable
resource for professionals seeking suitable solutions for their specific
challenges and advancing their research at the cutting edge. | [
"Jindong Han",
"Weijia Zhang",
"Hao Liu",
"Hui Xiong"
] | 2023-10-14 17:03:29 | http://arxiv.org/abs/2310.09620v1 | http://arxiv.org/pdf/2310.09620v1 | 2310.09620v1 |
A decoder-only foundation model for time-series forecasting | Motivated by recent advances in large language models for Natural Language
Processing (NLP), we design a time-series foundation model for forecasting
whose out-of-the-box zero-shot performance on a variety of public datasets
comes close to the accuracy of state-of-the-art supervised forecasting models
for each individual dataset. Our model is based on pretraining a
patched-decoder style attention model on a large time-series corpus, and can
work well across different forecasting history lengths, prediction lengths and
temporal granularities. | [
"Abhimanyu Das",
"Weihao Kong",
"Rajat Sen",
"Yichen Zhou"
] | 2023-10-14 17:01:37 | http://arxiv.org/abs/2310.10688v1 | http://arxiv.org/pdf/2310.10688v1 | 2310.10688v1 |
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning | Recently, model-based reinforcement learning algorithms have demonstrated
remarkable efficacy in visual input environments. These approaches begin by
constructing a parameterized simulation world model of the real environment
through self-supervised learning. By leveraging the imagination of the world
model, the agent's policy is enhanced without the constraints of sampling from
the real environment. The performance of these algorithms heavily relies on the
sequence modeling and generation capabilities of the world model. However,
constructing a perfectly accurate model of a complex unknown environment is
nearly impossible. Discrepancies between the model and reality may cause the
agent to pursue virtual goals, resulting in subpar performance in the real
environment. Introducing random noise into model-based reinforcement learning
has been proven beneficial. In this work, we introduce Stochastic
Transformer-based wORld Model (STORM), an efficient world model architecture
that combines the strong sequence modeling and generation capabilities of
Transformers with the stochastic nature of variational autoencoders. STORM
achieves a mean human performance of $126.7\%$ on the Atari $100$k benchmark,
setting a new record among state-of-the-art methods that do not employ
lookahead search techniques. Moreover, training an agent with $1.85$ hours of
real-time interaction experience on a single NVIDIA GeForce RTX 3090 graphics
card requires only $4.3$ hours, showcasing improved efficiency compared to
previous methodologies. | [
"Weipu Zhang",
"Gang Wang",
"Jian Sun",
"Yetian Yuan",
"Gao Huang"
] | 2023-10-14 16:42:02 | http://arxiv.org/abs/2310.09615v1 | http://arxiv.org/pdf/2310.09615v1 | 2310.09615v1 |
Towards Intelligent Network Management: Leveraging AI for Network Service Detection | As the complexity and scale of modern computer networks continue to increase,
there has emerged an urgent need for precise traffic analysis, which plays a
pivotal role in cutting-edge wireless connectivity technologies. This study
focuses on leveraging Machine Learning methodologies to create an advanced
network traffic classification system. We introduce a novel data-driven
approach that excels in identifying various network service types in real-time,
by analyzing patterns within the network traffic. Our method organizes similar
kinds of network traffic into distinct categories, referred to as network
services, based on latency requirement. Furthermore, it decomposes the network
traffic stream into multiple, smaller traffic flows, with each flow uniquely
carrying a specific service. Our ML models are trained on a dataset comprised
of labeled examples representing different network service types collected on
various Wi-Fi network conditions. Upon evaluation, our system demonstrates a
remarkable accuracy in distinguishing the network services. These results
emphasize the substantial promise of integrating Artificial Intelligence in
wireless technologies. Such an approach encourages more efficient energy
consumption, enhances Quality of Service assurance, and optimizes the
allocation of network resources, thus laying a solid groundwork for the
development of advanced intelligent networks. | [
"Khuong N. Nguyen",
"Abhishek Sehgal",
"Yuming Zhu",
"Junsu Choi",
"Guanbo Chen",
"Hao Chen",
"Boon Loong Ng",
"Charlie Zhang"
] | 2023-10-14 16:06:11 | http://arxiv.org/abs/2310.09609v1 | http://arxiv.org/pdf/2310.09609v1 | 2310.09609v1 |
Penetrative AI: Making LLMs Comprehend the Physical World | Recent developments in Large Language Models (LLMs) have demonstrated their
remarkable capabilities across a range of tasks. Questions, however, persist
about the nature of LLMs and their potential to integrate common-sense human
knowledge when performing tasks involving information about the real physical
world. This paper delves into these questions by exploring how LLMs can be
extended to interact with and reason about the physical world through IoT
sensors and actuators, a concept that we term "\textit{Penetrative AI}". The
paper explores such an extension at two levels of LLMs' ability to penetrate
into the physical world via the processing of sensory signals. Our preliminary
findings indicate that LLMs, with ChatGPT being the representative example in
our exploration, have considerable and unique proficiency in employing the
knowledge they learned during training for interpreting IoT sensor data and
reasoning over them about tasks in the physical realm. Not only this opens up
new applications for LLMs beyond traditional text-based tasks, but also enables
new ways of incorporating human knowledge in cyber-physical systems. | [
"Huatao Xu",
"Liying Han",
"Mo Li",
"Mani Srivastava"
] | 2023-10-14 15:48:15 | http://arxiv.org/abs/2310.09605v1 | http://arxiv.org/pdf/2310.09605v1 | 2310.09605v1 |
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior | This paper studies the fundamental problem of multi-layer generator models in
learning hierarchical representations. The multi-layer generator model that
consists of multiple layers of latent variables organized in a top-down
architecture tends to learn multiple levels of data abstraction. However, such
multi-layer latent variables are typically parameterized to be Gaussian, which
can be less informative in capturing complex abstractions, resulting in limited
success in hierarchical representation learning. On the other hand, the
energy-based (EBM) prior is known to be expressive in capturing the data
regularities, but it often lacks the hierarchical structure to capture
different levels of hierarchical representations. In this paper, we propose a
joint latent space EBM prior model with multi-layer latent variables for
effective hierarchical representation learning. We develop a variational joint
learning scheme that seamlessly integrates an inference model for efficient
inference. Our experiments demonstrate that the proposed joint EBM prior is
effective and expressive in capturing hierarchical representations and
modelling data distribution. | [
"Jiali Cui",
"Ying Nian Wu",
"Tian Han"
] | 2023-10-14 15:44:14 | http://arxiv.org/abs/2310.09604v1 | http://arxiv.org/pdf/2310.09604v1 | 2310.09604v1 |
Adaptive maximization of social welfare | We consider the problem of repeatedly choosing policies to maximize social
welfare. Welfare is a weighted sum of private utility and public revenue.
Earlier outcomes inform later policies. Utility is not observed, but indirectly
inferred. Response functions are learned through experimentation.
We derive a lower bound on regret, and a matching adversarial upper bound for
a variant of the Exp3 algorithm. Cumulative regret grows at a rate of
$T^{2/3}$. This implies that (i) welfare maximization is harder than the
multi-armed bandit problem (with a rate of $T^{1/2}$ for finite policy sets),
and (ii) our algorithm achieves the optimal rate. For the stochastic setting,
if social welfare is concave, we can achieve a rate of $T^{1/2}$ (for
continuous policy sets), using a dyadic search algorithm.
We analyze an extension to nonlinear income taxation, and sketch an extension
to commodity taxation. We compare our setting to monopoly pricing (which is
easier), and price setting for bilateral trade (which is harder). | [
"Nicolo Cesa-Bianchi",
"Roberto Colomboni",
"Maximilian Kasy"
] | 2023-10-14 15:09:56 | http://arxiv.org/abs/2310.09597v1 | http://arxiv.org/pdf/2310.09597v1 | 2310.09597v1 |
Causality and Independence Enhancement for Biased Node Classification | Most existing methods that address out-of-distribution (OOD) generalization
for node classification on graphs primarily focus on a specific type of data
biases, such as label selection bias or structural bias. However, anticipating
the type of bias in advance is extremely challenging, and designing models
solely for one specific type may not necessarily improve overall generalization
performance. Moreover, limited research has focused on the impact of mixed
biases, which are more prevalent and demanding in real-world scenarios. To
address these limitations, we propose a novel Causality and Independence
Enhancement (CIE) framework, applicable to various graph neural networks
(GNNs). Our approach estimates causal and spurious features at the node
representation level and mitigates the influence of spurious correlations
through the backdoor adjustment. Meanwhile, independence constraint is
introduced to improve the discriminability and stability of causal and spurious
features in complex biased environments. Essentially, CIE eliminates different
types of data biases from a unified perspective, without the need to design
separate methods for each bias as before. To evaluate the performance under
specific types of data biases, mixed biases, and low-resource scenarios, we
conducted comprehensive experiments on five publicly available datasets.
Experimental results demonstrate that our approach CIE not only significantly
enhances the performance of GNNs but outperforms state-of-the-art debiased node
classification methods. | [
"Guoxin Chen",
"Yongqing Wang",
"Fangda Guo",
"Qinglang Guo",
"Jiangli Shao",
"Huawei Shen",
"Xueqi Cheng"
] | 2023-10-14 13:56:24 | http://arxiv.org/abs/2310.09586v1 | http://arxiv.org/pdf/2310.09586v1 | 2310.09586v1 |
Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation | Deep Equilibrium Models (DEQs) and Neural Ordinary Differential Equations
(Neural ODEs) are two branches of implicit models that have achieved remarkable
success owing to their superior performance and low memory consumption. While
both are implicit models, DEQs and Neural ODEs are derived from different
mathematical formulations. Inspired by homotopy continuation, we establish a
connection between these two models and illustrate that they are actually two
sides of the same coin. Homotopy continuation is a classical method of solving
nonlinear equations based on a corresponding ODE. Given this connection, we
proposed a new implicit model called HomoODE that inherits the property of high
accuracy from DEQs and the property of stability from Neural ODEs. Unlike DEQs,
which explicitly solve an equilibrium-point-finding problem via Newton's
methods in the forward pass, HomoODE solves the equilibrium-point-finding
problem implicitly using a modified Neural ODE via homotopy continuation.
