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Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python | Machine learning (ML) has the potential to revolutionize a wide range of
research areas and industries, but many ML projects never progress past the
proof-of-concept stage. To address this issue, we introduce Model Share AI
(AIMS), an easy-to-use MLOps platform designed to streamline collaborative
model development, model provenance tracking, and model deployment, as well as
a host of other functions aiming to maximize the real-world impact of ML
research. AIMS features collaborative project spaces and a standardized model
evaluation process that ranks model submissions based on their performance on
unseen evaluation data, enabling collaborative model development and
crowd-sourcing. Model performance and various model metadata are automatically
captured to facilitate provenance tracking and allow users to learn from and
build on previous submissions. Additionally, AIMS allows users to deploy ML
models built in Scikit-Learn, TensorFlow Keras, PyTorch, and ONNX into live
REST APIs and automatically generated web apps with minimal code. The ability
to deploy models with minimal effort and to make them accessible to
non-technical end-users through web apps has the potential to make ML research
more applicable to real-world challenges. | [
"Heinrich Peters",
"Michael Parrott"
] | 2023-09-27 15:24:39 | http://arxiv.org/abs/2309.15719v1 | http://arxiv.org/pdf/2309.15719v1 | 2309.15719v1 |
Timbre-Trap: A Low-Resource Framework for Instrument-Agnostic Music Transcription | In recent years, research on music transcription has focused mainly on
architecture design and instrument-specific data acquisition. With the lack of
availability of diverse datasets, progress is often limited to solo-instrument
tasks such as piano transcription. Several works have explored multi-instrument
transcription as a means to bolster the performance of models on low-resource
tasks, but these methods face the same data availability issues. We propose
Timbre-Trap, a novel framework which unifies music transcription and audio
reconstruction by exploiting the strong separability between pitch and timbre.
We train a single U-Net to simultaneously estimate pitch salience and
reconstruct complex spectral coefficients, selecting between either output
during the decoding stage via a simple switch mechanism. In this way, the model
learns to produce coefficients corresponding to timbre-less audio, which can be
interpreted as pitch salience. We demonstrate that the framework leads to
performance comparable to state-of-the-art instrument-agnostic transcription
methods, while only requiring a small amount of annotated data. | [
"Frank Cwitkowitz",
"Kin Wai Cheuk",
"Woosung Choi",
"Marco A. Martínez-Ramírez",
"Keisuke Toyama",
"Wei-Hsiang Liao",
"Yuki Mitsufuji"
] | 2023-09-27 15:19:05 | http://arxiv.org/abs/2309.15717v1 | http://arxiv.org/pdf/2309.15717v1 | 2309.15717v1 |
Maximum Weight Entropy | This paper deals with uncertainty quantification and out-of-distribution
detection in deep learning using Bayesian and ensemble methods. It proposes a
practical solution to the lack of prediction diversity observed recently for
standard approaches when used out-of-distribution (Ovadia et al., 2019; Liu et
al., 2021). Considering that this issue is mainly related to a lack of weight
diversity, we claim that standard methods sample in "over-restricted" regions
of the weight space due to the use of "over-regularization" processes, such as
weight decay and zero-mean centered Gaussian priors. We propose to solve the
problem by adopting the maximum entropy principle for the weight distribution,
with the underlying idea to maximize the weight diversity. Under this paradigm,
the epistemic uncertainty is described by the weight distribution of maximal
entropy that produces neural networks "consistent" with the training
observations. Considering stochastic neural networks, a practical optimization
is derived to build such a distribution, defined as a trade-off between the
average empirical risk and the weight distribution entropy. We develop a novel
weight parameterization for the stochastic model, based on the singular value
decomposition of the neural network's hidden representations, which enables a
large increase of the weight entropy for a small empirical risk penalization.
We provide both theoretical and numerical results to assess the efficiency of
the approach. In particular, the proposed algorithm appears in the top three
best methods in all configurations of an extensive out-of-distribution
detection benchmark including more than thirty competitors. | [
"Antoine de Mathelin",
"François Deheeger",
"Mathilde Mougeot",
"Nicolas Vayatis"
] | 2023-09-27 14:46:10 | http://arxiv.org/abs/2309.15704v1 | http://arxiv.org/pdf/2309.15704v1 | 2309.15704v1 |
HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models | Advancements in deep neural networks have allowed automatic speech
recognition (ASR) systems to attain human parity on several publicly available
clean speech datasets. However, even state-of-the-art ASR systems experience
performance degradation when confronted with adverse conditions, as a
well-trained acoustic model is sensitive to variations in the speech domain,
e.g., background noise. Intuitively, humans address this issue by relying on
their linguistic knowledge: the meaning of ambiguous spoken terms is usually
inferred from contextual cues thereby reducing the dependency on the auditory
system. Inspired by this observation, we introduce the first open-source
benchmark to utilize external large language models (LLMs) for ASR error
correction, where N-best decoding hypotheses provide informative elements for
true transcription prediction. This approach is a paradigm shift from the
traditional language model rescoring strategy that can only select one
candidate hypothesis as the output transcription. The proposed benchmark
contains a novel dataset, HyPoradise (HP), encompassing more than 334,000 pairs
of N-best hypotheses and corresponding accurate transcriptions across prevalent
speech domains. Given this dataset, we examine three types of error correction
techniques based on LLMs with varying amounts of labeled
hypotheses-transcription pairs, which gains a significant word error rate (WER)
reduction. Experimental evidence demonstrates the proposed technique achieves a
breakthrough by surpassing the upper bound of traditional re-ranking based
methods. More surprisingly, LLM with reasonable prompt and its generative
capability can even correct those tokens that are missing in N-best list. We
make our results publicly accessible for reproducible pipelines with released
pre-trained models, thus providing a new evaluation paradigm for ASR error
correction with LLMs. | [
"Chen Chen",
"Yuchen Hu",
"Chao-Han Huck Yang",
"Sabato Macro Siniscalchi",
"Pin-Yu Chen",
"Eng Siong Chng"
] | 2023-09-27 14:44:10 | http://arxiv.org/abs/2309.15701v2 | http://arxiv.org/pdf/2309.15701v2 | 2309.15701v2 |
Deep Model Fusion: A Survey | Deep model fusion/merging is an emerging technique that merges the parameters
or predictions of multiple deep learning models into a single one. It combines
the abilities of different models to make up for the biases and errors of a
single model to achieve better performance. However, deep model fusion on
large-scale deep learning models (e.g., LLMs and foundation models) faces
several challenges, including high computational cost, high-dimensional
parameter space, interference between different heterogeneous models, etc.
Although model fusion has attracted widespread attention due to its potential
to solve complex real-world tasks, there is still a lack of complete and
detailed survey research on this technique. Accordingly, in order to understand
the model fusion method better and promote its development, we present a
comprehensive survey to summarize the recent progress. Specifically, we
categorize existing deep model fusion methods as four-fold: (1) "Mode
connectivity", which connects the solutions in weight space via a path of
non-increasing loss, in order to obtain better initialization for model fusion;
(2) "Alignment" matches units between neural networks to create better
conditions for fusion; (3) "Weight average", a classical model fusion method,
averages the weights of multiple models to obtain more accurate results closer
to the optimal solution; (4) "Ensemble learning" combines the outputs of
diverse models, which is a foundational technique for improving the accuracy
and robustness of the final model. In addition, we analyze the challenges faced
by deep model fusion and propose possible research directions for model fusion
in the future. Our review is helpful in deeply understanding the correlation
between different model fusion methods and practical application methods, which
can enlighten the research in the field of deep model fusion. | [
"Weishi Li",
"Yong Peng",
"Miao Zhang",
"Liang Ding",
"Han Hu",
"Li Shen"
] | 2023-09-27 14:40:12 | http://arxiv.org/abs/2309.15698v1 | http://arxiv.org/pdf/2309.15698v1 | 2309.15698v1 |
A Unified View of Differentially Private Deep Generative Modeling | The availability of rich and vast data sources has greatly advanced machine
learning applications in various domains. However, data with privacy concerns
comes with stringent regulations that frequently prohibited data access and
data sharing. Overcoming these obstacles in compliance with privacy
considerations is key for technological progress in many real-world application
scenarios that involve privacy sensitive data. Differentially private (DP) data
publishing provides a compelling solution, where only a sanitized form of the
data is publicly released, enabling privacy-preserving downstream analysis and
reproducible research in sensitive domains. In recent years, various approaches
have been proposed for achieving privacy-preserving high-dimensional data
generation by private training on top of deep neural networks. In this paper,
we present a novel unified view that systematizes these approaches. Our view
provides a joint design space for systematically deriving methods that cater to
different use cases. We then discuss the strengths, limitations, and inherent
correlations between different approaches, aiming to shed light on crucial
aspects and inspire future research. We conclude by presenting potential paths
forward for the field of DP data generation, with the aim of steering the
community toward making the next important steps in advancing
privacy-preserving learning. | [
"Dingfan Chen",
"Raouf Kerkouche",
"Mario Fritz"
] | 2023-09-27 14:38:16 | http://arxiv.org/abs/2309.15696v1 | http://arxiv.org/pdf/2309.15696v1 | 2309.15696v1 |
Breaking NoC Anonymity using Flow Correlation Attack | Network-on-Chip (NoC) is widely used as the internal communication fabric in
today's multicore System-on-Chip (SoC) designs. Security of the on-chip
communication is crucial because exploiting any vulnerability in shared NoC
would be a goldmine for an attacker. NoC security relies on effective
countermeasures against diverse attacks. We investigate the security strength
of existing anonymous routing protocols in NoC architectures. Specifically,
this paper makes two important contributions. We show that the existing
anonymous routing is vulnerable to machine learning (ML) based flow correlation
attacks on NoCs. We propose a lightweight anonymous routing that use traffic
obfuscation techniques which can defend against ML-based flow correlation
attacks. Experimental studies using both real and synthetic traffic reveal that
our proposed attack is successful against state-of-the-art anonymous routing in
NoC architectures with a high accuracy (up to 99%) for diverse traffic
patterns, while our lightweight countermeasure can defend against ML-based
attacks with minor hardware and performance overhead. | [
"Hansika Weerasena",
"Pan Zhixin",
"Khushboo Rani",
"Prabhat Mishra"
] | 2023-09-27 14:32:39 | http://arxiv.org/abs/2309.15687v1 | http://arxiv.org/pdf/2309.15687v1 | 2309.15687v1 |
Projection based fuzzy least squares twin support vector machine for class imbalance problems | Class imbalance is a major problem in many real world classification tasks.
Due to the imbalance in the number of samples, the support vector machine (SVM)
classifier gets biased toward the majority class. Furthermore, these samples
are often observed with a certain degree of noise. Therefore, to remove these
problems we propose a novel fuzzy based approach to deal with class imbalanced
as well noisy datasets. We propose two approaches to address these problems.
The first approach is based on the intuitionistic fuzzy membership, termed as
robust energy-based intuitionistic fuzzy least squares twin support vector
machine (IF-RELSTSVM). Furthermore, we introduce the concept of
hyperplane-based fuzzy membership in our second approach, where the final
classifier is termed as robust energy-based fuzzy least square twin support
vector machine (F-RELSTSVM). By using this technique, the membership values are
based on a projection based approach, where the data points are projected on
the hyperplanes. The performance of the proposed algorithms is evaluated on
several benchmark and synthetic datasets. The experimental results show that
the proposed IF-RELSTSVM and F-RELSTSVM models outperform the baseline
algorithms. Statistical tests are performed to check the significance of the
proposed algorithms. The results show the applicability of the proposed
algorithms on noisy as well as imbalanced datasets. | [
"M. Tanveer",
"Ritik Mishra",
"Bharat Richhariya"
] | 2023-09-27 14:28:48 | http://arxiv.org/abs/2309.15886v1 | http://arxiv.org/pdf/2309.15886v1 | 2309.15886v1 |
Joint Sampling and Optimisation for Inverse Rendering | When dealing with difficult inverse problems such as inverse rendering, using
Monte Carlo estimated gradients to optimise parameters can slow down
convergence due to variance. Averaging many gradient samples in each iteration
reduces this variance trivially. However, for problems that require thousands
of optimisation iterations, the computational cost of this approach rises
quickly.
We derive a theoretical framework for interleaving sampling and optimisation.
We update and reuse past samples with low-variance finite-difference estimators
that describe the change in the estimated gradients between each iteration. By
combining proportional and finite-difference samples, we continuously reduce
the variance of our novel gradient meta-estimators throughout the optimisation
process. We investigate how our estimator interlinks with Adam and derive a
stable combination.
We implement our method for inverse path tracing and demonstrate how our
estimator speeds up convergence on difficult optimisation tasks. | [
"Martin Balint",
"Karol Myszkowski",
"Hans-Peter Seidel",
"Gurprit Singh"
] | 2023-09-27 14:21:13 | http://arxiv.org/abs/2309.15676v1 | http://arxiv.org/pdf/2309.15676v1 | 2309.15676v1 |
Speech collage: code-switched audio generation by collaging monolingual corpora | Designing effective automatic speech recognition (ASR) systems for
Code-Switching (CS) often depends on the availability of the transcribed CS
resources. To address data scarcity, this paper introduces Speech Collage, a
method that synthesizes CS data from monolingual corpora by splicing audio
segments. We further improve the smoothness quality of audio generation using
an overlap-add approach. We investigate the impact of generated data on speech
recognition in two scenarios: using in-domain CS text and a zero-shot approach
with synthesized CS text. Empirical results highlight up to 34.4% and 16.2%
relative reductions in Mixed-Error Rate and Word-Error Rate for in-domain and
zero-shot scenarios, respectively. Lastly, we demonstrate that CS augmentation
bolsters the model's code-switching inclination and reduces its monolingual
bias. | [
"Amir Hussein",
"Dorsa Zeinali",
"Ondřej Klejch",
"Matthew Wiesner",
"Brian Yan",
"Shammur Chowdhury",
"Ahmed Ali",
"Shinji Watanabe",
"Sanjeev Khudanpur"
] | 2023-09-27 14:17:53 | http://arxiv.org/abs/2309.15674v1 | http://arxiv.org/pdf/2309.15674v1 | 2309.15674v1 |
MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection | In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have
been increasingly popular in the Bangla language, which is the seventh most
spoken language throughout the entire world. However, the language is
structurally complicated, which makes this field arduous to extract emotions in
an accurate manner. Several distinct approaches such as the extraction of
positive and negative sentiments as well as multiclass emotions, have been
implemented in this field of study. Nevertheless, the extraction of multiple
sentiments is an almost untouched area in this language. Which involves
identifying several feelings based on a single piece of text. Therefore, this
study demonstrates a thorough method for constructing an annotated corpus based
on scrapped data from Facebook to bridge the gaps in this subject area to
overcome the challenges. To make this annotation more fruitful, the
context-based approach has been used. Bidirectional Encoder Representations
from Transformers (BERT), a well-known methodology of transformers, have been
shown the best results of all methods implemented. Finally, a web application
has been developed to demonstrate the performance of the pre-trained
top-performer model (BERT) for multi-label ER in Bangla. | [
"Sumit Kumar Banshal",
"Sajal Das",
"Shumaiya Akter Shammi",
"Narayan Ranjan Chakraborty"
] | 2023-09-27 14:10:57 | http://arxiv.org/abs/2309.15670v1 | http://arxiv.org/pdf/2309.15670v1 | 2309.15670v1 |
On Computational Entanglement and Its Interpretation in Adversarial Machine Learning | Adversarial examples in machine learning has emerged as a focal point of
research due to their remarkable ability to deceive models with seemingly
inconspicuous input perturbations, potentially resulting in severe
consequences. In this study, we embark on a comprehensive exploration of
adversarial machine learning models, shedding light on their intrinsic
complexity and interpretability. Our investigation reveals intriguing links
between machine learning model complexity and Einstein's theory of special
relativity, through the concept of entanglement. More specific, we define
entanglement computationally and demonstrate that distant feature samples can
exhibit strong correlations, akin to entanglement in quantum realm. This
revelation challenges conventional perspectives in describing the phenomenon of
adversarial transferability observed in contemporary machine learning models.
By drawing parallels with the relativistic effects of time dilation and length
contraction during computation, we gain deeper insights into adversarial
machine learning, paving the way for more robust and interpretable models in
this rapidly evolving field. | [
"YenLung Lai",
"Xingbo Dong",
"Zhe Jin"
] | 2023-09-27 14:09:15 | http://arxiv.org/abs/2309.15669v2 | http://arxiv.org/pdf/2309.15669v2 | 2309.15669v2 |
Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification | Federated learning enables multiple clients to collaboratively contribute to
the learning of a global model orchestrated by a central server. This learning
scheme promotes clients' data privacy and requires reduced communication
overheads. In an application like network traffic classification, this helps
hide the network vulnerabilities and weakness points. However, federated
learning is susceptible to backdoor attacks, in which adversaries inject
manipulated model updates into the global model. These updates inject a salient
functionality in the global model that can be launched with specific input
patterns. Nonetheless, the vulnerability of network traffic classification
models based on federated learning to these attacks remains unexplored. In this
paper, we propose GABAttack, a novel genetic algorithm-based backdoor attack
against federated learning for network traffic classification. GABAttack
utilizes a genetic algorithm to optimize the values and locations of backdoor
trigger patterns, ensuring a better fit with the input and the model. This
input-tailored dynamic attack is promising for improved attack evasiveness
while being effective. Extensive experiments conducted over real-world network
datasets validate the success of the proposed GABAttack in various situations
while maintaining almost invisible activity. This research serves as an
alarming call for network security experts and practitioners to develop robust
defense measures against such attacks. | [
"Mahmoud Nazzal",
"Nura Aljaafari",
"Ahmed Sawalmeh",
"Abdallah Khreishah",
"Muhammad Anan",
"Abdulelah Algosaibi",
"Mohammed Alnaeem",
"Adel Aldalbahi",
"Abdulaziz Alhumam",
"Conrado P. Vizcarra",
"Shadan Alhamed"
] | 2023-09-27 14:02:02 | http://arxiv.org/abs/2310.06855v1 | http://arxiv.org/pdf/2310.06855v1 | 2310.06855v1 |
Federated Deep Equilibrium Learning: A Compact Shared Representation for Edge Communication Efficiency | Federated Learning (FL) is a prominent distributed learning paradigm
facilitating collaboration among nodes within an edge network to co-train a
global model without centralizing data. By shifting computation to the network
edge, FL offers robust and responsive edge-AI solutions and enhance
privacy-preservation. However, deploying deep FL models within edge
environments is often hindered by communication bottlenecks, data
heterogeneity, and memory limitations. To address these challenges jointly, we
introduce FeDEQ, a pioneering FL framework that effectively employs deep
equilibrium learning and consensus optimization to exploit a compact shared
data representation across edge nodes, allowing the derivation of personalized
models specific to each node. We delve into a unique model structure composed
of an equilibrium layer followed by traditional neural network layers. Here,
the equilibrium layer functions as a global feature representation that edge
nodes can adapt to personalize their local layers. Capitalizing on FeDEQ's
compactness and representation power, we present a novel distributed algorithm
rooted in the alternating direction method of multipliers (ADMM) consensus
optimization and theoretically establish its convergence for smooth objectives.
Experiments across various benchmarks demonstrate that FeDEQ achieves
performance comparable to state-of-the-art personalized methods while employing
models of up to 4 times smaller in communication size and 1.5 times lower
memory footprint during training. | [
"Long Tan Le",
"Tuan Dung Nguyen",
"Tung-Anh Nguyen",
"Choong Seon Hong",
"Nguyen H. Tran"
] | 2023-09-27 13:48:12 | http://arxiv.org/abs/2309.15659v1 | http://arxiv.org/pdf/2309.15659v1 | 2309.15659v1 |
Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting | We explore the ability of large language models (LLMs) to act as speech
recognition post-processors that perform rescoring and error correction. Our
first focus is on instruction prompting to let LLMs perform these task without
fine-tuning, for which we evaluate different prompting schemes, both zero- and
few-shot in-context learning, and a novel task activation prompting method that
combines causal instructions and demonstration to increase its context windows.
Next, we show that rescoring only by in-context learning with frozen LLMs
achieves results that are competitive with rescoring by domain-tuned LMs, using
a pretrained first-pass recognition system and rescoring output on two
out-of-domain tasks (ATIS and WSJ). By combining prompting techniques with
fine-tuning we achieve error rates below the N-best oracle level, showcasing
the generalization power of the LLMs. | [
"Chao-Han Huck Yang",
"Yile Gu",
"Yi-Chieh Liu",
"Shalini Ghosh",
"Ivan Bulyko",
"Andreas Stolcke"
] | 2023-09-27 13:36:03 | http://arxiv.org/abs/2309.15649v2 | http://arxiv.org/pdf/2309.15649v2 | 2309.15649v2 |
SANGEA: Scalable and Attributed Network Generation | The topic of synthetic graph generators (SGGs) has recently received much
attention due to the wave of the latest breakthroughs in generative modelling.
However, many state-of-the-art SGGs do not scale well with the graph size.
Indeed, in the generation process, all the possible edges for a fixed number of
nodes must often be considered, which scales in $\mathcal{O}(N^2)$, with $N$
being the number of nodes in the graph. For this reason, many state-of-the-art
SGGs are not applicable to large graphs. In this paper, we present SANGEA, a
sizeable synthetic graph generation framework which extends the applicability
of any SGG to large graphs. By first splitting the large graph into
communities, SANGEA trains one SGG per community, then links the community
graphs back together to create a synthetic large graph. Our experiments show
that the graphs generated by SANGEA have high similarity to the original graph,
in terms of both topology and node feature distribution. Additionally, these
generated graphs achieve high utility on downstream tasks such as link
prediction. Finally, we provide a privacy assessment of the generated graphs to
show that, even though they have excellent utility, they also achieve
reasonable privacy scores. | [
"Valentin Lemaire",
"Youssef Achenchabe",
"Lucas Ody",
"Houssem Eddine Souid",
"Gianmarco Aversano",
"Nicolas Posocco",
"Sabri Skhiri"
] | 2023-09-27 13:35:45 | http://arxiv.org/abs/2309.15648v1 | http://arxiv.org/pdf/2309.15648v1 | 2309.15648v1 |
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems | Cold-start recommendation is one of the major challenges faced by recommender
systems (RS). Herein, we focus on the user cold-start problem. Recently,
methods utilizing side information or meta-learning have been used to model
cold-start users. However, it is difficult to deploy these methods to
industrial RS. There has not been much research that pays attention to the user
cold-start problem in the matching stage. In this paper, we propose Cold & Warm
Net based on expert models who are responsible for modeling cold-start and
warm-up users respectively. A gate network is applied to incorporate the
results from two experts. Furthermore, dynamic knowledge distillation acting as
a teacher selector is introduced to assist experts in better learning user
representation. With comprehensive mutual information, features highly relevant
to user behavior are selected for the bias net which explicitly models user
behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in
comparison to models commonly applied in the matching stage and it outperforms
other models on all user types. The proposed model has also been deployed on an
industrial short video platform and achieves a significant increase in app
dwell time and user retention rate. | [
"Xiangyu Zhang",
"Zongqiang Kuang",
"Zehao Zhang",
"Fan Huang",
"Xianfeng Tan"
] | 2023-09-27 13:31:43 | http://arxiv.org/abs/2309.15646v1 | http://arxiv.org/pdf/2309.15646v1 | 2309.15646v1 |
Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection? | State-of-the-art anomalous sound detection systems often utilize angular
margin losses to learn suitable representations of acoustic data using an
auxiliary task, which usually is a supervised or self-supervised classification
task. The underlying idea is that, in order to solve this auxiliary task,
specific information about normal data needs to be captured in the learned
representations and that this information is also sufficient to differentiate
between normal and anomalous samples. Especially in noisy conditions,
discriminative models based on angular margin losses tend to significantly
outperform systems based on generative or one-class models. The goal of this
work is to investigate why using angular margin losses with auxiliary tasks
works well for detecting anomalous sounds. To this end, it is shown, both
theoretically and experimentally, that minimizing angular margin losses also
minimizes compactness loss while inherently preventing learning trivial
solutions. Furthermore, multiple experiments are conducted to show that using a
related classification task as an auxiliary task teaches the model to learn
representations suitable for detecting anomalous sounds in noisy conditions.
Among these experiments are performance evaluations, visualizing the embedding
space with t-SNE and visualizing the input representations with respect to the
anomaly score using randomized input sampling for explanation. | [
"Kevin Wilkinghoff",
"Frank Kurth"
] | 2023-09-27 13:29:38 | http://arxiv.org/abs/2309.15643v1 | http://arxiv.org/pdf/2309.15643v1 | 2309.15643v1 |
Efficient tensor network simulation of IBM's largest quantum processors | We show how quantum-inspired 2d tensor networks can be used to efficiently
and accurately simulate the largest quantum processors from IBM, namely Eagle
(127 qubits), Osprey (433 qubits) and Condor (1121 qubits). We simulate the
dynamics of a complex quantum many-body system -- specifically, the kicked
Ising experiment considered recently by IBM in Nature 618, p. 500-505 (2023) --
using graph-based Projected Entangled Pair States (gPEPS), which was proposed
by some of us in PRB 99, 195105 (2019). Our results show that simple tensor
updates are already sufficient to achieve very large unprecedented accuracy
with remarkably low computational resources for this model. Apart from
simulating the original experiment for 127 qubits, we also extend our results
to 433 and 1121 qubits, and for evolution times around 8 times longer, thus
setting a benchmark for the newest IBM quantum machines. We also report
accurate simulations for infinitely-many qubits. Our results show that gPEPS
are a natural tool to efficiently simulate quantum computers with an underlying
lattice-based qubit connectivity, such as all quantum processors based on
superconducting qubits. | [
"Siddhartha Patra",
"Saeed S. Jahromi",
"Sukhbinder Singh",
"Roman Orus"
] | 2023-09-27 13:27:01 | http://arxiv.org/abs/2309.15642v2 | http://arxiv.org/pdf/2309.15642v2 | 2309.15642v2 |
Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices | This paper proposes a novel approach to hedging portfolios of risky assets
when financial markets are affected by financial turmoils. We introduce a
completely novel approach to diversification activity not on the level of
single assets but on the level of ensemble algorithmic investment strategies
(AIS) built based on the prices of these assets. We employ four types of
diverse theoretical models (LSTM - Long Short-Term Memory, ARIMA-GARCH -
Autoregressive Integrated Moving Average - Generalized Autoregressive
Conditional Heteroskedasticity, momentum, and contrarian) to generate price
forecasts, which are then used to produce investment signals in single and
complex AIS. In such a way, we are able to verify the diversification potential
of different types of investment strategies consisting of various assets
(energy commodities, precious metals, cryptocurrencies, or soft commodities) in
hedging ensemble AIS built for equity indices (S&P 500 index). Empirical data
used in this study cover the period between 2004 and 2022. Our main conclusion
is that LSTM-based strategies outperform the other models and that the best
diversifier for the AIS built for the S&P 500 index is the AIS built for
Bitcoin. Finally, we test the LSTM model for a higher frequency of data (1
hour). We conclude that it outperforms the results obtained using daily data. | [
"Jakub Michańków",
"Paweł Sakowski",
"Robert Ślepaczuk"
] | 2023-09-27 13:18:39 | http://arxiv.org/abs/2309.15640v1 | http://arxiv.org/pdf/2309.15640v1 | 2309.15640v1 |
Enhancing Sharpness-Aware Optimization Through Variance Suppression | Sharpness-aware minimization (SAM) has well documented merits in enhancing
generalization of deep neural networks, even without sizable data augmentation.
Embracing the geometry of the loss function, where neighborhoods of 'flat
minima' heighten generalization ability, SAM seeks 'flat valleys' by minimizing
the maximum loss caused by an adversary perturbing parameters within the
neighborhood. Although critical to account for sharpness of the loss function,
such an 'over-friendly adversary' can curtail the outmost level of
generalization. The novel approach of this contribution fosters stabilization
of adversaries through variance suppression (VaSSO) to avoid such friendliness.
VaSSO's provable stability safeguards its numerical improvement over SAM in
model-agnostic tasks, including image classification and machine translation.
In addition, experiments confirm that VaSSO endows SAM with robustness against
high levels of label noise. | [
"Bingcong Li",
"Georgios B. Giannakis"
] | 2023-09-27 13:18:23 | http://arxiv.org/abs/2309.15639v2 | http://arxiv.org/pdf/2309.15639v2 | 2309.15639v2 |
FRS-Nets: Fourier Parameterized Rotation and Scale Equivariant Networks for Retinal Vessel Segmentation | With translation equivariance, convolution neural networks (CNNs) have
achieved great success in retinal vessel segmentation. However, some other
symmetries of the vascular morphology are not characterized by CNNs, such as
rotation and scale symmetries. To embed more equivariance into CNNs and achieve
the accuracy requirement for retinal vessel segmentation, we construct a novel
convolution operator (FRS-Conv), which is Fourier parameterized and equivariant
to rotation and scaling. Specifically, we first adopt a new parameterization
scheme, which enables convolutional filters to arbitrarily perform
transformations with high accuracy. Secondly, we derive the formulations for
the rotation and scale equivariant convolution mapping. Finally, we construct
FRS-Conv following the proposed formulations and replace the traditional
convolution filters in U-Net and Iter-Net with FRS-Conv (FRS-Nets). We
faithfully reproduce all compared methods and conduct comprehensive experiments
on three public datasets under both in-dataset and cross-dataset settings. With
merely 13.9% parameters of corresponding baselines, FRS-Nets have achieved
state-of-the-art performance and significantly outperform all compared methods.
It demonstrates the remarkable accuracy, generalization, and clinical
application potential of FRS-Nets. | [
"Zihong Sun",
"Qi Xie",
"Deyu Meng"
] | 2023-09-27 13:14:57 | http://arxiv.org/abs/2309.15638v1 | http://arxiv.org/pdf/2309.15638v1 | 2309.15638v1 |
An Empirical Study of AI Generated Text Detection Tools | Since ChatGPT has emerged as a major AIGC model, providing high-quality
responses across a wide range of applications (including software development
and maintenance), it has attracted much interest from many individuals. ChatGPT
has great promise, but there are serious problems that might arise from its
misuse, especially in the realms of education and public safety. Several AIGC
detectors are available, and they have all been tested on genuine text.