Further, we developed an acceleration method for HomoODE with a shared
learnable initial point. It is worth noting that our model also provides a
better understanding of why Augmented Neural ODEs work as long as the augmented
part is regarded as the equilibrium point to find. Comprehensive experiments
with several image classification tasks demonstrate that HomoODE surpasses
existing implicit models in terms of both accuracy and memory consumption. | [
"Shutong Ding",
"Tianyu Cui",
"Jingya Wang",
"Ye Shi"
] | 2023-10-14 13:28:36 | http://arxiv.org/abs/2310.09583v1 | http://arxiv.org/pdf/2310.09583v1 | 2310.09583v1 |
Reduced Policy Optimization for Continuous Control with Hard Constraints | Recent advances in constrained reinforcement learning (RL) have endowed
reinforcement learning with certain safety guarantees. However, deploying
existing constrained RL algorithms in continuous control tasks with general
hard constraints remains challenging, particularly in those situations with
non-convex hard constraints. Inspired by the generalized reduced gradient (GRG)
algorithm, a classical constrained optimization technique, we propose a reduced
policy optimization (RPO) algorithm that combines RL with GRG to address
general hard constraints. RPO partitions actions into basic actions and
nonbasic actions following the GRG method and outputs the basic actions via a
policy network. Subsequently, RPO calculates the nonbasic actions by solving
equations based on equality constraints using the obtained basic actions. The
policy network is then updated by implicitly differentiating nonbasic actions
with respect to basic actions. Additionally, we introduce an action projection
procedure based on the reduced gradient and apply a modified Lagrangian
relaxation technique to ensure inequality constraints are satisfied. To the
best of our knowledge, RPO is the first attempt that introduces GRG to RL as a
way of efficiently handling both equality and inequality hard constraints. It
is worth noting that there is currently a lack of RL environments with complex
hard constraints, which motivates us to develop three new benchmarks: two
robotics manipulation tasks and a smart grid operation control task. With these
benchmarks, RPO achieves better performance than previous constrained RL
algorithms in terms of both cumulative reward and constraint violation. We
believe RPO, along with the new benchmarks, will open up new opportunities for
applying RL to real-world problems with complex constraints. | [
"Shutong Ding",
"Jingya Wang",
"Yali Du",
"Ye Shi"
] | 2023-10-14 12:55:43 | http://arxiv.org/abs/2310.09574v1 | http://arxiv.org/pdf/2310.09574v1 | 2310.09574v1 |
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization | The performance of automated algorithm selection (AAS) strongly depends on
the portfolio of algorithms to choose from. Selecting the portfolio is a
non-trivial task that requires balancing the trade-off between the higher
flexibility of large portfolios with the increased complexity of the AAS task.
In practice, probably the most common way to choose the algorithms for the
portfolio is a greedy selection of the algorithms that perform well in some
reference tasks of interest.
We set out in this work to investigate alternative, data-driven portfolio
selection techniques. Our proposed method creates algorithm behavior
meta-representations, constructs a graph from a set of algorithms based on
their meta-representation similarity, and applies a graph algorithm to select a
final portfolio of diverse, representative, and non-redundant algorithms. We
evaluate two distinct meta-representation techniques (SHAP and performance2vec)
for selecting complementary portfolios from a total of 324 different variants
of CMA-ES for the task of optimizing the BBOB single-objective problems in
dimensionalities 5 and 30 with different cut-off budgets. We test two types of
portfolios: one related to overall algorithm behavior and the `personalized'
one (related to algorithm behavior per each problem separately). We observe
that the approach built on the performance2vec-based representations favors
small portfolios with negligible error in the AAS task relative to the virtual
best solver from the selected portfolio, whereas the portfolios built from the
SHAP-based representations gain from higher flexibility at the cost of
decreased performance of the AAS. Across most considered scenarios,
personalized portfolios yield comparable or slightly better performance than
the classical greedy approach. They outperform the full portfolio in all
scenarios. | [
"Ana Kostovska",
"Gjorgjina Cenikj",
"Diederick Vermetten",
"Anja Jankovic",
"Ana Nikolikj",
"Urban Skvorc",
"Peter Korosec",
"Carola Doerr",
"Tome Eftimov"
] | 2023-10-14 12:13:41 | http://arxiv.org/abs/2310.10685v1 | http://arxiv.org/pdf/2310.10685v1 | 2310.10685v1 |
Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity? | Foundation models like CLIP are trained on hundreds of millions of samples
and effortlessly generalize to new tasks and inputs. Out of the box, CLIP shows
stellar zero-shot and few-shot capabilities on a wide range of
out-of-distribution (OOD) benchmarks, which prior works attribute mainly to
today's large and comprehensive training dataset (like LAION). However, it is
questionable how meaningful terms like out-of-distribution generalization are
for CLIP as it seems likely that web-scale datasets like LAION simply contain
many samples that are similar to common OOD benchmarks originally designed for
ImageNet. To test this hypothesis, we retrain CLIP on pruned LAION splits that
replicate ImageNet's train-test similarity with respect to common OOD
benchmarks. While we observe a performance drop on some benchmarks,
surprisingly, CLIP's overall performance remains high. This shows that high
train-test similarity is insufficient to explain CLIP's OOD performance, and
other properties of the training data must drive CLIP to learn more
generalizable representations. Additionally, by pruning data points that are
dissimilar to the OOD benchmarks, we uncover a 100M split of LAION
($\frac{1}{4}$th of its original size) on which CLIP can be trained to match
its original OOD performance. | [
"Prasanna Mayilvahanan",
"Thaddäus Wiedemer",
"Evgenia Rusak",
"Matthias Bethge",
"Wieland Brendel"
] | 2023-10-14 11:24:28 | http://arxiv.org/abs/2310.09562v1 | http://arxiv.org/pdf/2310.09562v1 | 2310.09562v1 |
Graph Neural Network approaches for single-cell data: A recent overview | Graph Neural Networks (GNN) are reshaping our understanding of biomedicine
and diseases by revealing the deep connections among genes and cells. As both
algorithmic and biomedical technologies have advanced significantly, we're
entering a transformative phase of personalized medicine. While pioneering
tools like Graph Attention Networks (GAT) and Graph Convolutional Neural
Networks (Graph CNN) are advancing graph-based learning, the rise of
single-cell sequencing techniques is reshaping our insights on cellular
diversity and function. Numerous studies have combined GNNs with single-cell
data, showing promising results. In this work, we highlight the GNN
methodologies tailored for single-cell data over the recent years. We outline
the diverse range of graph deep learning architectures that center on GAT
methodologies. Furthermore, we underscore the several objectives of GNN
strategies in single-cell data contexts, ranging from cell-type annotation,
data integration and imputation, gene regulatory network reconstruction,
clustering and many others. This review anticipates a future where GNNs become
central to single-cell analysis efforts, particularly as vast omics datasets
are continuously generated and the interconnectedness of cells and genes
enhances our depth of knowledge in biomedicine. | [
"Konstantinos Lazaros",
"Dimitris E. Koumadorakis",
"Panagiotis Vlamos",
"Aristidis G. Vrahatis"
] | 2023-10-14 11:09:17 | http://arxiv.org/abs/2310.09561v1 | http://arxiv.org/pdf/2310.09561v1 | 2310.09561v1 |
A study of the impact of generative AI-based data augmentation on software metadata classification | This paper presents the system submitted by the team from IIT(ISM) Dhanbad in
FIRE IRSE 2023 shared task 1 on the automatic usefulness prediction of
code-comment pairs as well as the impact of Large Language Model(LLM) generated
data on original base data towards an associated source code. We have developed
a framework where we train a machine learning-based model using the neural
contextual representations of the comments and their corresponding codes to
predict the usefulness of code-comments pair and performance analysis with
LLM-generated data with base data. In the official assessment, our system
achieves a 4% increase in F1-score from baseline and the quality of generated
data. | [
"Tripti Kumari",
"Chakali Sai Charan",
"Ayan Das"
] | 2023-10-14 10:47:10 | http://arxiv.org/abs/2310.13714v1 | http://arxiv.org/pdf/2310.13714v1 | 2310.13714v1 |
Neural network scoring for efficient computing | Much work has been dedicated to estimating and optimizing workloads in
high-performance computing (HPC) and deep learning. However, researchers have
typically relied on few metrics to assess the efficiency of those techniques.
Most notably, the accuracy, the loss of the prediction, and the computational
time with regard to GPUs or/and CPUs characteristics. It is rare to see figures
for power consumption, partly due to the difficulty of obtaining accurate power
readings. In this paper, we introduce a composite score that aims to
characterize the trade-off between accuracy and power consumption measured
during the inference of neural networks. For this purpose, we present a new
open-source tool allowing researchers to consider more metrics: granular power
consumption, but also RAM/CPU/GPU utilization, as well as storage, and network
input/output (I/O). To our best knowledge, it is the first fit test for neural
architectures on hardware architectures. This is made possible thanks to
reproducible power efficiency measurements. We applied this procedure to
state-of-the-art neural network architectures on miscellaneous hardware. One of
the main applications and novelties is the measurement of algorithmic power
efficiency. The objective is to allow researchers to grasp their algorithms'
efficiencies better. This methodology was developed to explore trade-offs
between energy usage and accuracy in neural networks. It is also useful when
fitting hardware for a specific task or to compare two architectures more
accurately, with architecture exploration in mind. | [
"Hugo Waltsburger",
"Erwan Libessart",
"Chengfang Ren",
"Anthony Kolar",
"Regis Guinvarc'h"
] | 2023-10-14 10:29:52 | http://arxiv.org/abs/2310.09554v1 | http://arxiv.org/pdf/2310.09554v1 | 2310.09554v1 |
ARTree: A Deep Autoregressive Model for Phylogenetic Inference | Designing flexible probabilistic models over tree topologies is important for
developing efficient phylogenetic inference methods. To do that, previous works
often leverage the similarity of tree topologies via hand-engineered heuristic
features which would require pre-sampled tree topologies and may suffer from
limited approximation capability. In this paper, we propose a deep
autoregressive model for phylogenetic inference based on graph neural networks
(GNNs), called ARTree. By decomposing a tree topology into a sequence of leaf
node addition operations and modeling the involved conditional distributions
based on learnable topological features via GNNs, ARTree can provide a rich
family of distributions over the entire tree topology space that have simple
sampling algorithms and density estimation procedures, without using heuristic
features. We demonstrate the effectiveness and efficiency of our method on a
benchmark of challenging real data tree topology density estimation and
variational Bayesian phylogenetic inference problems. | [
"Tianyu Xie",
"Cheng Zhang"
] | 2023-10-14 10:26:03 | http://arxiv.org/abs/2310.09553v1 | http://arxiv.org/pdf/2310.09553v1 | 2310.09553v1 |
Benchmarking the Sim-to-Real Gap in Cloth Manipulation | Realistic physics engines play a crucial role for learning to manipulate
deformable objects such as garments in simulation. By doing so, researchers can
circumvent challenges such as sensing the deformation of the object in the
real-world. In spite of the extensive use of simulations for this task, few
works have evaluated the reality gap between deformable object simulators and
real-world data. We present a benchmark dataset to evaluate the sim-to-real gap
in cloth manipulation. The dataset is collected by performing a dynamic cloth
manipulation task involving contact with a rigid table. We use the dataset to
evaluate the reality gap, computational time, and simulation stability of four
popular deformable object simulators: MuJoCo, Bullet, Flex, and SOFA.
Additionally, we discuss the benefits and drawbacks of each simulator. The
benchmark dataset is open-source. Supplementary material, videos, and code, can
be found at https://sites.google.com/view/cloth-sim2real-benchmark. | [
"David Blanco-Mulero",
"Oriol Barbany",
"Gokhan Alcan",
"Adrià Colomé",
"Carme Torras",
"Ville Kyrki"
] | 2023-10-14 09:36:01 | http://arxiv.org/abs/2310.09543v1 | http://arxiv.org/pdf/2310.09543v1 | 2310.09543v1 |
CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering | Large language models (LLMs) have demonstrated remarkable performance by
following natural language instructions without fine-tuning them on
domain-specific tasks and data. However, leveraging LLMs for domain-specific
question answering suffers from severe limitations. The generated answer tends
to hallucinate due to the training data collection time (when using
off-the-shelf), complex user utterance and wrong retrieval (in
retrieval-augmented generation). Furthermore, due to the lack of awareness
about the domain and expected output, such LLMs may generate unexpected and
unsafe answers that are not tailored to the target domain. In this paper, we
propose CarExpert, an in-car retrieval-augmented conversational
question-answering system leveraging LLMs for different tasks. Specifically,
CarExpert employs LLMs to control the input, provide domain-specific documents
to the extractive and generative answering components, and controls the output
to ensure safe and domain-specific answers. A comprehensive empirical
evaluation exhibits that CarExpert outperforms state-of-the-art LLMs in
generating natural, safe and car-specific answers. | [
"Md Rashad Al Hasan Rony",
"Christian Suess",
"Sinchana Ramakanth Bhat",
"Viju Sudhi",
"Julia Schneider",
"Maximilian Vogel",
"Roman Teucher",
"Ken E. Friedl",
"Soumya Sahoo"
] | 2023-10-14 08:46:24 | http://arxiv.org/abs/2310.09536v1 | http://arxiv.org/pdf/2310.09536v1 | 2310.09536v1 |
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction | Protein structure-based property prediction has emerged as a promising
approach for various biological tasks, such as protein function prediction and
sub-cellular location estimation. The existing methods highly rely on
experimental protein structure data and fail in scenarios where these data are
unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were
utilized as alternatives. However, we observed that current practices, which
simply employ accurately predicted structures during inference, suffer from
notable degradation in prediction accuracy. While similar phenomena have been
extensively studied in general fields (e.g., Computer Vision) as model
robustness, their impact on protein property prediction remains unexplored. In
this paper, we first investigate the reason behind the performance decrease
when utilizing predicted structures, attributing it to the structure embedding
bias from the perspective of structure representation learning. To study this
problem, we identify a Protein 3D Graph Structure Learning Problem for Robust
Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present
a protein Structure embedding Alignment Optimization framework (SAO) to
mitigate the problem of structure embedding bias between the predicted and
experimental protein structures. Extensive experiments have shown that our
framework is model-agnostic and effective in improving the property prediction
of both predicted structures and experimental structures. The benchmark
datasets and codes will be released to benefit the community. | [
"Yufei Huang",
"Siyuan Li",
"Jin Su",
"Lirong Wu",
"Odin Zhang",
"Haitao Lin",
"Jingqi Qi",
"Zihan Liu",
"Zhangyang Gao",
"Yuyang Liu",
"Jiangbin Zheng",
"Stan. ZQ. Li"
] | 2023-10-14 08:43:42 | http://arxiv.org/abs/2310.11466v2 | http://arxiv.org/pdf/2310.11466v2 | 2310.11466v2 |
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks | In various engineering and applied science applications, repetitive numerical
simulations of partial differential equations (PDEs) for varying input
parameters are often required (e.g., aircraft shape optimization over many
design parameters) and solvers are required to perform rapid execution. In this
study, we suggest a path that potentially opens up a possibility for
physics-informed neural networks (PINNs), emerging deep-learning-based solvers,
to be considered as one such solver. Although PINNs have pioneered a proper
integration of deep-learning and scientific computing, they require repetitive
time-consuming training of neural networks, which is not suitable for
many-query scenarios. To address this issue, we propose a lightweight low-rank
PINNs containing only hundreds of model parameters and an associated
hypernetwork-based meta-learning algorithm, which allows efficient
approximation of solutions of PDEs for varying ranges of PDE input parameters.
Moreover, we show that the proposed method is effective in overcoming a
challenging issue, known as "failure modes" of PINNs. | [
"Woojin Cho",
"Kookjin Lee",
"Donsub Rim",
"Noseong Park"
] | 2023-10-14 08:13:43 | http://arxiv.org/abs/2310.09528v1 | http://arxiv.org/pdf/2310.09528v1 | 2310.09528v1 |
Software Metadata Classification based on Generative Artificial Intelligence | This paper presents a novel approach to enhance the performance of binary
code comment quality classification models through the application of
Generative Artificial Intelligence (AI). By leveraging the OpenAI API, a
dataset comprising 1239 newly generated code-comment pairs, extracted from
various GitHub repositories and open-source projects, has been labelled as
"Useful" or "Not Useful", and integrated into the existing corpus of 9048 pairs
in the C programming language. Employing a cutting-edge Large Language Model
Architecture, the generated dataset demonstrates notable improvements in model
accuracy. Specifically, when incorporated into the Support Vector Machine (SVM)
model, a 6% increase in precision is observed, rising from 0.79 to 0.85.
Additionally, the Artificial Neural Network (ANN) model exhibits a 1.5%
increase in recall, climbing from 0.731 to 0.746. This paper sheds light on the
potential of Generative AI in augmenting code comment quality classification
models. The results affirm the effectiveness of this methodology, indicating
its applicability in broader contexts within software development and quality
assurance domains. The findings underscore the significance of integrating
generative techniques to advance the accuracy and efficacy of machine learning
models in practical software engineering scenarios. | [
"Seetharam Killivalavan",
"Durairaj Thenmozhi"
] | 2023-10-14 07:38:16 | http://arxiv.org/abs/2310.13006v1 | http://arxiv.org/pdf/2310.13006v1 | 2310.13006v1 |
Instruction Tuning with Human Curriculum | The dominant paradigm for instruction tuning is the random-shuffled training
of maximally diverse instruction-response pairs. This paper explores the
potential benefits of applying a structured cognitive learning approach to
instruction tuning in contemporary large language models like ChatGPT and
GPT-4. Unlike the previous conventional randomized instruction dataset, we
propose a highly structured synthetic dataset that mimics the progressive and
organized nature of human education. We curate our dataset by aligning it with
educational frameworks, incorporating meta information including its topic and
cognitive rigor level for each sample. Our dataset covers comprehensive
fine-grained topics spanning diverse educational stages (from middle school to
graduate school) with various questions for each topic to enhance conceptual
depth using Bloom's taxonomy-a classification framework distinguishing various
levels of human cognition for each concept. The results demonstrate that this
cognitive rigorous training approach yields significant performance
enhancements - +3.06 on the MMLU benchmark and an additional +1.28 on AI2
Reasoning Challenge (hard set) - compared to conventional randomized training,
all while avoiding additional computational costs. This research highlights the
potential of leveraging human learning principles to enhance the capabilities
of language models in comprehending and responding to complex instructions and
tasks. | [
"Bruce W. Lee",
"Hyunsoo Cho",
"Kang Min Yoo"
] | 2023-10-14 07:16:08 | http://arxiv.org/abs/2310.09518v1 | http://arxiv.org/pdf/2310.09518v1 | 2310.09518v1 |
Efficient Link Prediction via GNN Layers Induced by Negative Sampling | Graph neural networks (GNNs) for link prediction can loosely be divided into
two broad categories. First, \emph{node-wise} architectures pre-compute
individual embeddings for each node that are later combined by a simple decoder
to make predictions. While extremely efficient at inference time (since node
embeddings are only computed once and repeatedly reused), model expressiveness
is limited such that isomorphic nodes contributing to candidate edges may not
be distinguishable, compromising accuracy. In contrast, \emph{edge-wise}
methods rely on the formation of edge-specific subgraph embeddings to enrich
the representation of pair-wise relationships, disambiguating isomorphic nodes
to improve accuracy, but with the cost of increased model complexity. To better
navigate this trade-off, we propose a novel GNN architecture whereby the
\emph{forward pass} explicitly depends on \emph{both} positive (as is typical)
and negative (unique to our approach) edges to inform more flexible, yet still
cheap node-wise embeddings. This is achieved by recasting the embeddings
themselves as minimizers of a forward-pass-specific energy function (distinct
from the actual training loss) that favors separation of positive and negative
samples. As demonstrated by extensive empirical evaluations, the resulting
architecture retains the inference speed of node-wise models, while producing
competitive accuracy with edge-wise alternatives. | [
"Yuxin Wang",
"Xiannian Hu",
"Quan Gan",
"Xuanjing Huang",
"Xipeng Qiu",
"David Wipf"
] | 2023-10-14 07:02:54 | http://arxiv.org/abs/2310.09516v1 | http://arxiv.org/pdf/2310.09516v1 | 2310.09516v1 |
Online Parameter Identification of Generalized Non-cooperative Game | This work studies the parameter identification problem of a generalized
non-cooperative game, where each player's cost function is influenced by an
observable signal and some unknown parameters. We consider the scenario where
equilibrium of the game at some observable signals can be observed with noises,
whereas our goal is to identify the unknown parameters with the observed data.