However, more study is needed to see how effective they are for multi-domain
ChatGPT material. This study aims to fill this need by creating a multi-domain
dataset for testing the state-of-the-art APIs and tools for detecting
artificially generated information used by universities and other research
institutions. A large dataset consisting of articles, abstracts, stories, news,
and product reviews was created for this study. The second step is to use the
newly created dataset to put six tools through their paces. Six different
artificial intelligence (AI) text identification systems, including "GPTkit,"
"GPTZero," "Originality," "Sapling," "Writer," and "Zylalab," have accuracy
rates between 55.29 and 97.0%. Although all the tools fared well in the
evaluations, originality was particularly effective across the board. | [
"Arslan Akram"
] | 2023-09-27 12:44:12 | http://arxiv.org/abs/2310.01423v1 | http://arxiv.org/pdf/2310.01423v1 | 2310.01423v1 |
Developing automatic verbatim transcripts for international multilingual meetings: an end-to-end solution | This paper presents an end-to-end solution for the creation of fully
automated conference meeting transcripts and their machine translations into
various languages. This tool has been developed at the World Intellectual
Property Organization (WIPO) using in-house developed speech-to-text (S2T) and
machine translation (MT) components. Beyond describing data collection and
fine-tuning, resulting in a highly customized and robust system, this paper
describes the architecture and evolution of the technical components as well as
highlights the business impact and benefits from the user side. We also point
out particular challenges in the evolution and adoption of the system and how
the new approach created a new product and replaced existing established
workflows in conference management documentation. | [
"Akshat Dewan",
"Michal Ziemski",
"Henri Meylan",
"Lorenzo Concina",
"Bruno Pouliquen"
] | 2023-09-27 12:16:15 | http://arxiv.org/abs/2309.15609v1 | http://arxiv.org/pdf/2309.15609v1 | 2309.15609v1 |
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps | We present a novel learned image reconstruction method for accelerated
cardiac MRI with multiple receiver coils based on deep convolutional neural
networks (CNNs) and algorithm unrolling. In contrast to many existing learned
MR image reconstruction techniques that necessitate coil-sensitivity map (CSM)
estimation as a distinct network component, our proposed approach avoids
explicit CSM estimation. Instead, it implicitly captures and learns to exploit
the inter-coil relationships of the images. Our method consists of a series of
novel learned image and k-space blocks with shared latent information and
adaptation to the acquisition parameters by feature-wise modulation (FiLM), as
well as coil-wise data-consistency (DC) blocks.
Our method achieved PSNR values of 34.89 and 35.56 and SSIM values of 0.920
and 0.942 in the cine track and mapping track validation leaderboard of the
MICCAI STACOM CMRxRecon Challenge, respectively, ranking 4th among different
teams at the time of writing.
Code will be made available at https://github.com/fzimmermann89/CMRxRecon | [
"Felix Frederik Zimmermann",
"Andreas Kofler"
] | 2023-09-27 12:15:05 | http://arxiv.org/abs/2309.15608v1 | http://arxiv.org/pdf/2309.15608v1 | 2309.15608v1 |
Entropic Matching for Expectation Propagation of Markov Jump Processes | This paper addresses the problem of statistical inference for latent
continuous-time stochastic processes, which is often intractable, particularly
for discrete state space processes described by Markov jump processes. To
overcome this issue, we propose a new tractable inference scheme based on an
entropic matching framework that can be embedded into the well-known
expectation propagation algorithm. We demonstrate the effectiveness of our
method by providing closed-form results for a simple family of approximate
distributions and apply it to the general class of chemical reaction networks,
which are a crucial tool for modeling in systems biology. Moreover, we derive
closed form expressions for point estimation of the underlying parameters using
an approximate expectation maximization procedure. We evaluate the performance
of our method on various chemical reaction network instantiations, including a
stochastic Lotka-Voltera example, and discuss its limitations and potential for
future improvements. Our proposed approach provides a promising direction for
addressing complex continuous-time Bayesian inference problems. | [
"Bastian Alt",
"Heinz Koeppl"
] | 2023-09-27 12:07:21 | http://arxiv.org/abs/2309.15604v1 | http://arxiv.org/pdf/2309.15604v1 | 2309.15604v1 |
Distill Knowledge in Multi-task Reinforcement Learning with Optimal-Transport Regularization | In multi-task reinforcement learning, it is possible to improve the data
efficiency of training agents by transferring knowledge from other different
but related tasks. Because the experiences from different tasks are usually
biased toward the specific task goals. Traditional methods rely on
Kullback-Leibler regularization to stabilize the transfer of knowledge from one
task to the others. In this work, we explore the direction of replacing the
Kullback-Leibler divergence with a novel Optimal transport-based
regularization. By using the Sinkhorn mapping, we can approximate the Optimal
transport distance between the state distribution of tasks. The distance is
then used as an amortized reward to regularize the amount of sharing
information. We experiment our frameworks on several grid-based navigation
multi-goal to validate the effectiveness of the approach. The results show that
our added Optimal transport-based rewards are able to speed up the learning
process of agents and outperforms several baselines on multi-task learning. | [
"Bang Giang Le",
"Viet Cuong Ta"
] | 2023-09-27 12:06:34 | http://arxiv.org/abs/2309.15603v1 | http://arxiv.org/pdf/2309.15603v1 | 2309.15603v1 |
OceanBench: The Sea Surface Height Edition | The ocean profoundly influences human activities and plays a critical role in
climate regulation. Our understanding has improved over the last decades with
the advent of satellite remote sensing data, allowing us to capture essential
quantities over the globe, e.g., sea surface height (SSH). However, ocean
satellite data presents challenges for information extraction due to their
sparsity and irregular sampling, signal complexity, and noise. Machine learning
(ML) techniques have demonstrated their capabilities in dealing with
large-scale, complex signals. Therefore we see an opportunity for ML models to
harness the information contained in ocean satellite data. However, data
representation and relevant evaluation metrics can be the defining factors when
determining the success of applied ML. The processing steps from the raw
observation data to a ML-ready state and from model outputs to interpretable
quantities require domain expertise, which can be a significant barrier to
entry for ML researchers. OceanBench is a unifying framework that provides
standardized processing steps that comply with domain-expert standards. It
provides plug-and-play data and pre-configured pipelines for ML researchers to
benchmark their models and a transparent configurable framework for researchers
to customize and extend the pipeline for their tasks. In this work, we
demonstrate the OceanBench framework through a first edition dedicated to SSH
interpolation challenges. We provide datasets and ML-ready benchmarking
pipelines for the long-standing problem of interpolating observations from
simulated ocean satellite data, multi-modal and multi-sensor fusion issues, and
transfer-learning to real ocean satellite observations. The OceanBench
framework is available at github.com/jejjohnson/oceanbench and the dataset
registry is available at github.com/quentinf00/oceanbench-data-registry. | [
"J. Emmanuel Johnson",
"Quentin Febvre",
"Anastasia Gorbunova",
"Sammy Metref",
"Maxime Ballarotta",
"Julien Le Sommer",
"Ronan Fablet"
] | 2023-09-27 12:00:40 | http://arxiv.org/abs/2309.15599v1 | http://arxiv.org/pdf/2309.15599v1 | 2309.15599v1 |
Exciton-Polariton Condensates: A Fourier Neural Operator Approach | Advancements in semiconductor fabrication over the past decade have catalyzed
extensive research into all-optical devices driven by exciton-polariton
condensates. Preliminary validations of such devices, including transistors,
have shown encouraging results even under ambient conditions. A significant
challenge still remains for large scale application however: the lack of a
robust solver that can be used to simulate complex nonlinear systems which
require an extended period of time to stabilize. Addressing this need, we
propose the application of a machine-learning-based Fourier Neural Operator
approach to find the solution to the Gross-Pitaevskii equations coupled with
extra exciton rate equations. This work marks the first direct application of
Neural Operators to an exciton-polariton condensate system. Our findings show
that the proposed method can predict final-state solutions to a high degree of
accuracy almost 1000 times faster than CUDA-based GPU solvers. Moreover, this
paves the way for potential all-optical chip design workflows by integrating
experimental data. | [
"Surya T. Sathujoda",
"Yuan Wang",
"Kanishk Gandhi"
] | 2023-09-27 11:47:26 | http://arxiv.org/abs/2309.15593v1 | http://arxiv.org/pdf/2309.15593v1 | 2309.15593v1 |
Demographic Parity: Mitigating Biases in Real-World Data | Computer-based decision systems are widely used to automate decisions in many
aspects of everyday life, which include sensitive areas like hiring, loaning
and even criminal sentencing. A decision pipeline heavily relies on large
volumes of historical real-world data for training its models. However,
historical training data often contains gender, racial or other biases which
are propagated to the trained models influencing computer-based decisions. In
this work, we propose a robust methodology that guarantees the removal of
unwanted biases while maximally preserving classification utility. Our approach
can always achieve this in a model-independent way by deriving from real-world
data the asymptotic dataset that uniquely encodes demographic parity and
realism. As a proof-of-principle, we deduce from public census records such an
asymptotic dataset from which synthetic samples can be generated to train
well-established classifiers. Benchmarking the generalization capability of
these classifiers trained on our synthetic data, we confirm the absence of any
explicit or implicit bias in the computer-aided decision. | [
"Orestis Loukas",
"Ho-Ryun Chung"
] | 2023-09-27 11:47:05 | http://arxiv.org/abs/2309.17347v1 | http://arxiv.org/pdf/2309.17347v1 | 2309.17347v1 |
Jointly Training Large Autoregressive Multimodal Models | In recent years, advances in the large-scale pretraining of language and
text-to-image models have revolutionized the field of machine learning. Yet,
integrating these two modalities into a single, robust model capable of
generating seamless multimodal outputs remains a significant challenge. To
address this gap, we present the Joint Autoregressive Mixture (JAM) framework,
a modular approach that systematically fuses existing text and image generation
models. We also introduce a specialized, data-efficient instruction-tuning
strategy, tailored for mixed-modal generation tasks. Our final instruct-tuned
model demonstrates unparalleled performance in generating high-quality
multimodal outputs and represents the first model explicitly designed for this
purpose. | [
"Emanuele Aiello",
"Lili Yu",
"Yixin Nie",
"Armen Aghajanyan",
"Barlas Oguz"
] | 2023-09-27 10:40:23 | http://arxiv.org/abs/2309.15564v2 | http://arxiv.org/pdf/2309.15564v2 | 2309.15564v2 |
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank | The application of Unbiased Learning to Rank (ULTR) is widespread in modern
systems for training unbiased ranking models from biased click logs. The key is
to explicitly model a generation process for user behavior and fit click data
based on examination hypothesis. Previous research found empirically that the
true latent relevance can be recovered in most cases as long as the clicks are
perfectly fitted. However, we demonstrate that this is not always achievable,
resulting in a significant reduction in ranking performance. In this work, we
aim to answer if or when the true relevance can be recovered from click data,
which is a foundation issue for ULTR field. We first define a ranking model as
identifiable if it can recover the true relevance up to a scaling
transformation, which is enough for pairwise ranking objective. Then we explore
an equivalent condition for identifiability that can be novely expressed as a
graph connectivity test problem: if and only if a graph (namely identifiability
graph, or IG) constructed on the underlying structure of the dataset is
connected, we can guarantee that the relevance can be correctly recovered. When
the IG is not connected, there may be bad cases leading to poor ranking
performance. To address this issue, we propose two methods, namely node
intervention and node merging, to modify the dataset and restore connectivity
of the IG. Empirical results obtained on a simulation dataset and two LTR
benchmark datasets confirm the validity of our proposed theorems and show the
effectiveness of our methods in mitigating data bias when the relevance model
is unidentifiable. | [
"Mouxiang Chen",
"Chenghao Liu",
"Zemin Liu",
"Zhuo Li",
"Jianling Sun"
] | 2023-09-27 10:31:58 | http://arxiv.org/abs/2309.15560v1 | http://arxiv.org/pdf/2309.15560v1 | 2309.15560v1 |
Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution | Self-interpreting neural networks have garnered significant interest in
research. Existing works in this domain often (1) lack a solid theoretical
foundation ensuring genuine interpretability or (2) compromise model
expressiveness. In response, we formulate a generic Additive Self-Attribution
(ASA) framework. Observing the absence of Shapley value in Additive
Self-Attribution, we propose Shapley Additive Self-Attributing Neural Network
(SASANet), with theoretical guarantees for the self-attribution value equal to
the output's Shapley values. Specifically, SASANet uses a marginal
contribution-based sequential schema and internal distillation-based training
strategies to model meaningful outputs for any number of features, resulting in
un-approximated meaningful value function. Our experimental results indicate
SASANet surpasses existing self-attributing models in performance and rivals
black-box models. Moreover, SASANet is shown more precise and efficient than
post-hoc methods in interpreting its own predictions. | [
"Ying Sun",
"Hengshu Zhu",
"Hui Xiong"
] | 2023-09-27 10:31:48 | http://arxiv.org/abs/2309.15559v1 | http://arxiv.org/pdf/2309.15559v1 | 2309.15559v1 |
Startup success prediction and VC portfolio simulation using CrunchBase data | Predicting startup success presents a formidable challenge due to the
inherently volatile landscape of the entrepreneurial ecosystem. The advent of
extensive databases like Crunchbase jointly with available open data enables
the application of machine learning and artificial intelligence for more
accurate predictive analytics. This paper focuses on startups at their Series B
and Series C investment stages, aiming to predict key success milestones such
as achieving an Initial Public Offering (IPO), attaining unicorn status, or
executing a successful Merger and Acquisition (M\&A). We introduce novel deep
learning model for predicting startup success, integrating a variety of factors
such as funding metrics, founder features, industry category. A distinctive
feature of our research is the use of a comprehensive backtesting algorithm
designed to simulate the venture capital investment process. This simulation
allows for a robust evaluation of our model's performance against historical
data, providing actionable insights into its practical utility in real-world
investment contexts. Evaluating our model on Crunchbase's, we achieved a 14
times capital growth and successfully identified on B round high-potential
startups including Revolut, DigitalOcean, Klarna, Github and others. Our
empirical findings illuminate the importance of incorporating diverse feature
sets in enhancing the model's predictive accuracy. In summary, our work
demonstrates the considerable promise of deep learning models and alternative
unstructured data in predicting startup success and sets the stage for future
advancements in this research area. | [
"Mark Potanin",
"Andrey Chertok",
"Konstantin Zorin",
"Cyril Shtabtsovsky"
] | 2023-09-27 10:22:37 | http://arxiv.org/abs/2309.15552v1 | http://arxiv.org/pdf/2309.15552v1 | 2309.15552v1 |
Identifying confounders in deep-learning-based model predictions using DeepRepViz | Deep Learning (DL) models are increasingly used to analyze neuroimaging data
and uncover insights about the brain, brain pathologies, and psychological
traits. However, extraneous `confounders' variables such as the age of the
participants, sex, or imaging artifacts can bias model predictions, preventing
the models from learning relevant brain-phenotype relationships. In this study,
we provide a solution called the `DeepRepViz' framework that enables
researchers to systematically detect confounders in their DL model predictions.
The framework consists of (1) a metric that quantifies the effect of potential
confounders and (2) a visualization tool that allows researchers to
qualitatively inspect what the DL model is learning. By performing experiments
on simulated and neuroimaging datasets, we demonstrate the benefits of using
DeepRepViz in combination with DL models. For example, experiments on the
neuroimaging datasets reveal that sex is a significant confounder in a DL model
predicting chronic alcohol users (Con-score=0.35). Similarly, DeepRepViz
identifies age as a confounder in a DL model predicting participants'
performance on a cognitive task (Con-score=0.3). Overall, DeepRepViz enables
researchers to systematically test for potential confounders and expose DL
models that rely on extraneous information such as age, sex, or imaging
artifacts. | [
"Roshan Prakash Rane",
"JiHoon Kim",
"Arjun Umesha",
"Didem Stark",
"Marc-André Schulz",
"Kerstin Ritter"
] | 2023-09-27 10:20:45 | http://arxiv.org/abs/2309.15551v1 | http://arxiv.org/pdf/2309.15551v1 | 2309.15551v1 |
From LAION-5B to LAION-EO: Filtering Billions of Images Using Anchor Datasets for Satellite Image Extraction | Large datasets, such as LAION-5B, contain a diverse distribution of images
shared online. However, extraction of domain-specific subsets of large image
corpora is challenging. The extraction approach based on an anchor dataset,
combined with further filtering, is proposed here and demonstrated for the
domain of satellite imagery. This results in the release of LAION-EO, a dataset
sourced from the web containing pairs of text and satellite images in high
(pixel-wise) resolution. The paper outlines the acquisition procedure as well
as some of the features of the dataset. | [
"Mikolaj Czerkawski",
"Alistair Francis"
] | 2023-09-27 09:53:38 | http://arxiv.org/abs/2309.15535v1 | http://arxiv.org/pdf/2309.15535v1 | 2309.15535v1 |
Uncertainty Quantification via Neural Posterior Principal Components | Uncertainty quantification is crucial for the deployment of image restoration
models in safety-critical domains, like autonomous driving and biological
imaging. To date, methods for uncertainty visualization have mainly focused on
per-pixel estimates. However, a heatmap of per-pixel variances is typically of
little practical use, as it does not capture the strong correlations between
pixels. A more natural measure of uncertainty corresponds to the variances
along the principal components (PCs) of the posterior distribution.
Theoretically, the PCs can be computed by applying PCA on samples generated
from a conditional generative model for the input image. However, this requires
generating a very large number of samples at test time, which is painfully slow
with the current state-of-the-art (diffusion) models. In this work, we present
a method for predicting the PCs of the posterior distribution for any input
image, in a single forward pass of a neural network. Our method can either wrap
around a pre-trained model that was trained to minimize the mean square error
(MSE), or can be trained from scratch to output both a predicted image and the
posterior PCs. We showcase our method on multiple inverse problems in imaging,
including denoising, inpainting, super-resolution, and biological
image-to-image translation. Our method reliably conveys instance-adaptive
uncertainty directions, achieving uncertainty quantification comparable with
posterior samplers while being orders of magnitude faster. Examples are
available at https://eliasnehme.github.io/NPPC/ | [
"Elias Nehme",
"Omer Yair",
"Tomer Michaeli"
] | 2023-09-27 09:51:29 | http://arxiv.org/abs/2309.15533v1 | http://arxiv.org/pdf/2309.15533v1 | 2309.15533v1 |
Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models | Large Language Models (LLMs) have recently demonstrated a remarkable success
across various tasks. However, efficiently serving LLMs has been a challenge
due to its large memory bottleneck, specifically in small batch inference
settings (e.g. mobile devices). Weight-only quantization can be a promising
approach, but sub-4 bit quantization remains a challenge due to large-magnitude
activation outliers. To mitigate the undesirable outlier effect, we first
propose per-IC quantization, a simple yet effective method that creates
quantization groups within each input channel (IC) rather than the conventional
per-output channel (OC). Our method is motivated by the observation that
activation outliers affect the input dimension of the weight matrix, so
similarly grouping the weights in the IC direction can isolate outliers to be
within a group. We also find that activation outliers do not dictate
quantization difficulty, and inherent weight sensitivities also exist. With
per-IC quantization as a new outlier-friendly scheme, we then propose Adaptive
Dimensions (AdaDim), a versatile quantization framework that can adapt to
various weight sensitivity patterns. We demonstrate the effectiveness of AdaDim
by augmenting prior methods such as Round-To-Nearest and GPTQ, showing
significant improvements across various language modeling benchmarks for both
base (up to +4.7% on MMLU) and instruction-tuned (up to +10% on HumanEval)
LLMs. | [
"Jung Hwan Heo",
"Jeonghoon Kim",
"Beomseok Kwon",
"Byeongwook Kim",
"Se Jung Kwon",
"Dongsoo Lee"
] | 2023-09-27 09:48:31 | http://arxiv.org/abs/2309.15531v1 | http://arxiv.org/pdf/2309.15531v1 | 2309.15531v1 |
Missing-modality Enabled Multi-modal Fusion Architecture for Medical Data | Fusing multi-modal data can improve the performance of deep learning models.
However, missing modalities are common for medical data due to patients'
specificity, which is detrimental to the performance of multi-modal models in
applications. Therefore, it is critical to adapt the models to missing
modalities. This study aimed to develop an efficient multi-modal fusion
architecture for medical data that was robust to missing modalities and further
improved the performance on disease diagnosis.X-ray chest radiographs for the
image modality, radiology reports for the text modality, and structured value
data for the tabular data modality were fused in this study. Each modality pair
was fused with a Transformer-based bi-modal fusion module, and the three
bi-modal fusion modules were then combined into a tri-modal fusion framework.
Additionally, multivariate loss functions were introduced into the training
process to improve model's robustness to missing modalities in the inference
process. Finally, we designed comparison and ablation experiments for
validating the effectiveness of the fusion, the robustness to missing
modalities and the enhancements from each key component. Experiments were
conducted on MIMIC-IV, MIMIC-CXR with the 14-label disease diagnosis task.
Areas under the receiver operating characteristic curve (AUROC), the area under
the precision-recall curve (AUPRC) were used to evaluate models' performance.
The experimental results demonstrated that our proposed multi-modal fusion
architecture effectively fused three modalities and showed strong robustness to
missing modalities. This method is hopeful to be scaled to more modalities to
enhance the clinical practicality of the model. | [
"Muyu Wang",
"Shiyu Fan",
"Yichen Li",
"Hui Chen"
] | 2023-09-27 09:46:07 | http://arxiv.org/abs/2309.15529v1 | http://arxiv.org/pdf/2309.15529v1 | 2309.15529v1 |
Robust Internal Representations for Domain Generalization | This paper which is part of the New Faculty Highlights Invited Speaker
Program of AAAI'23, serves as a comprehensive survey of my research in transfer
learning by utilizing embedding spaces. The work reviewed in this paper
specifically revolves around the inherent challenges associated with continual
learning and limited availability of labeled data. By providing an overview of
my past and ongoing contributions, this paper aims to present a holistic
understanding of my research, paving the way for future explorations and
advancements in the field. My research delves into the various settings of
transfer learning, including, few-shot learning, zero-shot learning, continual
learning, domain adaptation, and distributed learning. I hope this survey
provides a forward-looking perspective for researchers who would like to focus
on similar research directions. | [
"Mohammad Rostami"
] | 2023-09-27 09:41:02 | http://arxiv.org/abs/2309.15522v1 | http://arxiv.org/pdf/2309.15522v1 | 2309.15522v1 |
MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis | Nowadays, Machine Learning (ML) is experiencing tremendous popularity that
has never been seen before. The operationalization of ML models is governed by
a set of concepts and methods referred to as Machine Learning Operations
(MLOps). Nevertheless, researchers, as well as professionals, often focus more
on the automation aspect and neglect the continuous deployment and monitoring
aspects of MLOps. As a result, there is a lack of continuous learning through
the flow of feedback from production to development, causing unexpected model
deterioration over time due to concept drifts, particularly when dealing with
scarce data. This work explores the complete application of MLOps in the
context of scarce data analysis. The paper proposes a new holistic approach to
enhance biomedical image analysis. Our method includes: a fingerprinting
process that enables selecting the best models, datasets, and model development
strategy relative to the image analysis task at hand; an automated model
development stage; and a continuous deployment and monitoring process to ensure
continuous learning. For preliminary results, we perform a proof of concept for
fingerprinting in microscopic image datasets. | [
"Angelo Yamachui Sitcheu",
"Nils Friederich",
"Simon Baeuerle",
"Oliver Neumann",
"Markus Reischl",
"Ralf Mikut"
] | 2023-09-27 09:39:45 | http://arxiv.org/abs/2309.15521v2 | http://arxiv.org/pdf/2309.15521v2 | 2309.15521v2 |
SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography | Myocardial infarction (MI) is a severe case of coronary artery disease (CAD)
and ultimately, its detection is substantial to prevent progressive damage to
the myocardium. In this study, we propose a novel view-fusion model named
self-attention fusion network (SAF-Net) to detect MI from multi-view
echocardiography recordings. The proposed framework utilizes apical 2-chamber
(A2C) and apical 4-chamber (A4C) view echocardiography recordings for
classification. Three reference frames are extracted from each recording of
both views and deployed pre-trained deep networks to extract highly
representative features. The SAF-Net model utilizes a self-attention mechanism
to learn dependencies in extracted feature vectors. The proposed model is
computationally efficient thanks to its compact architecture having three main
parts: a feature embedding to reduce dimensionality, self-attention for
view-pooling, and dense layers for the classification. Experimental evaluation
is performed using the HMC-QU-TAU dataset which consists of 160 patients with
A2C and A4C view echocardiography recordings. The proposed SAF-Net model
achieves a high-performance level with 88.26% precision, 77.64% sensitivity,
and 78.13% accuracy. The results demonstrate that the SAF-Net model achieves
the most accurate MI detection over multi-view echocardiography recordings. | [
"Ilke Adalioglu",
"Mete Ahishali",
"Aysen Degerli",
"Serkan Kiranyaz",
"Moncef Gabbouj"
] | 2023-09-27 09:38:03 | http://arxiv.org/abs/2309.15520v1 | http://arxiv.org/pdf/2309.15520v1 | 2309.15520v1 |
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training | Modern DNN-based recommendation systems rely on training-derived embeddings
of sparse features. Input sparsity makes obtaining high-quality embeddings for
rarely-occurring categories harder as their representations are updated
infrequently. We demonstrate a training-time technique to produce superior
embeddings via effective cross-category learning and theoretically explain its
surprising effectiveness. The scheme, termed the multi-layer embeddings
training (MLET), trains embeddings using factorization of the embedding layer,
with an inner dimension higher than the target embedding dimension. For
inference efficiency, MLET converts the trained two-layer embedding into a
single-layer one thus keeping inference-time model size unchanged.
Empirical superiority of MLET is puzzling as its search space is not larger
than that of the single-layer embedding. The strong dependence of MLET on the
inner dimension is even more surprising. We develop a theory that explains both
of these behaviors by showing that MLET creates an adaptive update mechanism
modulated by the singular vectors of embeddings. When tested on multiple
state-of-the-art recommendation models for click-through rate (CTR) prediction
tasks, MLET consistently produces better models, especially for rare items. At
constant model quality, MLET allows embedding dimension, and model size,
reduction by up to 16x, and 5.8x on average, across the models. | [
"Zihao Deng",
"Benjamin Ghaemmaghami",
"Ashish Kumar Singh",
"Benjamin Cho",
"Leo Orshansky",
"Mattan Erez",
"Michael Orshansky"
] | 2023-09-27 09:32:10 | http://arxiv.org/abs/2309.15881v1 | http://arxiv.org/pdf/2309.15881v1 | 2309.15881v1 |
GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network | Electroencephalography(EEG) classification is a crucial task in neuroscience,
neural engineering, and several commercial applications. Traditional EEG
classification models, however, have often overlooked or inadequately leveraged
the brain's topological information. Recognizing this shortfall, there has been
a burgeoning interest in recent years in harnessing the potential of Graph
Neural Networks (GNN) to exploit the topological information by modeling
features selected from each EEG channel in a graph structure. To further
facilitate research in this direction, we introduce GNN4EEG, a versatile and
user-friendly toolkit for GNN-based modeling of EEG signals. GNN4EEG comprises
three components: (i)A large benchmark constructed with four EEG classification
tasks based on EEG data collected from 123 participants. (ii)Easy-to-use
implementations on various state-of-the-art GNN-based EEG classification
models, e.g., DGCNN, RGNN, etc. (iii)Implementations of comprehensive
experimental settings and evaluation protocols, e.g., data splitting protocols,
and cross-validation protocols. GNN4EEG is publicly released at
https://github.com/Miracle-2001/GNN4EEG. | [
"Kaiyuan Zhang",
"Ziyi Ye",
"Qingyao Ai",
"Xiaohui Xie",
"Yiqun Liu"
] | 2023-09-27 09:31:13 | http://arxiv.org/abs/2309.15515v1 | http://arxiv.org/pdf/2309.15515v1 | 2309.15515v1 |
Finite Scalar Quantization: VQ-VAE Made Simple | We propose to replace vector quantization (VQ) in the latent representation
of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where
we project the VAE representation down to a few dimensions (typically less than
10). Each dimension is quantized to a small set of fixed values, leading to an
(implicit) codebook given by the product of these sets. By appropriately
choosing the number of dimensions and values each dimension can take, we obtain
the same codebook size as in VQ. On top of such discrete representations, we
can train the same models that have been trained on VQ-VAE representations. For
example, autoregressive and masked transformer models for image generation,
multimodal generation, and dense prediction computer vision tasks. Concretely,
we employ FSQ with MaskGIT for image generation, and with UViM for depth
estimation, colorization, and panoptic segmentation. Despite the much simpler
design of FSQ, we obtain competitive performance in all these tasks. We
emphasize that FSQ does not suffer from codebook collapse and does not need the
complex machinery employed in VQ (commitment losses, codebook reseeding, code
splitting, entropy penalties, etc.) to learn expressive discrete
representations. | [
"Fabian Mentzer",
"David Minnen",
"Eirikur Agustsson",
"Michael Tschannen"
] | 2023-09-27 09:13:40 | http://arxiv.org/abs/2309.15505v2 | http://arxiv.org/pdf/2309.15505v2 | 2309.15505v2 |
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations | Bayesian personalized federated learning (BPFL) addresses challenges in
existing personalized FL (PFL). BPFL aims to quantify the uncertainty and
heterogeneity within and across clients towards uncertainty representations by
addressing the statistical heterogeneity of client data. In PFL, some recent
preliminary work proposes to decompose hidden neural representations into
shared and local components and demonstrates interesting results. However, most
of them do not address client uncertainty and heterogeneity in FL systems,
while appropriately decoupling neural representations is challenging and often
ad hoc. In this paper, we make the first attempt to introduce a general BPFL
framework to decompose and jointly learn shared and personalized uncertainty
representations on statistically heterogeneous client data over time. A
Bayesian federated neural network BPFed instantiates BPFL by jointly learning
cross-client shared uncertainty and client-specific personalized uncertainty
over statistically heterogeneous and randomly participating clients. We further
involve continual updating of prior distribution in BPFed to speed up the
convergence and avoid catastrophic forgetting. Theoretical analysis and
guarantees are provided in addition to the experimental evaluation of BPFed
against the diversified baselines. | [
"Hui Chen",
"Hengyu Liu",
"Longbing Cao",
"Tiancheng Zhang"
] | 2023-09-27 08:52:08 | http://arxiv.org/abs/2309.15499v2 | http://arxiv.org/pdf/2309.15499v2 | 2309.15499v2 |
Explainable machine learning-based prediction model for diabetic nephropathy | The aim of this study is to analyze the effect of serum metabolites on
diabetic nephropathy (DN) and predict the prevalence of DN through a machine
learning approach. The dataset consists of 548 patients from April 2018 to
April 2019 in Second Affiliated Hospital of Dalian Medical University (SAHDMU).