Assuming that the observable signals and the corresponding noise-corrupted
equilibriums are acquired sequentially, we construct this parameter
identification problem as online optimization and introduce a novel online
parameter identification algorithm. To be specific, we construct a regularized
loss function that balances conservativeness and correctiveness, where the
conservativeness term ensures that the new estimates do not deviate
significantly from the current estimates, while the correctiveness term is
captured by the Karush-Kuhn-Tucker conditions. We then prove that when the
players' cost functions are linear with respect to the unknown parameters and
the learning rate of the online parameter identification algorithm satisfies
\mu_k \propto 1/\sqrt{k}, along with other assumptions, the regret bound of the
proposed algorithm is O(\sqrt{K}). Finally, we conduct numerical simulations on
a Nash-Cournot problem to demonstrate that the performance of the online
identification algorithm is comparable to that of the offline setting. | [
"Jianguo Chen",
"Jinlong Lei",
"Hongsheng Qi",
"Yiguang Hong"
] | 2023-10-14 06:43:58 | http://arxiv.org/abs/2310.09511v1 | http://arxiv.org/pdf/2310.09511v1 | 2310.09511v1 |
Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches | The forthcoming 6G systems are expected to address a wide range of
non-stationary tasks. This poses challenges to traditional medium access
control (MAC) protocols that are static and predefined. In response,
data-driven MAC protocols have recently emerged, offering ability to tailor
their signaling messages for specific tasks. This article presents a novel
categorization of these data-driven MAC protocols into three levels: Level 1
MAC. task-oriented neural protocols constructed using multi-agent deep
reinforcement learning (MADRL); Level 2 MAC. neural network-oriented symbolic
protocols developed by converting Level 1 MAC outputs into explicit symbols;
and Level 3 MAC. language-oriented semantic protocols harnessing large language
models (LLMs) and generative models. With this categorization, we aim to
explore the opportunities and challenges of each level by delving into their
foundational techniques. Drawing from information theory and associated
principles as well as selected case studies, this study provides insights into
the trajectory of data-driven MAC protocols and sheds light on future research
directions. | [
"Jihong Park",
"Seung-Woo Ko",
"Jinho Choi",
"Seong-Lyun Kim",
"Mehdi Bennis"
] | 2023-10-14 06:28:50 | http://arxiv.org/abs/2310.09506v1 | http://arxiv.org/pdf/2310.09506v1 | 2310.09506v1 |
Advancing Test-Time Adaptation for Acoustic Foundation Models in Open-World Shifts | Test-Time Adaptation (TTA) is a critical paradigm for tackling distribution
shifts during inference, especially in visual recognition tasks. However, while
acoustic models face similar challenges due to distribution shifts in test-time
speech, TTA techniques specifically designed for acoustic modeling in the
context of open-world data shifts remain scarce. This gap is further
exacerbated when considering the unique characteristics of acoustic foundation
models: 1) they are primarily built on transformer architectures with layer
normalization and 2) they deal with test-time speech data of varying lengths in
a non-stationary manner. These aspects make the direct application of
vision-focused TTA methods, which are mostly reliant on batch normalization and
assume independent samples, infeasible. In this paper, we delve into TTA for
pre-trained acoustic models facing open-world data shifts. We find that noisy,
high-entropy speech frames, often non-silent, carry key semantic content.
Traditional TTA methods might inadvertently filter out this information using
potentially flawed heuristics. In response, we introduce a heuristic-free,
learning-based adaptation enriched by confidence enhancement. Noting that
speech signals' short-term consistency, we also apply consistency
regularization during test-time optimization. Our experiments on synthetic and
real-world datasets affirm our method's superiority over existing baselines. | [
"Hongfu Liu",
"Hengguan Huang",
"Ye Wang"
] | 2023-10-14 06:22:08 | http://arxiv.org/abs/2310.09505v1 | http://arxiv.org/pdf/2310.09505v1 | 2310.09505v1 |
Learning In-between Imagery Dynamics via Physical Latent Spaces | We present a framework designed to learn the underlying dynamics between two
images observed at consecutive time steps. The complex nature of image data and
the lack of temporal information pose significant challenges in capturing the
unique evolving patterns. Our proposed method focuses on estimating the
intermediary stages of image evolution, allowing for interpretability through
latent dynamics while preserving spatial correlations with the image. By
incorporating a latent variable that follows a physical model expressed in
partial differential equations (PDEs), our approach ensures the
interpretability of the learned model and provides insight into corresponding
image dynamics. We demonstrate the robustness and effectiveness of our learning
framework through a series of numerical tests using geoscientific imagery data. | [
"Jihun Han",
"Yoonsang Lee",
"Anne Gelb"
] | 2023-10-14 05:14:51 | http://arxiv.org/abs/2310.09495v1 | http://arxiv.org/pdf/2310.09495v1 | 2310.09495v1 |
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning | Long-term time series forecasting (LTSF) is important for various domains but
is confronted by challenges in handling the complex temporal-contextual
relationships. As multivariate input models underperforming some recent
univariate counterparts, we posit that the issue lies in the inefficiency of
existing multivariate LTSF Transformers to model series-wise relationships: the
characteristic differences between series are often captured incorrectly. To
address this, we introduce ARM: a multivariate temporal-contextual adaptive
learning method, which is an enhanced architecture specifically designed for
multivariate LTSF modelling. ARM employs Adaptive Univariate Effect Learning
(AUEL), Random Dropping (RD) training strategy, and Multi-kernel Local
Smoothing (MKLS), to better handle individual series temporal patterns and
correctly learn inter-series dependencies. ARM demonstrates superior
performance on multiple benchmarks without significantly increasing
computational costs compared to vanilla Transformer, thereby advancing the
state-of-the-art in LTSF. ARM is also generally applicable to other LTSF
architecture beyond vanilla Transformer. | [
"Jiecheng Lu",
"Xu Han",
"Shihao Yang"
] | 2023-10-14 04:37:38 | http://arxiv.org/abs/2310.09488v1 | http://arxiv.org/pdf/2310.09488v1 | 2310.09488v1 |
Mirage: Model-Agnostic Graph Distillation for Graph Classification | GNNs, like other deep learning models, are data and computation hungry. There
is a pressing need to scale training of GNNs on large datasets to enable their
usage on low-resource environments. Graph distillation is an effort in that
direction with the aim to construct a smaller synthetic training set from the
original training data without significantly compromising model performance.
While initial efforts are promising, this work is motivated by two key
observations: (1) Existing graph distillation algorithms themselves rely on
training with the full dataset, which undermines the very premise of graph
distillation. (2) The distillation process is specific to the target GNN
architecture and hyper-parameters and thus not robust to changes in the
modeling pipeline. We circumvent these limitations by designing a distillation
algorithm called Mirage for graph classification. Mirage is built on the
insight that a message-passing GNN decomposes the input graph into a multiset
of computation trees. Furthermore, the frequency distribution of computation
trees is often skewed in nature, enabling us to condense this data into a
concise distilled summary. By compressing the computation data itself, as
opposed to emulating gradient flows on the original training set-a prevalent
approach to date-Mirage transforms into an unsupervised and
architecture-agnostic distillation algorithm. Extensive benchmarking on
real-world datasets underscores Mirage's superiority, showcasing enhanced
generalization accuracy, data compression, and distillation efficiency when
compared to state-of-the-art baselines. | [
"Mridul Gupta",
"Sahil Manchanda",
"Hariprasad Kodamana",
"Sayan Ranu"
] | 2023-10-14 04:21:52 | http://arxiv.org/abs/2310.09486v2 | http://arxiv.org/pdf/2310.09486v2 | 2310.09486v2 |
Applying Bayesian Ridge Regression AI Modeling in Virus Severity Prediction | Artificial intelligence (AI) is a powerful tool for reshaping healthcare
systems. In healthcare, AI is invaluable for its capacity to manage vast
amounts of data, which can lead to more accurate and speedy diagnoses,
ultimately easing the workload on healthcare professionals. As a result, AI has
proven itself to be a power tool across various industries, simplifying complex
tasks and pattern recognition that would otherwise be overwhelming for humans
or traditional computer algorithms. In this paper, we review the strengths and
weaknesses of Bayesian Ridge Regression, an AI model that can be used to bring
cutting edge virus analysis to healthcare professionals around the world. The
model's accuracy assessment revealed promising results, with room for
improvement primarily related to data organization. In addition, the severity
index serves as a valuable tool to gain a broad overview of patient care needs,
aligning with healthcare professionals' preference for broader categorizations. | [
"Jai Pal",
"Bryan Hong"
] | 2023-10-14 04:17:00 | http://arxiv.org/abs/2310.09485v1 | http://arxiv.org/pdf/2310.09485v1 | 2310.09485v1 |
Exploring the Design Space of Diffusion Autoencoders for Face Morphing | Face morphs created by Diffusion Autoencoders are a recent innovation and the
design space of such an approach has not been well explored. We explore three
axes of the design space, i.e., 1) sampling algorithms, 2) the reverse DDIM
solver, and 3) partial sampling through small amounts of added noise. | [
"Zander Blasingame",
"Chen Liu"
] | 2023-10-14 04:11:01 | http://arxiv.org/abs/2310.09484v1 | http://arxiv.org/pdf/2310.09484v1 | 2310.09484v1 |
Can CNNs Accurately Classify Human Emotions? A Deep-Learning Facial Expression Recognition Study | Emotional Artificial Intelligences are currently one of the most anticipated
developments of AI. If successful, these AIs will be classified as one of the
most complex, intelligent nonhuman entities as they will possess sentience, the
primary factor that distinguishes living humans and mechanical machines. For
AIs to be classified as "emotional," they should be able to empathize with
others and classify their emotions because without such abilities they cannot
normally interact with humans. This study investigates the CNN model's ability
to recognize and classify human facial expressions (positive, neutral,
negative). The CNN model made for this study is programmed in Python and
trained with preprocessed data from the Chicago Face Database. The model is
intentionally designed with less complexity to further investigate its ability.