We select the optimal 38 features through a Least absolute shrinkage and
selection operator (LASSO) regression model and a 10-fold cross-validation. We
compare four machine learning algorithms, including eXtreme Gradient Boosting
(XGB), random forest, decision tree and logistic regression, by AUC-ROC curves,
decision curves, calibration curves. We quantify feature importance and
interaction effects in the optimal predictive model by Shapley Additive
exPlanations (SHAP) method. The XGB model has the best performance to screen
for DN with the highest AUC value of 0.966. The XGB model also gains more
clinical net benefits than others and the fitting degree is better. In
addition, there are significant interactions between serum metabolites and
duration of diabetes. We develop a predictive model by XGB algorithm to screen
for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in
the model, and can possibly be biomarkers for DN. | [
"Jing-Mei Yin",
"Yang Li",
"Jun-Tang Xue",
"Guo-Wei Zong",
"Zhong-Ze Fang",
"Lang Zou"
] | 2023-09-27 08:46:57 | http://arxiv.org/abs/2309.16730v1 | http://arxiv.org/pdf/2309.16730v1 | 2309.16730v1 |
Fast Locality Sensitive Hashing with Theoretical Guarantee | Locality-sensitive hashing (LSH) is an effective randomized technique widely
used in many machine learning tasks. The cost of hashing is proportional to
data dimensions, and thus often the performance bottleneck when dimensionality
is high and the number of hash functions involved is large. Surprisingly,
however, little work has been done to improve the efficiency of LSH
computation. In this paper, we design a simple yet efficient LSH scheme, named
FastLSH, under l2 norm. By combining random sampling and random projection,
FastLSH reduces the time complexity from O(n) to O(m) (m<n), where n is the
data dimensionality and m is the number of sampled dimensions. Moreover,
FastLSH has provable LSH property, which distinguishes it from the non-LSH fast
sketches. We conduct comprehensive experiments over a collection of real and
synthetic datasets for the nearest neighbor search task. Experimental results
demonstrate that FastLSH is on par with the state-of-the-arts in terms of
answer quality, space occupation and query efficiency, while enjoying up to 80x
speedup in hash function evaluation. We believe that FastLSH is a promising
alternative to the classic LSH scheme. | [
"Zongyuan Tan",
"Hongya Wang",
"Bo Xu",
"Minjie Luo",
"Ming Du"
] | 2023-09-27 08:21:38 | http://arxiv.org/abs/2309.15479v1 | http://arxiv.org/pdf/2309.15479v1 | 2309.15479v1 |
The Robust Semantic Segmentation UNCV2023 Challenge Results | This paper outlines the winning solutions employed in addressing the MUAD
uncertainty quantification challenge held at ICCV 2023. The challenge was
centered around semantic segmentation in urban environments, with a particular
focus on natural adversarial scenarios. The report presents the results of 19
submitted entries, with numerous techniques drawing inspiration from
cutting-edge uncertainty quantification methodologies presented at prominent
conferences in the fields of computer vision and machine learning and journals
over the past few years. Within this document, the challenge is introduced,
shedding light on its purpose and objectives, which primarily revolved around
enhancing the robustness of semantic segmentation in urban scenes under varying
natural adversarial conditions. The report then delves into the top-performing
solutions. Moreover, the document aims to provide a comprehensive overview of
the diverse solutions deployed by all participants. By doing so, it seeks to
offer readers a deeper insight into the array of strategies that can be
leveraged to effectively handle the inherent uncertainties associated with
autonomous driving and semantic segmentation, especially within urban
environments. | [
"Xuanlong Yu",
"Yi Zuo",
"Zitao Wang",
"Xiaowen Zhang",
"Jiaxuan Zhao",
"Yuting Yang",
"Licheng Jiao",
"Rui Peng",
"Xinyi Wang",
"Junpei Zhang",
"Kexin Zhang",
"Fang Liu",
"Roberto Alcover-Couso",
"Juan C. SanMiguel",
"Marcos Escudero-Viñolo",
"Hanlin Tian",
"Kenta Matsui",
"Tianhao Wang",
"Fahmy Adan",
"Zhitong Gao",
"Xuming He",
"Quentin Bouniot",
"Hossein Moghaddam",
"Shyam Nandan Rai",
"Fabio Cermelli",
"Carlo Masone",
"Andrea Pilzer",
"Elisa Ricci",
"Andrei Bursuc",
"Arno Solin",
"Martin Trapp",
"Rui Li",
"Angela Yao",
"Wenlong Chen",
"Ivor Simpson",
"Neill D. F. Campbell",
"Gianni Franchi"
] | 2023-09-27 08:20:03 | http://arxiv.org/abs/2309.15478v1 | http://arxiv.org/pdf/2309.15478v1 | 2309.15478v1 |
Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey | The emerging field of artificial intelligence of things (AIoT, AI+IoT) is
driven by the widespread use of intelligent infrastructures and the impressive
success of deep learning (DL). With the deployment of DL on various intelligent
infrastructures featuring rich sensors and weak DL computing capabilities, a
diverse range of AIoT applications has become possible. However, DL models are
notoriously resource-intensive. Existing research strives to realize
near-/realtime inference of AIoT live data and low-cost training using AIoT
datasets on resource-scare infrastructures. Accordingly, the accuracy and
responsiveness of DL models are bounded by resource availability. To this end,
the algorithm-system co-design that jointly optimizes the resource-friendly DL
models and model-adaptive system scheduling improves the runtime resource
availability and thus pushes the performance boundary set by the standalone
level. Unlike previous surveys on resource-friendly DL models or hand-crafted
DL compilers/frameworks with partially fine-tuned components, this survey aims
to provide a broader optimization space for more free resource-performance
tradeoffs. The cross-level optimization landscape involves various granularity,
including the DL model, computation graph, operator, memory schedule, and
hardware instructor in both on-device and distributed paradigms. Furthermore,
due to the dynamic nature of AIoT context, which includes heterogeneous
hardware, agnostic sensing data, varying user-specified performance demands,
and resource constraints, this survey explores the context-aware
inter-/intra-device controllers for automatic cross-level adaptation.
Additionally, we identify some potential directions for resource-efficient AIoT
systems. By consolidating problems and techniques scattered over diverse
levels, we aim to help readers understand their connections and stimulate
further discussions. | [
"Sicong Liu",
"Bin Guo",
"Cheng Fang",
"Ziqi Wang",
"Shiyan Luo",
"Zimu Zhou",
"Zhiwen Yu"
] | 2023-09-27 08:04:24 | http://arxiv.org/abs/2309.15467v1 | http://arxiv.org/pdf/2309.15467v1 | 2309.15467v1 |
DTC: Deep Tracking Control -- A Unifying Approach to Model-Based Planning and Reinforcement-Learning for Versatile and Robust Locomotion | Legged locomotion is a complex control problem that requires both accuracy
and robustness to cope with real-world challenges. Legged systems have
traditionally been controlled using trajectory optimization with inverse
dynamics. Such hierarchical model-based methods are appealing due to intuitive
cost function tuning, accurate planning, and most importantly, the insightful
understanding gained from more than one decade of extensive research. However,
model mismatch and violation of assumptions are common sources of faulty
operation and may hinder successful sim-to-real transfer. Simulation-based
reinforcement learning, on the other hand, results in locomotion policies with
unprecedented robustness and recovery skills. Yet, all learning algorithms
struggle with sparse rewards emerging from environments where valid footholds
are rare, such as gaps or stepping stones. In this work, we propose a hybrid
control architecture that combines the advantages of both worlds to
simultaneously achieve greater robustness, foot-placement accuracy, and terrain
generalization. Our approach utilizes a model-based planner to roll out a
reference motion during training. A deep neural network policy is trained in
simulation, aiming to track the optimized footholds. We evaluate the accuracy
of our locomotion pipeline on sparse terrains, where pure data-driven methods
are prone to fail. Furthermore, we demonstrate superior robustness in the
presence of slippery or deformable ground when compared to model-based
counterparts. Finally, we show that our proposed tracking controller
generalizes across different trajectory optimization methods not seen during
training. In conclusion, our work unites the predictive capabilities and
optimality guarantees of online planning with the inherent robustness
attributed to offline learning. | [
"Fabian Jenelten",
"Junzhe He",
"Farbod Farshidian",
"Marco Hutter"
] | 2023-09-27 07:57:37 | http://arxiv.org/abs/2309.15462v1 | http://arxiv.org/pdf/2309.15462v1 | 2309.15462v1 |
SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problems | This paper introduces a novel approach to solve inverse problems by
leveraging deep learning techniques. The objective is to infer unknown
parameters that govern a physical system based on observed data. We focus on
scenarios where the underlying forward model demonstrates pronounced nonlinear
behaviour, and where the dimensionality of the unknown parameter space is
substantially smaller than that of the observations. Our proposed method builds
upon physics-informed neural networks (PINNs) trained with a hybrid loss
function that combines observed data with simulated data generated by a known
(approximate) physical model. Experimental results on an orbit restitution
problem demonstrate that our approach surpasses the performance of standard
PINNs, providing improved accuracy and robustness. | [
"Sidney Besnard",
"Frédéric Jurie",
"Jalal M. Fadili"
] | 2023-09-27 06:34:55 | http://arxiv.org/abs/2309.16729v1 | http://arxiv.org/pdf/2309.16729v1 | 2309.16729v1 |
Graph Neural Prompting with Large Language Models | Large Language Models (LLMs) have shown remarkable generalization capability
with exceptional performance in various language modeling tasks. However, they
still exhibit inherent limitations in precisely capturing and returning
grounded knowledge. While existing work has explored utilizing knowledge graphs
to enhance language modeling via joint training and customized model
architectures, applying this to LLMs is problematic owing to their large number
of parameters and high computational cost. In addition, how to leverage the
pre-trained LLMs and avoid training a customized model from scratch remains an
open question. In this work, we propose Graph Neural Prompting (GNP), a novel
plug-and-play method to assist pre-trained LLMs in learning beneficial
knowledge from KGs. GNP encompasses various designs, including a standard graph
neural network encoder, a cross-modality pooling module, a domain projector,
and a self-supervised link prediction objective. Extensive experiments on
multiple datasets demonstrate the superiority of GNP on both commonsense and
biomedical reasoning tasks across different LLM sizes and settings. | [
"Yijun Tian",
"Huan Song",
"Zichen Wang",
"Haozhu Wang",
"Ziqing Hu",
"Fang Wang",
"Nitesh V. Chawla",
"Panpan Xu"
] | 2023-09-27 06:33:29 | http://arxiv.org/abs/2309.15427v1 | http://arxiv.org/pdf/2309.15427v1 | 2309.15427v1 |
NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions | We present a novel type of neural fields that uses general radial bases for
signal representation. State-of-the-art neural fields typically rely on
grid-based representations for storing local neural features and N-dimensional
linear kernels for interpolating features at continuous query points. The
spatial positions of their neural features are fixed on grid nodes and cannot
well adapt to target signals. Our method instead builds upon general radial
bases with flexible kernel position and shape, which have higher spatial
adaptivity and can more closely fit target signals. To further improve the
channel-wise capacity of radial basis functions, we propose to compose them
with multi-frequency sinusoid functions. This technique extends a radial basis
to multiple Fourier radial bases of different frequency bands without requiring
extra parameters, facilitating the representation of details. Moreover, by
marrying adaptive radial bases with grid-based ones, our hybrid combination
inherits both adaptivity and interpolation smoothness. We carefully designed
weighting schemes to let radial bases adapt to different types of signals
effectively. Our experiments on 2D image and 3D signed distance field
representation demonstrate the higher accuracy and compactness of our method
than prior arts. When applied to neural radiance field reconstruction, our
method achieves state-of-the-art rendering quality, with small model size and
comparable training speed. | [
"Zhang Chen",
"Zhong Li",
"Liangchen Song",
"Lele Chen",
"Jingyi Yu",
"Junsong Yuan",
"Yi Xu"
] | 2023-09-27 06:32:05 | http://arxiv.org/abs/2309.15426v1 | http://arxiv.org/pdf/2309.15426v1 | 2309.15426v1 |
Deep Learning in Deterministic Computational Mechanics | The rapid growth of deep learning research, including within the field of
computational mechanics, has resulted in an extensive and diverse body of
literature. To help researchers identify key concepts and promising
methodologies within this field, we provide an overview of deep learning in
deterministic computational mechanics. Five main categories are identified and
explored: simulation substitution, simulation enhancement, discretizations as
neural networks, generative approaches, and deep reinforcement learning. This
review focuses on deep learning methods rather than applications for
computational mechanics, thereby enabling researchers to explore this field
more effectively. As such, the review is not necessarily aimed at researchers
with extensive knowledge of deep learning -- instead, the primary audience is
researchers at the verge of entering this field or those who attempt to gain an
overview of deep learning in computational mechanics. The discussed concepts
are, therefore, explained as simple as possible. | [
"Leon Herrmann",
"Stefan Kollmannsberger"
] | 2023-09-27 05:57:19 | http://arxiv.org/abs/2309.15421v1 | http://arxiv.org/pdf/2309.15421v1 | 2309.15421v1 |
The Triad of Failure Modes and a Possible Way Out | We present a novel objective function for cluster-based self-supervised
learning (SSL) that is designed to circumvent the triad of failure modes,
namely representation collapse, cluster collapse, and the problem of invariance
to permutations of cluster assignments. This objective consists of three key
components: (i) A generative term that penalizes representation collapse, (ii)
a term that promotes invariance to data augmentations, thereby addressing the
issue of label permutations and (ii) a uniformity term that penalizes cluster
collapse. Additionally, our proposed objective possesses two notable
advantages. Firstly, it can be interpreted from a Bayesian perspective as a
lower bound on the data log-likelihood. Secondly, it enables the training of a
standard backbone architecture without the need for asymmetric elements like
stop gradients, momentum encoders, or specialized clustering layers. Due to its
simplicity and theoretical foundation, our proposed objective is well-suited
for optimization. Experiments on both toy and real world data demonstrate its
effectiveness | [
"Emanuele Sansone"
] | 2023-09-27 05:54:14 | http://arxiv.org/abs/2309.15420v1 | http://arxiv.org/pdf/2309.15420v1 | 2309.15420v1 |
Neuro-Inspired Hierarchical Multimodal Learning | Integrating and processing information from various sources or modalities are
critical for obtaining a comprehensive and accurate perception of the real
world. Drawing inspiration from neuroscience, we develop the
Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the
concept of information bottleneck. Distinct from most traditional fusion models
that aim to incorporate all modalities as input, our model designates the prime
modality as input, while the remaining modalities act as detectors in the
information pathway. Our proposed perception model focuses on constructing an
effective and compact information flow by achieving a balance between the
minimization of mutual information between the latent state and the input modal
state, and the maximization of mutual information between the latent states and
the remaining modal states. This approach leads to compact latent state
representations that retain relevant information while minimizing redundancy,
thereby substantially enhancing the performance of downstream tasks.
Experimental evaluations on both the MUStARD and CMU-MOSI datasets demonstrate
that our model consistently distills crucial information in multimodal learning
scenarios, outperforming state-of-the-art benchmarks. | [
"Xiongye Xiao",
"Gengshuo Liu",
"Gaurav Gupta",
"Defu Cao",
"Shixuan Li",
"Yaxing Li",
"Tianqing Fang",
"Mingxi Cheng",
"Paul Bogdan"
] | 2023-09-27 05:50:05 | http://arxiv.org/abs/2309.15877v1 | http://arxiv.org/pdf/2309.15877v1 | 2309.15877v1 |
Automatic Feature Fairness in Recommendation via Adversaries | Fairness is a widely discussed topic in recommender systems, but its
practical implementation faces challenges in defining sensitive features while
maintaining recommendation accuracy. We propose feature fairness as the
foundation to achieve equitable treatment across diverse groups defined by
various feature combinations. This improves overall accuracy through balanced
feature generalizability. We introduce unbiased feature learning through
adversarial training, using adversarial perturbation to enhance feature
representation. The adversaries improve model generalization for
under-represented features. We adapt adversaries automatically based on two
forms of feature biases: frequency and combination variety of feature values.