We hypothesized that the model will perform better than chance (33.3%) in
classifying each emotion class of input data. The model accuracy was tested
with novel images. Accuracy was summarized in a percentage report, comparative
plot, and confusion matrix. Results of this study supported the hypothesis as
the model had 75% accuracy over 10,000 images (data), highlighting the
possibility of AIs that accurately analyze human emotions and the prospect of
viable Emotional AIs. | [
"Ashley Jisue Hong",
"David DiStefano",
"Sejal Dua"
] | 2023-10-14 02:44:44 | http://arxiv.org/abs/2310.09473v1 | http://arxiv.org/pdf/2310.09473v1 | 2310.09473v1 |
Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems | In the field of quantum information, classical optimizers play an important
role. From experimentalists optimizing their physical devices to theorists
exploring variational quantum algorithms, many aspects of quantum information
require the use of a classical optimizer. For this reason, there are many
papers that benchmark the effectiveness of different optimizers for specific
quantum optimization tasks and choices of parameterized algorithms. However,
for researchers exploring new algorithms or physical devices, the insights from
these studies don't necessarily translate. To address this concern, we compare
the performance of classical optimizers across a series of partially-randomized
tasks to more broadly sample the space of quantum optimization problems. We
focus on local zeroth-order optimizers due to their generally favorable
performance and query-efficiency on quantum systems. We discuss insights from
these experiments that can help motivate future works to improve these
optimizers for use on quantum systems. | [
"Lucas Tecot",
"Cho-Jui Hsieh"
] | 2023-10-14 02:13:26 | http://arxiv.org/abs/2310.09468v1 | http://arxiv.org/pdf/2310.09468v1 | 2310.09468v1 |
A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading | Despite advances in artificial intelligence-enhanced trading methods,
developing a profitable automated trading system remains challenging in the
rapidly evolving cryptocurrency market. This study aims to address these
challenges by developing a reinforcement learning-based automated trading
system for five popular altcoins~(cryptocurrencies other than Bitcoin): Binance
Coin, Ethereum, Litecoin, Ripple, and Tether. To this end, we present
CausalReinforceNet, a framework framed as a decision support system. Designed
as the foundational architecture of the trading system, the CausalReinforceNet
framework enhances the capabilities of the reinforcement learning agent through
causal analysis. Within this framework, we use Bayesian networks in the feature
engineering process to identify the most relevant features with causal
relationships that influence cryptocurrency price movements. Additionally, we
incorporate probabilistic price direction signals from dynamic Bayesian
networks to enhance our reinforcement learning agent's decision-making. Due to
the high volatility of the cryptocurrency market, we design our framework to
adopt a conservative approach that limits sell and buy position sizes to manage
risk. We develop two agents using the CausalReinforceNet framework, each based
on distinct reinforcement learning algorithms. The results indicate that our
framework substantially surpasses the Buy-and-Hold benchmark strategy in
profitability. Additionally, both agents generated notable returns on
investment for Binance Coin and Ethereum. | [
"Rasoul Amirzadeh",
"Dhananjay Thiruvady",
"Asef Nazari",
"Mong Shan Ee"
] | 2023-10-14 01:08:52 | http://arxiv.org/abs/2310.09462v1 | http://arxiv.org/pdf/2310.09462v1 | 2310.09462v1 |
Large Language Model Unlearning | We study how to perform unlearning, i.e. forgetting undesirable
(mis)behaviors, on large language models (LLMs). We show at least three
scenarios of aligning LLMs with human preferences can benefit from unlearning:
(1) removing harmful responses, (2) erasing copyright-protected content as
requested, and (3) eliminating hallucinations. Unlearning, as an alignment
technique, has three advantages. (1) It only requires negative (e.g. harmful)
examples, which are much easier and cheaper to collect (e.g. via red teaming or
user reporting) than positive (e.g. helpful and often human-written) examples
required in RLHF (RL from human feedback). (2) It is computationally efficient.
(3) It is especially effective when we know which training samples cause the
misbehavior. To the best of our knowledge, our work is among the first to
explore LLM unlearning. We are also among the first to formulate the settings,
goals, and evaluations in LLM unlearning. We show that if practitioners only
have limited resources, and therefore the priority is to stop generating
undesirable outputs rather than to try to generate desirable outputs,
unlearning is particularly appealing. Despite only having negative samples, our
ablation study shows that unlearning can still achieve better alignment
performance than RLHF with just 2% of its computational time. | [
"Yuanshun Yao",
"Xiaojun Xu",
"Yang Liu"
] | 2023-10-14 00:32:55 | http://arxiv.org/abs/2310.10683v1 | http://arxiv.org/pdf/2310.10683v1 | 2310.10683v1 |
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents | Recent advancements in reasoning abilities of Large Language Models (LLM) has
promoted their usage in problems that require high-level planning for robots
and artificial agents. However, current techniques that utilize LLMs for such
planning tasks make certain key assumptions such as, access to datasets that
permit finetuning, meticulously engineered prompts that only provide relevant
and essential information to the LLM, and most importantly, a deterministic
approach to allow execution of the LLM responses either in the form of existing
policies or plan operators. In this work, we propose LgTS (LLM-guided
Teacher-Student learning), a novel approach that explores the planning
abilities of LLMs to provide a graphical representation of the sub-goals to a
reinforcement learning (RL) agent that does not have access to the transition
dynamics of the environment. The RL agent uses Teacher-Student learning
algorithm to learn a set of successful policies for reaching the goal state
from the start state while simultaneously minimizing the number of
environmental interactions. Unlike previous methods that utilize LLMs, our
approach does not assume access to a propreitary or a fine-tuned LLM, nor does
it require pre-trained policies that achieve the sub-goals proposed by the LLM.
Through experiments on a gridworld based DoorKey domain and a search-and-rescue
inspired domain, we show that generating a graphical structure of sub-goals
helps in learning policies for the LLM proposed sub-goals and the
Teacher-Student learning algorithm minimizes the number of environment
interactions when the transition dynamics are unknown. | [
"Yash Shukla",
"Wenchang Gao",
"Vasanth Sarathy",
"Alvaro Velasquez",
"Robert Wright",
"Jivko Sinapov"
] | 2023-10-14 00:07:03 | http://arxiv.org/abs/2310.09454v1 | http://arxiv.org/pdf/2310.09454v1 | 2310.09454v1 |
Pairwise Similarity Learning is SimPLE | In this paper, we focus on a general yet important learning problem, pairwise
similarity learning (PSL). PSL subsumes a wide range of important applications,
such as open-set face recognition, speaker verification, image retrieval and
person re-identification. The goal of PSL is to learn a pairwise similarity
function assigning a higher similarity score to positive pairs (i.e., a pair of
samples with the same label) than to negative pairs (i.e., a pair of samples
with different label). We start by identifying a key desideratum for PSL, and
then discuss how existing methods can achieve this desideratum. We then propose
a surprisingly simple proxy-free method, called SimPLE, which requires neither
feature/proxy normalization nor angular margin and yet is able to generalize
well in open-set recognition. We apply the proposed method to three challenging
PSL tasks: open-set face recognition, image retrieval and speaker verification.
Comprehensive experimental results on large-scale benchmarks show that our
method performs significantly better than current state-of-the-art methods. | [
"Yandong Wen",
"Weiyang Liu",
"Yao Feng",
"Bhiksha Raj",
"Rita Singh",
"Adrian Weller",
"Michael J. Black",
"Bernhard Schölkopf"
] | 2023-10-13 23:56:47 | http://arxiv.org/abs/2310.09449v1 | http://arxiv.org/pdf/2310.09449v1 | 2310.09449v1 |
G10: Enabling An Efficient Unified GPU Memory and Storage Architecture with Smart Tensor Migrations | To break the GPU memory wall for scaling deep learning workloads, a variety
of architecture and system techniques have been proposed recently. Their
typical approaches include memory extension with flash memory and direct
storage access. However, these techniques still suffer from suboptimal
performance and introduce complexity to the GPU memory management, making them
hard to meet the scalability requirement of deep learning workloads today. In
this paper, we present a unified GPU memory and storage architecture named G10
driven by the fact that the tensor behaviors of deep learning workloads are
highly predictable. G10 integrates the host memory, GPU memory, and flash
memory into a unified memory space, to scale the GPU memory capacity while
enabling transparent data migrations. Based on this unified GPU memory and
storage architecture, G10 utilizes compiler techniques to characterize the
tensor behaviors in deep learning workloads. Therefore, it can schedule data
migrations in advance by considering the available bandwidth of flash memory
and host memory. The cooperative mechanism between deep learning compilers and
the unified memory architecture enables G10 to hide data transfer overheads in
a transparent manner. We implement G10 based on an open-source GPU simulator.
Our experiments demonstrate that G10 outperforms state-of-the-art GPU memory
solutions by up to 1.75$\times$, without code modifications to deep learning
workloads. With the smart data migration mechanism, G10 can reach 90.3\% of the
performance of the ideal case assuming unlimited GPU memory. | [
"Haoyang Zhang",
"Yirui Eric Zhou",
"Yuqi Xue",
"Yiqi Liu",
"Jian Huang"
] | 2023-10-13 23:32:28 | http://arxiv.org/abs/2310.09443v1 | http://arxiv.org/pdf/2310.09443v1 | 2310.09443v1 |
Target Variable Engineering | How does the formulation of a target variable affect performance within the
ML pipeline? The experiments in this study examine numeric targets that have
been binarized by comparing against a threshold. We compare the predictive
performance of regression models trained to predict the numeric targets vs.
classifiers trained to predict their binarized counterparts. Specifically, we
make this comparison at every point of a randomized hyperparameter optimization
search to understand the effect of computational resource budget on the
tradeoff between the two. We find that regression requires significantly more
computational effort to converge upon the optimal performance, and is more
sensitive to both randomness and heuristic choices in the training process.
Although classification can and does benefit from systematic hyperparameter
tuning and model selection, the improvements are much less than for regression.
This work comprises the first systematic comparison of regression and
classification within the framework of computational resource requirements. Our
findings contribute to calls for greater replicability and efficiency within
the ML pipeline for the sake of building more sustainable and robust AI
systems. | [
"Jessica Clark"
] | 2023-10-13 23:12:21 | http://arxiv.org/abs/2310.09440v1 | http://arxiv.org/pdf/2310.09440v1 | 2310.09440v1 |
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks | Continual learning (CL) has two main objectives: preventing catastrophic
forgetting (CF) and encouraging knowledge transfer (KT). The existing
literature mainly focused on overcoming CF. Some work has also been done on KT
when the tasks are similar. To our knowledge, only one method has been proposed
to learn a sequence of mixed tasks. However, these techniques still suffer from
CF and/or limited KT. This paper proposes a new CL method to achieve both. It
overcomes CF by isolating the knowledge of each task via discovering a
subnetwork for it. A soft-masking mechanism is also proposed to preserve the
previous knowledge and to enable the new task to leverage the past knowledge to
achieve KT. Experiments using classification, generation, information
extraction, and their mixture (i.e., heterogeneous tasks) show that the
proposed method consistently outperforms strong baselines. | [
"Zixuan Ke",
"Bing Liu",
"Wenhan Xiong",
"Asli Celikyilmaz",
"Haoran Li"
] | 2023-10-13 23:00:39 | http://arxiv.org/abs/2310.09436v1 | http://arxiv.org/pdf/2310.09436v1 | 2310.09436v1 |
Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations | In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent
Neural Networks) to learn and represent nonlinear integral operators that
appear in nonlinear integro-differential equations (IDEs). The LSTM-RNN
representation of the nonlinear integral operator allows us to turn a system of
nonlinear integro-differential equations into a system of ordinary differential
equations for which many efficient solvers are available. Furthermore, because
the use of LSTM-RNN representation of the nonlinear integral operator in an IDE
eliminates the need to perform a numerical integration in each numerical time
evolution step, the overall temporal cost of the LSTM-RNN-based IDE solver can
be reduced to $O(n_T)$ from $O(n_T^2)$ if a $n_T$-step trajectory is to be
computed. We illustrate the efficiency and robustness of this LSTM-RNN-based
numerical IDE solver with a model problem. Additionally, we highlight the
generalizability of the learned integral operator by applying it to IDEs driven
by different external forces. As a practical application, we show how this
methodology can effectively solve the Dyson's equation for quantum many-body
systems. | [
"Hardeep Bassi",
"Yuanran Zhu",
"Senwei Liang",
"Jia Yin",
"Cian C. Reeves",
"Vojtech Vlcek",
"Chao Yang"
] | 2023-10-13 22:57:46 | http://arxiv.org/abs/2310.09434v1 | http://arxiv.org/pdf/2310.09434v1 | 2310.09434v1 |
Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing | Microring resonators (MRRs) are promising devices for time-delay photonic
reservoir computing, but the impact of the different physical effects taking
place in the MRRs on the reservoir computing performance is yet to be fully
understood. We numerically analyze the impact of linear losses as well as
thermo-optic and free-carrier effects relaxation times on the prediction error
of the time-series task NARMA-10. We demonstrate the existence of three
regions, defined by the input power and the frequency detuning between the
optical source and the microring resonance, that reveal the cavity transition
from linear to nonlinear regimes. One of these regions offers very low error in
time-series prediction under relatively low input power and number of nodes
while the other regions either lack nonlinearity or become unstable. This study
provides insight into the design of the MRR and the optimization of its
physical properties for improving the prediction performance of time-delay
reservoir computing. | [
"Bernard J. Giron Castro",
"Christophe Peucheret",
"Darko Zibar",
"Francesco Da Ros"
] | 2023-10-13 22:48:50 | http://arxiv.org/abs/2310.09433v1 | http://arxiv.org/pdf/2310.09433v1 | 2310.09433v1 |
Enhancing BERT-Based Visual Question Answering through Keyword-Driven Sentence Selection | The Document-based Visual Question Answering competition addresses the
automatic detection of parent-child relationships between elements in
multi-page documents. The goal is to identify the document elements that answer
a specific question posed in natural language. This paper describes the
PoliTo's approach to addressing this task, in particular, our best solution
explores a text-only approach, leveraging an ad hoc sampling strategy.
Specifically, our approach leverages the Masked Language Modeling technique to
fine-tune a BERT model, focusing on sentences containing sensitive keywords
that also occur in the questions, such as references to tables or images.
Thanks to the effectiveness of this approach, we are able to achieve high
performance compared to baselines, demonstrating how our solution contributes
positively to this task. | [
"Davide Napolitano",
"Lorenzo Vaiani",
"Luca Cagliero"
] | 2023-10-13 22:43:55 | http://arxiv.org/abs/2310.09432v1 | http://arxiv.org/pdf/2310.09432v1 | 2310.09432v1 |
Offline Reinforcement Learning for Optimizing Production Bidding Policies | The online advertising market, with its thousands of auctions run per second,
presents a daunting challenge for advertisers who wish to optimize their spend
under a budget constraint. Thus, advertising platforms typically provide
automated agents to their customers, which act on their behalf to bid for
impression opportunities in real time at scale. Because these proxy agents are
owned by the platform but use advertiser funds to operate, there is a strong
practical need to balance reliability and explainability of the agent with
optimizing power. We propose a generalizable approach to optimizing bidding
policies in production environments by learning from real data using offline
reinforcement learning. This approach can be used to optimize any
differentiable base policy (practically, a heuristic policy based on principles
which the advertiser can easily understand), and only requires data generated
by the base policy itself. We use a hybrid agent architecture that combines
arbitrary base policies with deep neural networks, where only the optimized
base policy parameters are eventually deployed, and the neural network part is
discarded after training. We demonstrate that such an architecture achieves
statistically significant performance gains in both simulated and at-scale
production bidding environments. Our approach does not incur additional
infrastructure, safety, or explainability costs, as it directly optimizes
parameters of existing production routines without replacing them with black
box-style models like neural networks. | [
"Dmytro Korenkevych",
"Frank Cheng",
"Artsiom Balakir",
"Alex Nikulkov",
"Lingnan Gao",
"Zhihao Cen",
"Zuobing Xu",
"Zheqing Zhu"
] | 2023-10-13 22:14:51 | http://arxiv.org/abs/2310.09426v1 | http://arxiv.org/pdf/2310.09426v1 | 2310.09426v1 |
ZeroSwap: Data-driven Optimal Market Making in DeFi | Automated Market Makers (AMMs) are major centers of matching liquidity supply
and demand in Decentralized Finance. Their functioning relies primarily on the
presence of liquidity providers (LPs) incentivized to invest their assets into
a liquidity pool. However, the prices at which a pooled asset is traded is
often more stale than the prices on centralized and more liquid exchanges. This
leads to the LPs suffering losses to arbitrage. This problem is addressed by
adapting market prices to trader behavior, captured via the classical market
microstructure model of Glosten and Milgrom. In this paper, we propose the
first optimal Bayesian and the first model-free data-driven algorithm to
optimally track the external price of the asset. The notion of optimality that
we use enforces a zero-profit condition on the prices of the market maker,
hence the name ZeroSwap. This ensures that the market maker balances losses to
informed traders with profits from noise traders. The key property of our
approach is the ability to estimate the external market price without the need
for price oracles or loss oracles. Our theoretical guarantees on the
performance of both these algorithms, ensuring the stability and convergence of
their price recommendations, are of independent interest in the theory of
reinforcement learning. We empirically demonstrate the robustness of our
algorithms to changing market conditions. | [
"Viraj Nadkarni",
"Jiachen Hu",
"Ranvir Rana",
"Chi Jin",
"Sanjeev Kulkarni",
"Pramod Viswanath"
] | 2023-10-13 21:28:19 | http://arxiv.org/abs/2310.09413v1 | http://arxiv.org/pdf/2310.09413v1 | 2310.09413v1 |
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks | This article addresses the pump-scheduling optimization problem to enhance
real-time control of real-world water distribution networks (WDNs). Our primary
objectives are to adhere to physical operational constraints while reducing
energy consumption and operational costs. Traditional optimization techniques,
such as evolution-based and genetic algorithms, often fall short due to their
lack of convergence guarantees. Conversely, reinforcement learning (RL) stands
out for its adaptability to uncertainties and reduced inference time, enabling
real-time responsiveness. However, the effective implementation of RL is
contingent on building accurate simulation models for WDNs, and prior
applications have been limited by errors in simulation training data. These
errors can potentially cause the RL agent to learn misleading patterns and
actions and recommend suboptimal operational strategies. To overcome these
challenges, we present an improved "hybrid RL" methodology. This method
integrates the benefits of RL while anchoring it in historical data, which
serves as a baseline to incrementally introduce optimal control
recommendations. By leveraging operational data as a foundation for the agent's
actions, we enhance the explainability of the agent's actions, foster more
robust recommendations, and minimize error. Our findings demonstrate that the
hybrid RL agent can significantly improve sustainability, operational
efficiency, and dynamically adapt to emerging scenarios in real-world WDNs. | [
"Harsh Patel",
"Yuan Zhou",
"Alexander P Lamb",
"Shu Wang",
"Jieliang Luo"
] | 2023-10-13 21:26:16 | http://arxiv.org/abs/2310.09412v1 | http://arxiv.org/pdf/2310.09412v1 | 2310.09412v1 |
Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review | In recent years, deep learning has revolutionized natural language processing
(NLP) by enabling the development of models that can learn complex
representations of language data, leading to significant improvements in
performance across a wide range of NLP tasks. Deep learning models for NLP
typically use large amounts of data to train deep neural networks, allowing
them to learn the patterns and relationships in language data. This is in
contrast to traditional NLP approaches, which rely on hand-engineered features
and rules to perform NLP tasks. The ability of deep neural networks to learn
hierarchical representations of language data, handle variable-length input
sequences, and perform well on large datasets makes them well-suited for NLP
applications. Driven by the exponential growth of textual data and the
increasing demand for condensed, coherent, and informative summaries, text
summarization has been a critical research area in the field of NLP. Applying
deep learning to text summarization refers to the use of deep neural networks
to perform text summarization tasks. In this survey, we begin with a review of
fashionable text summarization tasks in recent years, including extractive,
abstractive, multi-document, and so on. Next, we discuss most deep
learning-based models and their experimental results on these tasks. The paper
also covers datasets and data representation for summarization tasks. Finally,
we delve into the opportunities and challenges associated with summarization
tasks and their corresponding methodologies, aiming to inspire future research
efforts to advance the field further. A goal of our survey is to explain how
these methods differ in their requirements as understanding them is essential
for choosing a technique suited for a specific setting. | [
"Guanghua Wang",
"Weili Wu"
] | 2023-10-13 21:24:37 | http://arxiv.org/abs/2310.09411v1 | http://arxiv.org/pdf/2310.09411v1 | 2310.09411v1 |
Identifiability of Product of Experts Models | Product of experts (PoE) are layered networks in which the value at each node
is an AND (or product) of the values (possibly negated) at its inputs. These
were introduced as a neural network architecture that can efficiently learn to
generate high-dimensional data which satisfy many low-dimensional constraints
-- thereby allowing each individual expert to perform a simple task. PoEs have
found a variety of applications in learning.