This allows us to dynamically adjust perturbation strengths and adversarial
training weights. Stronger perturbations are applied to feature values with
fewer combination varieties to improve generalization, while higher weights for
low-frequency features address training imbalances. We leverage the Adaptive
Adversarial perturbation based on the widely-applied Factorization Machine
(AAFM) as our backbone model. In experiments, AAFM surpasses strong baselines
in both fairness and accuracy measures. AAFM excels in providing item- and
user-fairness for single- and multi-feature tasks, showcasing their versatility
and scalability. To maintain good accuracy, we find that adversarial
perturbation must be well-managed: during training, perturbations should not
overly persist and their strengths should decay. | [
"Hengchang Hu",
"Yiming Cao",
"Zhankui He",
"Samson Tan",
"Min-Yen Kan"
] | 2023-09-27 05:48:05 | http://arxiv.org/abs/2309.15418v1 | http://arxiv.org/pdf/2309.15418v1 | 2309.15418v1 |
STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs | Many emerging user-facing services adopt Graph Neural Networks (GNNs) to
improve serving accuracy. When the graph used by a GNN model changes,
representations (embedding) of nodes in the graph should be updated
accordingly. However, the node representation update is too slow, resulting in
either long response latency of user queries (the inference is performed after
the update completes) or high staleness problem (the inference is performed
based on stale data). Our in-depth analysis shows that the slow update is
mainly due to neighbor explosion problem in graphs and duplicated computation.
Based on such findings, we propose STAG, a GNN serving framework that enables
low latency and low staleness of GNN-based services. It comprises a
collaborative serving mechanism and an additivity-based incremental propagation
strategy. With the collaborative serving mechanism, only part of node
representations are updated during the update phase, and the final
representations are calculated in the inference phase. It alleviates the
neighbor explosion problem. The additivity-based incremental propagation
strategy reuses intermediate data during the update phase, eliminating
duplicated computation problem. Experimental results show that STAG accelerates
the update phase by 1.3x~90.1x, and greatly reduces staleness time with a
slight increase in response latency. | [
"Jiawen Wang",
"Quan Chen",
"Deze Zeng",
"Zhuo Song",
"Chen Chen",
"Minyi Guo"
] | 2023-09-27 05:15:02 | http://arxiv.org/abs/2309.15875v1 | http://arxiv.org/pdf/2309.15875v1 | 2309.15875v1 |
Revolutionizing Terrain-Precipitation Understanding through AI-driven Knowledge Discovery | Advancing our understanding of climate processes in regions characterized by
intricate terrain complexity is a paramount challenge in contemporary climate
science, particularly in the context of global climate change. Notably, the
scarcity of observational data in these regions has imposed substantial
limitations on understanding the nuanced climate dynamics therein. For the
first time, utilizing cutting-edge AI-driven knowledge discovery techniques, we
have uncovered explicit equations that elucidate the intricate relationship
between terrain features and precipitation patterns, illuminating the
previously concealed complexities governing these relationships. These
equations, thus far undisclosed, exhibit remarkable accuracy compared to
conventional empirical models when applied to precipitation data. Building on
this foundation, we reveal a phenomenon known as the '1995 turning point,'
indicating a significant shift in the terrain-precipitation relationship in
approximately 1995, related to the forces of climate change. These equations
have practical applications, particularly in achieving fine-scale downscaling
precipitation predictions from low-resolution future climate data. This
capability provides invaluable insights into the expected changes in
precipitation patterns across diverse terrains under future climate scenarios. | [
"Hao Xu",
"Yuntian Chen",
"Zhenzhong Zeng",
"Nina Li",
"Jian Li",
"Dongxiao Zhang"
] | 2023-09-27 04:47:22 | http://arxiv.org/abs/2309.15400v1 | http://arxiv.org/pdf/2309.15400v1 | 2309.15400v1 |
Model-Free, Regret-Optimal Best Policy Identification in Online CMDPs | This paper considers the best policy identification (BPI) problem in online
Constrained Markov Decision Processes (CMDPs). We are interested in algorithms
that are model-free, have low regret, and identify an optimal policy with a
high probability. Existing model-free algorithms for online CMDPs with
sublinear regret and constraint violation do not provide any convergence
guarantee to an optimal policy and provide only average performance guarantees
when a policy is uniformly sampled at random from all previously used policies.
In this paper, we develop a new algorithm, named
Pruning-Refinement-Identification (PRI), based on a fundamental structural
property of CMDPs proved in Koole(1988); Ross(1989), which we call limited
stochasticity. The property says for a CMDP with $N$ constraints, there exists
an optimal policy with at most $N$ stochastic decisions.
The proposed algorithm first identifies at which step and in which state a
stochastic decision has to be taken and then fine-tunes the distributions of
these stochastic decisions. PRI achieves trio objectives: (i) PRI is a
model-free algorithm; and (ii) it outputs a near-optimal policy with a high
probability at the end of learning; and (iii) in the tabular setting, PRI
guarantees $\tilde{\mathcal{O}}(\sqrt{K})$ regret and constraint violation,
which significantly improves the best existing regret bound
$\tilde{\mathcal{O}}(K^{\frac{4}{5}})$ under a model-free algorithm, where $K$
is the total number of episodes. | [
"Zihan Zhou",
"Honghao Wei",
"Lei Ying"
] | 2023-09-27 04:33:09 | http://arxiv.org/abs/2309.15395v3 | http://arxiv.org/pdf/2309.15395v3 | 2309.15395v3 |
Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissions | We present a comprehensive evaluation of the robustness and explainability of
ResNet-like models in the context of Unintended Radiated Emission (URE)
classification and suggest a new approach leveraging Neural Stochastic
Differential Equations (SDEs) to address identified limitations. We provide an
empirical demonstration of the fragility of ResNet-like models to Gaussian
noise perturbations, where the model performance deteriorates sharply and its
F1-score drops to near insignificance at 0.008 with a Gaussian noise of only
0.5 standard deviation. We also highlight a concerning discrepancy where the
explanations provided by ResNet-like models do not reflect the inherent
periodicity in the input data, a crucial attribute in URE detection from stable
devices. In response to these findings, we propose a novel application of
Neural SDEs to build models for URE classification that are not only robust to
noise but also provide more meaningful and intuitive explanations. Neural SDE
models maintain a high F1-score of 0.93 even when exposed to Gaussian noise
with a standard deviation of 0.5, demonstrating superior resilience to ResNet
models. Neural SDE models successfully recover the time-invariant or periodic
horizontal bands from the input data, a feature that was conspicuously missing
in the explanations generated by ResNet-like models. This advancement presents
a small but significant step in the development of robust and interpretable
models for real-world URE applications where data is inherently noisy and
assurance arguments demand interpretable machine learning predictions. | [
"Sumit Kumar Jha",
"Susmit Jha",
"Rickard Ewetz",
"Alvaro Velasquez"
] | 2023-09-27 03:37:16 | http://arxiv.org/abs/2309.15386v1 | http://arxiv.org/pdf/2309.15386v1 | 2309.15386v1 |
ADGym: Design Choices for Deep Anomaly Detection | Deep learning (DL) techniques have recently been applied to anomaly detection
(AD), yielding successful outcomes in areas such as finance, medical services,
and cloud computing. However, much of the current research evaluates a deep AD
algorithm holistically, failing to understand the contributions of individual
design choices like loss functions and network architectures. Consequently, the
importance of prerequisite steps, such as preprocessing, might be overshadowed
by the spotlight on novel loss functions and architectures. In this paper, we
address these oversights by posing two questions: (i) Which components (i.e.,
design choices) of deep AD methods are pivotal in detecting anomalies? (ii) How
can we construct tailored AD algorithms for specific datasets by selecting the
best design choices automatically, rather than relying on generic, pre-existing
solutions? To this end, we introduce ADGym, the first platform designed for
comprehensive evaluation and automatic selection of AD design elements in deep
methods. Extensive experiments reveal that merely adopting existing leading
methods is not ideal. Models crafted using ADGym markedly surpass current
state-of-the-art techniques. | [
"Minqi Jiang",
"Chaochuan Hou",
"Ao Zheng",
"Songqiao Han",
"Hailiang Huang",
"Qingsong Wen",
"Xiyang Hu",
"Yue Zhao"
] | 2023-09-27 03:10:32 | http://arxiv.org/abs/2309.15376v1 | http://arxiv.org/pdf/2309.15376v1 | 2309.15376v1 |
PPG to ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling | An electrocardiogram (ECG or EKG) is a medical test that measures the heart's
electrical activity. ECGs are often used to diagnose and monitor a wide range
of heart conditions, including arrhythmias, heart attacks, and heart failure.
On the one hand, the conventional ECG requires clinical measurement, which
restricts its deployment to medical facilities. On the other hand, single-lead
ECG has become popular on wearable devices using administered procedures. An
alternative to ECG is Photoplethysmography (PPG), which uses non-invasive,
low-cost optical methods to measure cardiac physiology, making it a suitable
option for capturing vital heart signs in daily life. As a result, it has
become increasingly popular in health monitoring and is used in various
clinical and commercial wearable devices. While ECG and PPG correlate strongly,
the latter does not offer significant clinical diagnostic value. Here, we
propose a subject-independent attention-based deep state-space model to
translate PPG signals to corresponding ECG waveforms. The model is highly
data-efficient by incorporating prior knowledge in terms of probabilistic
graphical models. Notably, the model enables the detection of atrial
fibrillation (AFib), the most common heart rhythm disorder in adults, by
complementing ECG's accuracy with continuous PPG monitoring. We evaluated the
model on 55 subjects from the MIMIC III database. Quantitative and qualitative
experimental results demonstrate the effectiveness and efficiency of our
approach. | [
"Khuong Vo",
"Mostafa El-Khamy",
"Yoojin Choi"
] | 2023-09-27 03:07:46 | http://arxiv.org/abs/2309.15375v1 | http://arxiv.org/pdf/2309.15375v1 | 2309.15375v1 |
Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences | One among several advantages of measure transport methods is that they allow
for a unified framework for processing and analysis of data distributed
according to a wide class of probability measures. Within this context, we
present results from computational studies aimed at assessing the potential of
measure transport techniques, specifically, the use of triangular transport
maps, as part of a workflow intended to support research in the biological
sciences. Scarce data scenarios, which are common in domains such as radiation
biology, are of particular interest. We find that when data is scarce, sparse
transport maps are advantageous. In particular, statistics gathered from
computing series of (sparse) adaptive transport maps, trained on a series of
randomly chosen subsets of the set of available data samples, leads to
uncovering information hidden in the data. As a result, in the radiation
biology application considered here, this approach provides a tool for
generating hypotheses about gene relationships and their dynamics under
radiation exposure. | [
"Vanessa Lopez-Marrero",
"Patrick R. Johnstone",
"Gilchan Park",
"Xihaier Luo"
] | 2023-09-27 02:36:42 | http://arxiv.org/abs/2309.15366v1 | http://arxiv.org/pdf/2309.15366v1 | 2309.15366v1 |
C3Net: interatomic potential neural network for prediction of physicochemical properties in heterogenous systems | Understanding the interactions of a solute with its environment is of
fundamental importance in chemistry and biology. In this work, we propose a
deep neural network architecture for atom type embeddings in its molecular
context and interatomic potential that follows fundamental physical laws. The
architecture is applied to predict physicochemical properties in heterogeneous
systems including solvation in diverse solvents, 1-octanol-water partitioning,
and PAMPA with a single set of network weights. We show that our architecture
is generalized well to the physicochemical properties and outperforms
state-of-the-art approaches based on quantum mechanics and neural networks in
the task of solvation free energy prediction. The interatomic potentials at
each atom in a solute obtained from the model allow quantitative analysis of
the physicochemical properties at atomic resolution consistent with chemical
and physical reasoning. The software is available at
https://github.com/SehanLee/C3Net. | [
"Sehan Lee",
"Jaechang Lim",
"Woo Youn Kim"
] | 2023-09-27 00:51:24 | http://arxiv.org/abs/2309.15334v1 | http://arxiv.org/pdf/2309.15334v1 | 2309.15334v1 |
Exploring Learned Representations of Neural Networks with Principal Component Analysis | Understanding feature representation for deep neural networks (DNNs) remains
an open question within the general field of explainable AI. We use principal
component analysis (PCA) to study the performance of a k-nearest neighbors
classifier (k-NN), nearest class-centers classifier (NCC), and support vector
machines on the learned layer-wise representations of a ResNet-18 trained on
CIFAR-10. We show that in certain layers, as little as 20% of the intermediate
feature-space variance is necessary for high-accuracy classification and that
across all layers, the first ~100 PCs completely determine the performance of
the k-NN and NCC classifiers. We relate our findings to neural collapse and
provide partial evidence for the related phenomenon of intermediate neural
collapse. Our preliminary work provides three distinct yet interpretable
surrogate models for feature representation with an affine linear model the
best performing. We also show that leveraging several surrogate models affords
us a clever method to estimate where neural collapse may initially occur within
the DNN. | [
"Amit Harlev",
"Andrew Engel",
"Panos Stinis",
"Tony Chiang"
] | 2023-09-27 00:18:25 | http://arxiv.org/abs/2309.15328v1 | http://arxiv.org/pdf/2309.15328v1 | 2309.15328v1 |
Neural Operators for Accelerating Scientific Simulations and Design | Scientific discovery and engineering design are currently limited by the time
and cost of physical experiments, selected mostly through trial-and-error and
intuition that require deep domain expertise. Numerical simulations present an
alternative to physical experiments but are usually infeasible for complex
real-world domains due to the computational requirements of existing numerical
methods. Artificial intelligence (AI) presents a potential paradigm shift by
developing fast data-driven surrogate models. In particular, an AI framework,
known as neural operators, presents a principled framework for learning
mappings between functions defined on continuous domains, e.g., spatiotemporal
processes and partial differential equations (PDE). They can extrapolate and
predict solutions at new locations unseen during training, i.e., perform
zero-shot super-resolution. Neural operators can augment or even replace
existing simulators in many applications, such as computational fluid dynamics,
weather forecasting, and material modeling, while being 4-5 orders of magnitude
faster. Further, neural operators can be integrated with physics and other
domain constraints enforced at finer resolutions to obtain high-fidelity
solutions and good generalization. Since neural operators are differentiable,
they can directly optimize parameters for inverse design and other inverse
problems. We believe that neural operators present a transformative approach to
simulation and design, enabling rapid research and development. | [
"Kamyar Azizzadenesheli",
"Nikola Kovachki",
"Zongyi Li",
"Miguel Liu-Schiaffini",
"Jean Kossaifi",
"Anima Anandkumar"
] | 2023-09-27 00:12:07 | http://arxiv.org/abs/2309.15325v3 | http://arxiv.org/pdf/2309.15325v3 | 2309.15325v3 |
On the Power of SVD in the Stochastic Block Model | A popular heuristic method for improving clustering results is to apply
dimensionality reduction before running clustering algorithms. It has been
observed that spectral-based dimensionality reduction tools, such as PCA or
SVD, improve the performance of clustering algorithms in many applications.
This phenomenon indicates that spectral method not only serves as a
dimensionality reduction tool, but also contributes to the clustering procedure
in some sense. It is an interesting question to understand the behavior of
spectral steps in clustering problems.