We study the problem of identifiability of a product of experts model having
a layer of binary latent variables, and a layer of binary observables that are
iid conditional on the latents. The previous best upper bound on the number of
observables needed to identify the model was exponential in the number of
parameters. We show: (a) When the latents are uniformly distributed, the model
is identifiable with a number of observables equal to the number of parameters
(and hence best possible). (b) In the more general case of arbitrarily
distributed latents, the model is identifiable for a number of observables that
is still linear in the number of parameters (and within a factor of two of
best-possible). The proofs rely on root interlacing phenomena for some special
three-term recurrences. | [
"Spencer L. Gordon",
"Manav Kant",
"Eric Ma",
"Leonard J. Schulman",
"Andrei Staicu"
] | 2023-10-13 20:33:33 | http://arxiv.org/abs/2310.09397v1 | http://arxiv.org/pdf/2310.09397v1 | 2310.09397v1 |
Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication | Recent studies on semantic communication commonly rely on neural network (NN)
based transceivers such as deep joint source and channel coding (DeepJSCC).
Unlike traditional transceivers, these neural transceivers are trainable using
actual source data and channels, enabling them to extract and communicate
semantics. On the flip side, each neural transceiver is inherently biased
towards specific source data and channels, making different transceivers
difficult to understand intended semantics, particularly upon their initial
encounter. To align semantics over multiple neural transceivers, we propose a
distributed learning based solution, which leverages split learning (SL) and
partial NN fine-tuning techniques. In this method, referred to as SL with layer
freezing (SLF), each encoder downloads a misaligned decoder, and locally
fine-tunes a fraction of these encoder-decoder NN layers. By adjusting this
fraction, SLF controls computing and communication costs. Simulation results
confirm the effectiveness of SLF in aligning semantics under different source
data and channel dissimilarities, in terms of classification accuracy,
reconstruction errors, and recovery time for comprehending intended semantics
from misalignment. | [
"Jinhyuk Choi",
"Jihong Park",
"Seung-Woo Ko",
"Jinho Choi",
"Mehdi Bennis",
"Seong-Lyun Kim"
] | 2023-10-13 20:29:55 | http://arxiv.org/abs/2310.09394v1 | http://arxiv.org/pdf/2310.09394v1 | 2310.09394v1 |
Machine Learning Estimation of Maximum Vertical Velocity from Radar | Despite being the source region of severe weather hazards, the quantification
of the fast current of upward moving air (i.e., updraft) remains unavailable
for operational forecasting. Updraft proxies, like overshooting top area from
satellite images, have been linked to severe weather hazards but only relate to
a limited portion of the total storm updraft. This study investigates if a
machine learning model, namely U-Nets, can skillfully retrieve maximum vertical
velocity and its areal extent from 3-dimensional (3D) gridded radar
reflectivity alone. The machine learning model is trained using simulated radar
reflectivity and vertical velocity from the National Severe Storm Laboratory's
convection permitting Warn on Forecast System (WoFS). A parametric regression
technique using the Sinh-arcsinh-normal (SHASH) distribution is adapted to run
with UNets, allowing for both deterministic and probabilistic predictions of
maximum vertical velocity. The best models after hyperparameter search provided
less than 50% root mean squared error, a coefficient of determination greater
than 0.65 and an intersection over union (IoU) of more than 0.45 on the
independent test set composed of WoFS data. Beyond the WoFS analysis, a case
study was conducted using real radar data and corresponding dual-Doppler
analyses of vertical velocity within a supercell. The U-Net consistently
underestimates the dual-Doppler updraft speed estimates by 50%. Meanwhile, the
area of the 5 and 10 m s-1 updraft cores show an IoU of 0.25. While the above
statistics are not exceptional, the machine learning model enables quick
distillation of 3D radar data that is related to the maximum vertical velocity
which could be useful in assessing a storm's severe potential. | [
"Randy J. Chase",
"Amy McGovern",
"Cameron Homeyer",
"Peter Marinescu",
"Corey Potvin"
] | 2023-10-13 20:26:55 | http://arxiv.org/abs/2310.09392v1 | http://arxiv.org/pdf/2310.09392v1 | 2310.09392v1 |
CORN: Co-Trained Full-Reference And No-Reference Audio Metrics | Perceptual evaluation constitutes a crucial aspect of various
audio-processing tasks. Full reference (FR) or similarity-based metrics rely on
high-quality reference recordings, to which lower-quality or corrupted versions
of the recording may be compared for evaluation. In contrast, no-reference (NR)
metrics evaluate a recording without relying on a reference. Both the FR and NR
approaches exhibit advantages and drawbacks relative to each other. In this
paper, we present a novel framework called CORN that amalgamates these dual
approaches, concurrently training both FR and NR models together. After
training, the models can be applied independently. We evaluate CORN by
predicting several common objective metrics and across two different
architectures. The NR model trained using CORN has access to a reference
recording during training, and thus, as one would expect, it consistently
outperforms baseline NR models trained independently. Perhaps even more
remarkable is that the CORN FR model also outperforms its baseline counterpart,
even though it relies on the same training data and the same model
architecture. Thus, a single training regime produces two independently useful
models, each outperforming independently trained models. | [
"Pranay Manocha",
"Donald Williamson",
"Adam Finkelstein"
] | 2023-10-13 20:17:44 | http://arxiv.org/abs/2310.09388v1 | http://arxiv.org/pdf/2310.09388v1 | 2310.09388v1 |
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations | In this paper we introduce learnable lattice vector quantization and
demonstrate its effectiveness for learning discrete representations. Our
method, termed LL-VQ-VAE, replaces the vector quantization layer in VQ-VAE with
lattice-based discretization. The learnable lattice imposes a structure over
all discrete embeddings, acting as a deterrent against codebook collapse,
leading to high codebook utilization. Compared to VQ-VAE, our method obtains
lower reconstruction errors under the same training conditions, trains in a
fraction of the time, and with a constant number of parameters (equal to the
embedding dimension $D$), making it a very scalable approach. We demonstrate
these results on the FFHQ-1024 dataset and include FashionMNIST and Celeb-A. | [
"Ahmed Khalil",
"Robert Piechocki",
"Raul Santos-Rodriguez"
] | 2023-10-13 20:03:18 | http://arxiv.org/abs/2310.09382v1 | http://arxiv.org/pdf/2310.09382v1 | 2310.09382v1 |
Identifying and examining machine learning biases on Adult dataset | This research delves into the reduction of machine learning model bias
through Ensemble Learning. Our rigorous methodology comprehensively assesses
bias across various categorical variables, ultimately revealing a pronounced
gender attribute bias. The empirical evidence unveils a substantial
gender-based wage prediction disparity: wages predicted for males, initially at
\$902.91, significantly decrease to \$774.31 when the gender attribute is
alternated to females. Notably, Kullback-Leibler divergence scores point to
gender bias, with values exceeding 0.13, predominantly within tree-based
models. Employing Ensemble Learning elucidates the quest for fairness and
transparency. Intriguingly, our findings reveal that the stacked model aligns
with individual models, confirming the resilience of model bias. This study
underscores ethical considerations and advocates the implementation of hybrid
models for a data-driven society marked by impartiality and inclusivity. | [
"Sahil Girhepuje"
] | 2023-10-13 19:41:47 | http://arxiv.org/abs/2310.09373v1 | http://arxiv.org/pdf/2310.09373v1 | 2310.09373v1 |
From Words and Exercises to Wellness: Farsi Chatbot for Self-Attachment Technique | In the wake of the post-pandemic era, marked by social isolation and surging
rates of depression and anxiety, conversational agents based on digital
psychotherapy can play an influential role compared to traditional therapy
sessions. In this work, we develop a voice-capable chatbot in Farsi to guide
users through Self-Attachment (SAT), a novel, self-administered, holistic
psychological technique based on attachment theory. Our chatbot uses a dynamic
array of rule-based and classification-based modules to comprehend user input
throughout the conversation and navigates a dialogue flowchart accordingly,
recommending appropriate SAT exercises that depend on the user's emotional and
mental state. In particular, we collect a dataset of over 6,000 utterances and
develop a novel sentiment-analysis module that classifies user sentiment into
12 classes, with accuracy above 92%. To keep the conversation novel and
engaging, the chatbot's responses are retrieved from a large dataset of
utterances created with the aid of Farsi GPT-2 and a reinforcement learning
approach, thus requiring minimal human annotation. Our chatbot also offers a
question-answering module, called SAT Teacher, to answer users' questions about
the principles of Self-Attachment. Finally, we design a cross-platform
application as the bot's user interface. We evaluate our platform in a ten-day
human study with N=52 volunteers from the non-clinical population, who have had
over 2,000 dialogues in total with the chatbot. The results indicate that the
platform was engaging to most users (75%), 72% felt better after the
interactions, and 74% were satisfied with the SAT Teacher's performance. | [
"Sina Elahimanesh",
"Shayan Salehi",
"Sara Zahedi Movahed",
"Lisa Alazraki",
"Ruoyu Hu",
"Abbas Edalat"
] | 2023-10-13 19:09:31 | http://arxiv.org/abs/2310.09362v1 | http://arxiv.org/pdf/2310.09362v1 | 2310.09362v1 |
Is Certifying $\ell_p$ Robustness Still Worthwhile? | Over the years, researchers have developed myriad attacks that exploit the
ubiquity of adversarial examples, as well as defenses that aim to guard against
the security vulnerabilities posed by such attacks. Of particular interest to
this paper are defenses that provide provable guarantees against the class of
$\ell_p$-bounded attacks. Certified defenses have made significant progress,
taking robustness certification from toy models and datasets to large-scale
problems like ImageNet classification. While this is undoubtedly an interesting
academic problem, as the field has matured, its impact in practice remains
unclear, thus we find it useful to revisit the motivation for continuing this
line of research. There are three layers to this inquiry, which we address in
this paper: (1) why do we care about robustness research? (2) why do we care
about the $\ell_p$-bounded threat model? And (3) why do we care about
certification as opposed to empirical defenses? In brief, we take the position
that local robustness certification indeed confers practical value to the field
of machine learning. We focus especially on the latter two questions from
above. With respect to the first of the two, we argue that the $\ell_p$-bounded
threat model acts as a minimal requirement for safe application of models in
security-critical domains, while at the same time, evidence has mounted
suggesting that local robustness may lead to downstream external benefits not
immediately related to robustness. As for the second, we argue that (i)
certification provides a resolution to the cat-and-mouse game of adversarial
attacks; and furthermore, that (ii) perhaps contrary to popular belief, there
may not exist a fundamental trade-off between accuracy, robustness, and
certifiability, while moreover, certified training techniques constitute a
particularly promising way for learning robust models. | [
"Ravi Mangal",
"Klas Leino",
"Zifan Wang",
"Kai Hu",
"Weicheng Yu",
"Corina Pasareanu",
"Anupam Datta",
"Matt Fredrikson"
] | 2023-10-13 19:08:21 | http://arxiv.org/abs/2310.09361v1 | http://arxiv.org/pdf/2310.09361v1 | 2310.09361v1 |
Exact Verification of ReLU Neural Control Barrier Functions | Control Barrier Functions (CBFs) are a popular approach for safe control of
nonlinear systems. In CBF-based control, the desired safety properties of the
system are mapped to nonnegativity of a CBF, and the control input is chosen to
ensure that the CBF remains nonnegative for all time. Recently, machine
learning methods that represent CBFs as neural networks (neural control barrier
functions, or NCBFs) have shown great promise due to the universal
representability of neural networks. However, verifying that a learned CBF
guarantees safety remains a challenging research problem. This paper presents
novel exact conditions and algorithms for verifying safety of feedforward NCBFs
with ReLU activation functions. The key challenge in doing so is that, due to
the piecewise linearity of the ReLU function, the NCBF will be
nondifferentiable at certain points, thus invalidating traditional safety
verification methods that assume a smooth barrier function. We resolve this
issue by leveraging a generalization of Nagumo's theorem for proving invariance
of sets with nonsmooth boundaries to derive necessary and sufficient conditions
for safety. Based on this condition, we propose an algorithm for safety
verification of NCBFs that first decomposes the NCBF into piecewise linear
segments and then solves a nonlinear program to verify safety of each segment
as well as the intersections of the linear segments. We mitigate the complexity
by only considering the boundary of the safe region and by pruning the segments
with Interval Bound Propagation (IBP) and linear relaxation. We evaluate our
approach through numerical studies with comparison to state-of-the-art
SMT-based methods. Our code is available at
https://github.com/HongchaoZhang-HZ/exactverif-reluncbf-nips23. | [
"Hongchao Zhang",
"Junlin Wu",
"Yevgeniy Vorobeychik",
"Andrew Clark"
] | 2023-10-13 18:59:04 | http://arxiv.org/abs/2310.09360v1 | http://arxiv.org/pdf/2310.09360v1 | 2310.09360v1 |
When are Bandits Robust to Misspecification? | Parametric feature-based reward models are widely employed by algorithms for
decision making settings such as bandits and contextual bandits. The typical
assumption under which they are analysed is realizability, i.e., that the true
rewards of actions are perfectly explained by some parametric model in the
class. We are, however, interested in the situation where the true rewards are
(potentially significantly) misspecified with respect to the model class. For
parameterized bandits and contextual bandits, we identify sufficient
conditions, depending on the problem instance and model class, under which
classic algorithms such as $\epsilon$-greedy and LinUCB enjoy sublinear (in the
time horizon) regret guarantees under even grossly misspecified rewards. This
is in contrast to existing worst-case results for misspecified bandits which
show regret bounds that scale linearly with time, and shows that there can be a
nontrivially large set of bandit instances that are robust to misspecification. | [
"Debangshu Banerjee",
"Aditya Gopalan"
] | 2023-10-13 18:53:30 | http://arxiv.org/abs/2310.09358v1 | http://arxiv.org/pdf/2310.09358v1 | 2310.09358v1 |
Uncertainty Quantification using Generative Approach | We present the Incremental Generative Monte Carlo (IGMC) method, designed to
measure uncertainty in deep neural networks using deep generative approaches.
IGMC iteratively trains generative models, adding their output to the dataset,
to compute the posterior distribution of the expectation of a random variable.
We provide a theoretical guarantee of the convergence rate of IGMC relative to
the sample size and sampling depth. Due to its compatibility with deep
generative approaches, IGMC is adaptable to both neural network classification
and regression tasks. We empirically study the behavior of IGMC on the MNIST
digit classification task. | [
"Yunsheng Zhang"
] | 2023-10-13 18:05:25 | http://arxiv.org/abs/2310.09338v1 | http://arxiv.org/pdf/2310.09338v1 | 2310.09338v1 |
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task | Modern generative models exhibit unprecedented capabilities to generate
extremely realistic data. However, given the inherent compositionality of the
real world, reliable use of these models in practical applications requires
that they exhibit the capability to compose a novel set of concepts to generate
outputs not seen in the training data set. Prior work demonstrates that recent
diffusion models do exhibit intriguing compositional generalization abilities,
but also fail unpredictably. Motivated by this, we perform a controlled study
for understanding compositional generalization in conditional diffusion models
in a synthetic setting, varying different attributes of the training data and
measuring the model's ability to generate samples out-of-distribution. Our
results show: (i) the order in which the ability to generate samples from a
concept and compose them emerges is governed by the structure of the underlying
data-generating process; (ii) performance on compositional tasks exhibits a
sudden ``emergence'' due to multiplicative reliance on the performance of
constituent tasks, partially explaining emergent phenomena seen in generative
models; and (iii) composing concepts with lower frequency in the training data
to generate out-of-distribution samples requires considerably more optimization
steps compared to generating in-distribution samples. Overall, our study lays a
foundation for understanding capabilities and compositionality in generative
models from a data-centric perspective. | [
"Maya Okawa",
"Ekdeep Singh Lubana",
"Robert P. Dick",
"Hidenori Tanaka"
] | 2023-10-13 18:00:59 | http://arxiv.org/abs/2310.09336v1 | http://arxiv.org/pdf/2310.09336v1 | 2310.09336v1 |
Statistical guarantees for stochastic Metropolis-Hastings | A Metropolis-Hastings step is widely used for gradient-based Markov chain
Monte Carlo methods in uncertainty quantification. By calculating acceptance
probabilities on batches, a stochastic Metropolis-Hastings step saves
computational costs, but reduces the effective sample size. We show that this
obstacle can be avoided by a simple correction term. We study statistical
properties of the resulting stationary distribution of the chain if the
corrected stochastic Metropolis-Hastings approach is applied to sample from a
Gibbs posterior distribution in a nonparametric regression setting. Focusing on
deep neural network regression, we prove a PAC-Bayes oracle inequality which
yields optimal contraction rates and we analyze the diameter and show high
coverage probability of the resulting credible sets. With a numerical example
in a high-dimensional parameter space, we illustrate that credible sets and
contraction rates of the stochastic Metropolis-Hastings algorithm indeed behave
similar to those obtained from the classical Metropolis-adjusted Langevin
algorithm. | [
"Sebastian Bieringer",
"Gregor Kasieczka",
"Maximilian F. Steffen",
"Mathias Trabs"
] | 2023-10-13 18:00:26 | http://arxiv.org/abs/2310.09335v1 | http://arxiv.org/pdf/2310.09335v1 | 2310.09335v1 |
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning | In deep learning, auxiliary objectives are often used to facilitate learning
in situations where data is scarce, or the principal task is extremely complex.
This idea is primarily inspired by the improved generalization capability
induced by solving multiple tasks simultaneously, which leads to a more robust
shared representation. Nevertheless, finding optimal auxiliary tasks that give
rise to the desired improvement is a crucial problem that often requires
hand-crafted solutions or expensive meta-learning approaches. In this paper, we
propose a novel framework, dubbed Detaux, whereby a weakly supervised
disentanglement procedure is used to discover new unrelated classification
tasks and the associated labels that can be exploited with the principal task
in any Multi-Task Learning (MTL) model. The disentanglement procedure works at
a representation level, isolating a subspace related to the principal task,
plus an arbitrary number of orthogonal subspaces. In the most disentangled
subspaces, through a clustering procedure, we generate the additional
classification tasks, and the associated labels become their representatives.
Subsequently, the original data, the labels associated with the principal task,
and the newly discovered ones can be fed into any MTL framework. Extensive
validation on both synthetic and real data, along with various ablation
studies, demonstrate promising results, revealing the potential in what has
been, so far, an unexplored connection between learning disentangled
representations and MTL. The code will be made publicly available upon
acceptance. | [
"Geri Skenderi",
"Luigi Capogrosso",
"Andrea Toaiari",
"Matteo Denitto",
"Franco Fummi",
"Simone Melzi",
"Marco Cristani"
] | 2023-10-13 17:40:39 | http://arxiv.org/abs/2310.09278v1 | http://arxiv.org/pdf/2310.09278v1 | 2310.09278v1 |
A Hybrid Approach for Depression Classification: Random Forest-ANN Ensemble on Motor Activity Signals | Regarding the rising number of people suffering from mental health illnesses
in today's society, the importance of mental health cannot be overstated.
Wearable sensors, which are increasingly widely available, provide a potential
way to track and comprehend mental health issues. These gadgets not only
monitor everyday activities but also continuously record vital signs like heart
rate, perhaps providing information on a person's mental state. Recent research
has used these sensors in conjunction with machine learning methods to identify
patterns relating to different mental health conditions, highlighting the
immense potential of this data beyond simple activity monitoring. In this
research, we present a novel algorithm called the Hybrid Random forest - Neural
network that has been tailored to evaluate sensor data from depressed patients.
Our method has a noteworthy accuracy of 80\% when evaluated on a special
dataset that included both unipolar and bipolar depressive patients as well as
healthy controls. The findings highlight the algorithm's potential for reliably
determining a person's depression condition using sensor data, making a
substantial contribution to the area of mental health diagnostics. | [
"Anket Patil",
"Dhairya Shah",
"Abhishek Shah",
"Mokshit Gala"
] | 2023-10-13 17:39:35 | http://arxiv.org/abs/2310.09277v1 | http://arxiv.org/pdf/2310.09277v1 | 2310.09277v1 |
Subsets and Splits