As an initial step in this direction, this paper studies the power of
vanilla-SVD algorithm in the stochastic block model (SBM). We show that, in the
symmetric setting, vanilla-SVD algorithm recovers all clusters correctly. This
result answers an open question posed by Van Vu (Combinatorics Probability and
Computing, 2018) in the symmetric setting. | [
"Xinyu Mao",
"Jiapeng Zhang"
] | 2023-09-27 00:04:27 | http://arxiv.org/abs/2309.15322v1 | http://arxiv.org/pdf/2309.15322v1 | 2309.15322v1 |
DeepROCK: Error-controlled interaction detection in deep neural networks | The complexity of deep neural networks (DNNs) makes them powerful but also
makes them challenging to interpret, hindering their applicability in
error-intolerant domains. Existing methods attempt to reason about the internal
mechanism of DNNs by identifying feature interactions that influence prediction
outcomes. However, such methods typically lack a systematic strategy to
prioritize interactions while controlling confidence levels, making them
difficult to apply in practice for scientific discovery and hypothesis
validation. In this paper, we introduce a method, called DeepROCK, to address
this limitation by using knockoffs, which are dummy variables that are designed
to mimic the dependence structure of a given set of features while being
conditionally independent of the response. Together with a novel DNN
architecture involving a pairwise-coupling layer, DeepROCK jointly controls the
false discovery rate (FDR) and maximizes statistical power. In addition, we
identify a challenge in correctly controlling FDR using off-the-shelf feature
interaction importance measures. DeepROCK overcomes this challenge by proposing
a calibration procedure applied to existing interaction importance measures to
make the FDR under control at a target level. Finally, we validate the
effectiveness of DeepROCK through extensive experiments on simulated and real
datasets. | [
"Winston Chen",
"William Stafford Noble",
"Yang Young Lu"
] | 2023-09-26 23:58:19 | http://arxiv.org/abs/2309.15319v1 | http://arxiv.org/pdf/2309.15319v1 | 2309.15319v1 |
MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees | Decision trees remain one of the most popular machine learning models today,
largely due to their out-of-the-box performance and interpretability. In this
work, we present a Bayesian approach to decision tree induction via maximum a
posteriori inference of a posterior distribution over trees. We first
demonstrate a connection between maximum a posteriori inference of decision
trees and AND/OR search. Using this connection, we propose an AND/OR search
algorithm, dubbed MAPTree, which is able to recover the maximum a posteriori
tree. Lastly, we demonstrate the empirical performance of the maximum a
posteriori tree both on synthetic data and in real world settings. On 16 real
world datasets, MAPTree either outperforms baselines or demonstrates comparable
performance but with much smaller trees. On a synthetic dataset, MAPTree also
demonstrates greater robustness to noise and better generalization than
existing approaches. Finally, MAPTree recovers the maxiumum a posteriori tree
faster than existing sampling approaches and, in contrast with those
algorithms, is able to provide a certificate of optimality. The code for our
experiments is available at https://github.com/ThrunGroup/maptree. | [
"Colin Sullivan",
"Mo Tiwari",
"Sebastian Thrun"
] | 2023-09-26 23:43:37 | http://arxiv.org/abs/2309.15312v2 | http://arxiv.org/pdf/2309.15312v2 | 2309.15312v2 |
STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience | Terrain awareness, i.e., the ability to identify and distinguish different
types of terrain, is a critical ability that robots must have to succeed at
autonomous off-road navigation. Current approaches that provide robots with
this awareness either rely on labeled data which is expensive to collect,
engineered features and cost functions that may not generalize, or expert human
demonstrations which may not be available. Towards endowing robots with terrain
awareness without these limitations, we introduce Self-supervised TErrain
Representation LearnING (STERLING), a novel approach for learning terrain
representations that relies solely on easy-to-collect, unconstrained (e.g.,
non-expert), and unlabelled robot experience, with no additional constraints on
data collection. STERLING employs a novel multi-modal self-supervision
objective through non-contrastive representation learning to learn relevant
terrain representations for terrain-aware navigation. Through physical robot
experiments in off-road environments, we evaluate STERLING features on the task
of preference-aligned visual navigation and find that STERLING features perform
on par with fully supervised approaches and outperform other state-of-the-art
methods with respect to preference alignment. Additionally, we perform a
large-scale experiment of autonomously hiking a 3-mile long trail which
STERLING completes successfully with only two manual interventions,
demonstrating its robustness to real-world off-road conditions. | [
"Haresh Karnan",
"Elvin Yang",
"Daniel Farkash",
"Garrett Warnell",
"Joydeep Biswas",
"Peter Stone"
] | 2023-09-26 22:55:32 | http://arxiv.org/abs/2309.15302v2 | http://arxiv.org/pdf/2309.15302v2 | 2309.15302v2 |
Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field | In many areas of decision-making, forecasting is an essential pillar.
Consequently, many different forecasting methods have been proposed. From our
experience, recently presented forecasting methods are computationally
intensive, poorly automated, tailored to a particular data set, or they lack a
predictable time-to-result. To this end, we introduce Telescope, a novel
machine learning-based forecasting approach that automatically retrieves
relevant information from a given time series and splits it into parts,
handling each of them separately. In contrast to deep learning methods, our
approach doesn't require parameterization or the need to train and fit a
multitude of parameters. It operates with just one time series and provides
forecasts within seconds without any additional setup. Our experiments show
that Telescope outperforms recent methods by providing accurate and reliable
forecasts while making no assumptions about the analyzed time series. | [
"André Bauer",
"Mark Leznik",
"Michael Stenger",
"Robert Leppich",
"Nikolas Herbst",
"Samuel Kounev",
"Ian Foster"
] | 2023-09-26 22:42:25 | http://arxiv.org/abs/2309.15871v1 | http://arxiv.org/pdf/2309.15871v1 | 2309.15871v1 |
Beyond Log-Concavity: Theory and Algorithm for Sum-Log-Concave Optimization | This paper extends the classic theory of convex optimization to the
minimization of functions that are equal to the negated logarithm of what we
term as a sum-log-concave function, i.e., a sum of log-concave functions. In
particular, we show that such functions are in general not convex but still
satisfy generalized convexity inequalities. These inequalities unveil the key
importance of a certain vector that we call the cross-gradient and that is, in
general, distinct from the usual gradient. Thus, we propose the Cross Gradient
Descent (XGD) algorithm moving in the opposite direction of the cross-gradient
and derive a convergence analysis. As an application of our sum-log-concave
framework, we introduce the so-called checkered regression method relying on a
sum-log-concave function. This classifier extends (multiclass) logistic
regression to non-linearly separable problems since it is capable of
tessellating the feature space by using any given number of hyperplanes,
creating a checkerboard-like pattern of decision regions. | [
"Mastane Achab"
] | 2023-09-26 22:22:45 | http://arxiv.org/abs/2309.15298v1 | http://arxiv.org/pdf/2309.15298v1 | 2309.15298v1 |
Multiple Case Physics-Informed Neural Network for Biomedical Tube Flows | Fluid dynamics computations for tube-like geometries are important for
biomedical evaluation of vascular and airway fluid dynamics. Physics-Informed
Neural Networks (PINNs) have recently emerged as a good alternative to
traditional computational fluid dynamics (CFD) methods. The vanilla PINN,
however, requires much longer training time than the traditional CFD methods
for each specific flow scenario and thus does not justify its mainstream use.
Here, we explore the use of the multi-case PINN approach for calculating
biomedical tube flows, where varied geometry cases are parameterized and
pre-trained on the PINN, such that results for unseen geometries can be
obtained in real time. Our objective is to identify network architecture,
tube-specific, and regularization strategies that can optimize this, via
experiments on a series of idealized 2D stenotic tube flows. | [
"Hong Shen Wong",
"Wei Xuan Chan",
"Bing Huan Li",
"Choon Hwai Yap"
] | 2023-09-26 22:15:49 | http://arxiv.org/abs/2309.15294v2 | http://arxiv.org/pdf/2309.15294v2 | 2309.15294v2 |
Maximum Diffusion Reinforcement Learning | The assumption that data are independent and identically distributed
underpins all machine learning. When data are collected sequentially from agent
experiences this assumption does not generally hold, as in reinforcement
learning. Here, we derive a method that overcomes these limitations by
exploiting the statistical mechanics of ergodic processes, which we term
maximum diffusion reinforcement learning. By decorrelating agent experiences,
our approach provably enables agents to learn continually in single-shot
deployments regardless of how they are initialized. Moreover, we prove our
approach generalizes well-known maximum entropy techniques, and show that it
robustly exceeds state-of-the-art performance across popular benchmarks. Our
results at the nexus of physics, learning, and control pave the way towards
more transparent and reliable decision-making in reinforcement learning agents,
such as locomoting robots and self-driving cars. | [
"Thomas A. Berrueta",
"Allison Pinosky",
"Todd D. Murphey"
] | 2023-09-26 22:14:56 | http://arxiv.org/abs/2309.15293v2 | http://arxiv.org/pdf/2309.15293v2 | 2309.15293v2 |
Scaling Representation Learning from Ubiquitous ECG with State-Space Models | Ubiquitous sensing from wearable devices in the wild holds promise for
enhancing human well-being, from diagnosing clinical conditions and measuring
stress to building adaptive health promoting scaffolds. But the large volumes
of data therein across heterogeneous contexts pose challenges for conventional
supervised learning approaches. Representation Learning from biological signals
is an emerging realm catalyzed by the recent advances in computational modeling
and the abundance of publicly shared databases. The electrocardiogram (ECG) is
the primary researched modality in this context, with applications in health
monitoring, stress and affect estimation. Yet, most studies are limited by
small-scale controlled data collection and over-parameterized architecture
choices. We introduce \textbf{WildECG}, a pre-trained state-space model for
representation learning from ECG signals. We train this model in a
self-supervised manner with 275,000 10s ECG recordings collected in the wild
and evaluate it on a range of downstream tasks. The proposed model is a robust
backbone for ECG analysis, providing competitive performance on most of the
tasks considered, while demonstrating efficacy in low-resource regimes. The
code and pre-trained weights are shared publicly at
https://github.com/klean2050/tiles_ecg_model. | [
"Kleanthis Avramidis",
"Dominika Kunc",
"Bartosz Perz",
"Kranti Adsul",
"Tiantian Feng",
"Przemysław Kazienko",
"Stanisław Saganowski",
"Shrikanth Narayanan"
] | 2023-09-26 22:08:19 | http://arxiv.org/abs/2309.15292v1 | http://arxiv.org/pdf/2309.15292v1 | 2309.15292v1 |
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction | Motion prediction is crucial for autonomous vehicles to operate safely in
complex traffic environments. Extracting effective spatiotemporal relationships
among traffic elements is key to accurate forecasting. Inspired by the
successful practice of pretrained large language models, this paper presents
SEPT, a modeling framework that leverages self-supervised learning to develop
powerful spatiotemporal understanding for complex traffic scenes. Specifically,
our approach involves three masking-reconstruction modeling tasks on scene
inputs including agents' trajectories and road network, pretraining the scene
encoder to capture kinematics within trajectory, spatial structure of road
network, and interactions among roads and agents. The pretrained encoder is
then finetuned on the downstream forecasting task. Extensive experiments
demonstrate that SEPT, without elaborate architectural design or manual feature
engineering, achieves state-of-the-art performance on the Argoverse 1 and
Argoverse 2 motion forecasting benchmarks, outperforming previous methods on
all main metrics by a large margin. | [
"Zhiqian Lan",
"Yuxuan Jiang",
"Yao Mu",
"Chen Chen",
"Shengbo Eben Li",
"Hang Zhao",
"Keqiang Li"
] | 2023-09-26 21:56:03 | http://arxiv.org/abs/2309.15289v3 | http://arxiv.org/pdf/2309.15289v3 | 2309.15289v3 |
Composable Coresets for Determinant Maximization: Greedy is Almost Optimal | Given a set of $n$ vectors in $\mathbb{R}^d$, the goal of the
\emph{determinant maximization} problem is to pick $k$ vectors with the maximum
volume. Determinant maximization is the MAP-inference task for determinantal
point processes (DPP) and has recently received considerable attention for
modeling diversity. As most applications for the problem use large amounts of
data, this problem has been studied in the relevant \textit{composable coreset}
setting. In particular, [Indyk-Mahabadi-OveisGharan-Rezaei--SODA'20, ICML'19]
showed that one can get composable coresets with optimal approximation factor
of $\tilde O(k)^k$ for the problem, and that a local search algorithm achieves
an almost optimal approximation guarantee of $O(k)^{2k}$. In this work, we show
that the widely-used Greedy algorithm also provides composable coresets with an
almost optimal approximation factor of $O(k)^{3k}$, which improves over the
previously known guarantee of $C^{k^2}$, and supports the prior experimental
results showing the practicality of the greedy algorithm as a coreset. Our main
result follows by showing a local optimality property for Greedy: swapping a
single point from the greedy solution with a vector that was not picked by the
greedy algorithm can increase the volume by a factor of at most $(1+\sqrt{k})$.
This is tight up to the additive constant $1$. Finally, our experiments show
that the local optimality of the greedy algorithm is even lower than the
theoretical bound on real data sets. | [
"Siddharth Gollapudi",
"Sepideh Mahabadi",
"Varun Sivashankar"
] | 2023-09-26 21:46:44 | http://arxiv.org/abs/2309.15286v1 | http://arxiv.org/pdf/2309.15286v1 | 2309.15286v1 |
A Physics Enhanced Residual Learning (PERL) Framework for Traffic State Prediction | In vehicle trajectory prediction, physics models and data-driven models are
two predominant methodologies. However, each approach presents its own set of
challenges: physics models fall short in predictability, while data-driven
models lack interpretability. Addressing these identified shortcomings, this
paper proposes a novel framework, the Physics-Enhanced Residual Learning (PERL)
model. PERL integrates the strengths of physics-based and data-driven methods
for traffic state prediction. PERL contains a physics model and a residual
learning model. Its prediction is the sum of the physics model result and a
predicted residual as a correction to it. It preserves the interpretability
inherent to physics-based models and has reduced data requirements compared to
data-driven methods. Experiments were conducted using a real-world vehicle
trajectory dataset. We proposed a PERL model, with the Intelligent Driver Model
(IDM) as its physics car-following model and Long Short-Term Memory (LSTM) as
its residual learning model. We compare this PERL model with the physics
car-following model, data-driven model, and other physics-informed neural
network (PINN) models. The result reveals that PERL achieves better prediction
with a small dataset, compared to the physics model, data-driven model, and
PINN model. Second, the PERL model showed faster convergence during training,
offering comparable performance with fewer training samples than the
data-driven model and PINN model. Sensitivity analysis also proves comparable
performance of PERL using another residual learning model and a physics
car-following model. | [
"Keke Long",
"Haotian Shi",
"Zihao Sheng",
"Xiaopeng Li",
"Sikai Chen"
] | 2023-09-26 21:41:45 | http://arxiv.org/abs/2309.15284v1 | http://arxiv.org/pdf/2309.15284v1 | 2309.15284v1 |
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models | Robots need to have a memory of previously observed, but currently occluded
objects to work reliably in realistic environments. We investigate the problem
of encoding object-oriented memory into a multi-object manipulation reasoning
and planning framework. We propose DOOM and LOOM, which leverage transformer
relational dynamics to encode the history of trajectories given partial-view
point clouds and an object discovery and tracking engine. Our approaches can
perform multiple challenging tasks including reasoning with occluded objects,
novel objects appearance, and object reappearance. Throughout our extensive
simulation and real-world experiments, we find that our approaches perform well
in terms of different numbers of objects and different numbers of distractor
actions. Furthermore, we show our approaches outperform an implicit memory
baseline. | [
"Yixuan Huang",
"Jialin Yuan",
"Chanho Kim",
"Pupul Pradhan",
"Bryan Chen",
"Li Fuxin",
"Tucker Hermans"
] | 2023-09-26 21:31:24 | http://arxiv.org/abs/2309.15278v1 | http://arxiv.org/pdf/2309.15278v1 | 2309.15278v1 |
Efficient Low-rank Backpropagation for Vision Transformer Adaptation | The increasing scale of vision transformers (ViT) has made the efficient
fine-tuning of these large models for specific needs a significant challenge in
various applications. This issue originates from the computationally demanding
matrix multiplications required during the backpropagation process through
linear layers in ViT. In this paper, we tackle this problem by proposing a new
Low-rank BackPropagation via Walsh-Hadamard Transformation (LBP-WHT) method.
Intuitively, LBP-WHT projects the gradient into a low-rank space and carries
out backpropagation. This approach substantially reduces the computation needed
for adapting ViT, as matrix multiplication in the low-rank space is far less
resource-intensive. We conduct extensive experiments with different models
(ViT, hybrid convolution-ViT model) on multiple datasets to demonstrate the
effectiveness of our method. For instance, when adapting an EfficientFormer-L1
model on CIFAR100, our LBP-WHT achieves 10.4% higher accuracy than the
state-of-the-art baseline, while requiring 9 MFLOPs less computation. As the
first work to accelerate ViT adaptation with low-rank backpropagation, our
LBP-WHT method is complementary to many prior efforts and can be combined with
them for better performance. | [
"Yuedong Yang",
"Hung-Yueh Chiang",
"Guihong Li",
"Diana Marculescu",
"Radu Marculescu"
] | 2023-09-26 21:27:55 | http://arxiv.org/abs/2309.15275v1 | http://arxiv.org/pdf/2309.15275v1 | 2309.15275v1 |
Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning | Purpose: To identify ocular hypertension (OHT) subtypes with different trends
of visual field (VF) progression based on unsupervised machine learning and to
discover factors associated with fast VF progression. Participants: A total of
3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with
at least five follow-up VF tests were included in the study. Methods: We used a
latent class mixed model (LCMM) to identify OHT subtypes using standard
automated perimetry (SAP) mean deviation (MD) trajectories. We characterized
the subtypes based on demographic, clinical, ocular, and VF factors at the
baseline. We then identified factors driving fast VF progression using
generalized estimating equation (GEE) and justified findings qualitatively and
quantitatively. Results: The LCMM model discovered four clusters (subtypes) of
eyes with different trajectories of MD worsening. The number of eyes in
clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labelled the
clusters as Improvers, Stables, Slow progressors, and Fast progressors based on
their mean of MD decline, which were 0.08, -0.06, -0.21, and -0.45 dB/year,
respectively. Eyes with fast VF progression had higher baseline age,
intraocular pressure (IOP), pattern standard deviation (PSD) and refractive
error (RE), but lower central corneal thickness (CCT). Fast progression was
associated with calcium channel blockers, being male, heart disease history,
diabetes history, African American race, stroke history, and migraine
headaches. | [
"Xiaoqin Huang",
"Asma Poursoroush",
"Jian Sun",
"Michael V. Boland",
"Chris Johnson",
"Siamak Yousefi"
] | 2023-09-26 20:46:40 | http://arxiv.org/abs/2309.15867v1 | http://arxiv.org/pdf/2309.15867v1 | 2309.15867v1 |
STARC: A General Framework For Quantifying Differences Between Reward Functions | In order to solve a task using reinforcement learning, it is necessary to
first formalise the goal of that task as a reward function. However, for many
real-world tasks, it is very difficult to manually specify a reward function
that never incentivises undesirable behaviour. As a result, it is increasingly
popular to use reward learning algorithms, which attempt to learn a reward
function from data. However, the theoretical foundations of reward learning are
not yet well-developed. In particular, it is typically not known when a given
reward learning algorithm with high probability will learn a reward function
that is safe to optimise. This means that reward learning algorithms generally
must be evaluated empirically, which is expensive, and that their failure modes
are difficult to predict in advance. One of the roadblocks to deriving better
theoretical guarantees is the lack of good methods for quantifying the
difference between reward functions. In this paper we provide a solution to
this problem, in the form of a class of pseudometrics on the space of all
reward functions that we call STARC (STAndardised Reward Comparison) metrics.
We show that STARC metrics induce both an upper and a lower bound on worst-case
regret, which implies that our metrics are tight, and that any metric with the
same properties must be bilipschitz equivalent to ours. Moreover, we also
identify a number of issues with reward metrics proposed by earlier works.
Finally, we evaluate our metrics empirically, to demonstrate their practical
efficacy. STARC metrics can be used to make both theoretical and empirical
analysis of reward learning algorithms both easier and more principled. | [
"Joar Skalse",
"Lucy Farnik",
"Sumeet Ramesh Motwani",
"Erik Jenner",
"Adam Gleave",
"Alessandro Abate"
] | 2023-09-26 20:31:19 | http://arxiv.org/abs/2309.15257v1 | http://arxiv.org/pdf/2309.15257v1 | 2309.15257v1 |
Method and Validation for Optimal Lineup Creation for Daily Fantasy Football Using Machine Learning and Linear Programming | Daily fantasy sports (DFS) are weekly or daily online contests where
real-game performances of individual players are converted to fantasy points
(FPTS). Users select players for their lineup to maximize their FPTS within a
set player salary cap. This paper focuses on (1) the development of a method to
forecast NFL player performance under uncertainty and (2) determining an
optimal lineup to maximize FPTS under a set salary limit. A supervised learning
neural network was created and used to project FPTS based on past player
performance (2018 NFL regular season for this work) prior to the upcoming week.
These projected FPTS were used in a mixed integer linear program to find the
optimal lineup. The performance of resultant lineups was compared to
randomly-created lineups. On average, the optimal lineups outperformed the
random lineups. The generated lineups were then compared to real-world lineups
from users on DraftKings. The generated lineups generally fell in approximately
the 31st percentile (median). The FPTS methods and predictions presented here
can be further improved using this study as a baseline comparison. | [
"Joseph M. Mahoney",
"Tomasz B. Paniak"
] | 2023-09-26 20:26:32 | http://arxiv.org/abs/2309.15253v2 | http://arxiv.org/pdf/2309.15253v2 | 2309.15253v2 |
V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration | This paper presents a LiDAR-based end-to-end autonomous driving method with
Vehicle-to-Everything (V2X) communication integration, termed V2X-Lead, to
address the challenges of navigating unregulated urban scenarios under
mixed-autonomy traffic conditions. The proposed method aims to handle imperfect
partial observations by fusing the onboard LiDAR sensor and V2X communication
data. A model-free and off-policy deep reinforcement learning (DRL) algorithm
is employed to train the driving agent, which incorporates a carefully designed
reward function and multi-task learning technique to enhance generalization
across diverse driving tasks and scenarios. Experimental results demonstrate
the effectiveness of the proposed approach in improving safety and efficiency
in the task of traversing unsignalized intersections in mixed-autonomy traffic,
and its generalizability to previously unseen scenarios, such as roundabouts.
The integration of V2X communication offers a significant data source for
autonomous vehicles (AVs) to perceive their surroundings beyond onboard
sensors, resulting in a more accurate and comprehensive perception of the
driving environment and more safe and robust driving behavior. | [
"Zhiyun Deng",
"Yanjun Shi",
"Weiming Shen"
] | 2023-09-26 20:26:03 | http://arxiv.org/abs/2309.15252v1 | http://arxiv.org/pdf/2309.15252v1 | 2309.15252v1 |
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets | We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to
detect geometric anomalies in Multimodal geospatial datasets. Geospatial data
comprises of acquired and derived heterogeneous data modalities that we
transform to semantically meaningful, image-like tensors to address the
challenges of representation, alignment, and fusion of multimodal data. SeMAnD
is comprised of (i) a simple data augmentation strategy, called
RandPolyAugment, capable of generating diverse augmentations of vector
geometries, and (ii) a self-supervised training objective with three components
that incentivize learning representations of multimodal data that are
discriminative to local changes in one modality which are not corroborated by
the other modalities. Detecting local defects is crucial for geospatial anomaly
detection where even small anomalies (e.g., shifted, incorrectly connected,
malformed, or missing polygonal vector geometries like roads, buildings,
landcover, etc.) are detrimental to the experience and safety of users of
geospatial applications like mapping, routing, search, and recommendation
systems. Our empirical study on test sets of different types of real-world
geometric geospatial anomalies across 3 diverse geographical regions
demonstrates that SeMAnD is able to detect real-world defects and outperforms
domain-agnostic anomaly detection strategies by 4.8-19.7% as measured using
anomaly classification AUC. We also show that model performance increases (i)
up to 20.4% as the number of input modalities increase and (ii) up to 22.9% as
the diversity and strength of training data augmentations increase. | [
"Daria Reshetova",
"Swetava Ganguli",
"C. V. Krishnakumar Iyer",
"Vipul Pandey"
] | 2023-09-26 20:18:31 | http://arxiv.org/abs/2309.15245v1 | http://arxiv.org/pdf/2309.15245v1 | 2309.15245v1 |
Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks | In this paper, we present a novel training approach called the Homotopy
Relaxation Training Algorithm (HRTA), aimed at accelerating the training
process in contrast to traditional methods. Our algorithm incorporates two key
mechanisms: one involves building a homotopy activation function that
seamlessly connects the linear activation function with the ReLU activation
function; the other technique entails relaxing the homotopy parameter to
enhance the training refinement process. We have conducted an in-depth analysis
of this novel method within the context of the neural tangent kernel (NTK),
revealing significantly improved convergence rates. Our experimental results,
especially when considering networks with larger widths, validate the
theoretical conclusions. This proposed HRTA exhibits the potential for other
activation functions and deep neural networks. | [
"Yahong Yang",
"Qipin Chen",
"Wenrui Hao"
] | 2023-09-26 20:18:09 | http://arxiv.org/abs/2309.15244v1 | http://arxiv.org/pdf/2309.15244v1 | 2309.15244v1 |
Learning Using Generated Privileged Information by Text-to-Image Diffusion Models | Learning Using Privileged Information is a particular type of knowledge
distillation where the teacher model benefits from an additional data
representation during training, called privileged information, improving the
student model, which does not see the extra representation. However, privileged
information is rarely available in practice. To this end, we propose a text
classification framework that harnesses text-to-image diffusion models to
generate artificial privileged information. The generated images and the
original text samples are further used to train multimodal teacher models based
on state-of-the-art transformer-based architectures. Finally, the knowledge
from multimodal teachers is distilled into a text-based (unimodal) student.
Hence, by employing a generative model to produce synthetic data as privileged
information, we guide the training of the student model. Our framework, called
Learning Using Generated Privileged Information (LUGPI), yields noticeable
performance gains on four text classification data sets, demonstrating its
potential in text classification without any additional cost during inference. | [
"Rafael-Edy Menadil",
"Mariana-Iuliana Georgescu",
"Radu Tudor Ionescu"
] | 2023-09-26 20:04:48 | http://arxiv.org/abs/2309.15238v1 | http://arxiv.org/pdf/2309.15238v1 | 2309.15238v1 |
Cross-Validation for Training and Testing Co-occurrence Network Inference Algorithms | Microorganisms are found in almost every environment, including the soil,
water, air, and inside other organisms, like animals and plants. While some
microorganisms cause diseases, most of them help in biological processes such
as decomposition, fermentation and nutrient cycling. A lot of research has gone
into studying microbial communities in various environments and how their
interactions and relationships can provide insights into various diseases.
Co-occurrence network inference algorithms help us understand the complex
associations of micro-organisms, especially bacteria. Existing network
inference algorithms employ techniques such as correlation, regularized linear
regression, and conditional dependence, which have different hyper-parameters
that determine the sparsity of the network. Previous methods for evaluating the
quality of the inferred network include using external data, and network
consistency across sub-samples, both which have several drawbacks that limit
their applicability in real microbiome composition data sets. We propose a
novel cross-validation method to evaluate co-occurrence network inference
algorithms, and new methods for applying existing algorithms to predict on test
data. Our empirical study shows that the proposed method is useful for
hyper-parameter selection (training) and comparing the quality of the inferred
networks between different algorithms (testing). | [
"Daniel Agyapong",
"Jeffrey Ryan Propster",
"Jane Marks",
"Toby Dylan Hocking"
] | 2023-09-26 19:43:15 | http://arxiv.org/abs/2309.15225v1 | http://arxiv.org/pdf/2309.15225v1 | 2309.15225v1 |
Collaborative Watermarking for Adversarial Speech Synthesis | Advances in neural speech synthesis have brought us technology that is not
only close to human naturalness, but is also capable of instant voice cloning
with little data, and is highly accessible with pre-trained models available.
Naturally, the potential flood of generated content raises the need for
synthetic speech detection and watermarking. Recently, considerable research
effort in synthetic speech detection has been related to the Automatic Speaker
Verification and Spoofing Countermeasure Challenge (ASVspoof), which focuses on
passive countermeasures. This paper takes a complementary view to generated
speech detection: a synthesis system should make an active effort to watermark
the generated speech in a way that aids detection by another machine, but
remains transparent to a human listener. We propose a collaborative training
scheme for synthetic speech watermarking and show that a HiFi-GAN neural
vocoder collaborating with the ASVspoof 2021 baseline countermeasure models
consistently improves detection performance over conventional classifier
training. Furthermore, we demonstrate how collaborative training can be paired
with augmentation strategies for added robustness against noise and
time-stretching. Finally, listening tests demonstrate that collaborative
training has little adverse effect on perceptual quality of vocoded speech. | [
"Lauri Juvela",
"Xin Wang"
] | 2023-09-26 19:43:14 | http://arxiv.org/abs/2309.15224v1 | http://arxiv.org/pdf/2309.15224v1 | 2309.15224v1 |
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition | We propose a neural language modeling system based on low-rank adaptation
(LoRA) for speech recognition output rescoring. Although pretrained language
models (LMs) like BERT have shown superior performance in second-pass
rescoring, the high computational cost of scaling up the pretraining stage and
adapting the pretrained models to specific domains limit their practical use in
rescoring. Here we present a method based on low-rank decomposition to train a
rescoring BERT model and adapt it to new domains using only a fraction (0.08%)
of the pretrained parameters. These inserted matrices are optimized through a
discriminative training objective along with a correlation-based regularization
loss. The proposed low-rank adaptation Rescore-BERT (LoRB) architecture is
evaluated on LibriSpeech and internal datasets with decreased training times by
factors between 5.4 and 3.6. | [
"Yu Yu",
"Chao-Han Huck Yang",
"Jari Kolehmainen",
"Prashanth G. Shivakumar",
"Yile Gu",
"Sungho Ryu",
"Roger Ren",
"Qi Luo",
"Aditya Gourav",
"I-Fan Chen",
"Yi-Chieh Liu",
"Tuan Dinh",
"Ankur Gandhe",
"Denis Filimonov",
"Shalini Ghosh",
"Andreas Stolcke",
"Ariya Rastow",
"Ivan Bulyko"
] | 2023-09-26 19:41:34 | http://arxiv.org/abs/2309.15223v2 | http://arxiv.org/pdf/2309.15223v2 | 2309.15223v2 |
Auto-grading C programming assignments with CodeBERT and Random Forest Regressor | Grading coding assignments manually is challenging due to complexity and
subjectivity. However, auto-grading with deep learning simplifies the task. It
objectively assesses code quality, detects errors, and assigns marks
accurately, reducing the burden on instructors while ensuring efficient and
fair assessment. This study provides an analysis of auto-grading of the C
programming assignments using machine learning and deep learning approaches
like regression, convolutional neural networks (CNN) and long short-term memory
(LSTM). Using a code-based transformer word embedding model called CodeBERT,
the textual code inputs were transformed into vectors, and the vectors were
then fed into several models. The testing findings demonstrated the efficacy of
the suggested strategy with a root mean squared error (RMSE) of 1.89. The
contrast between statistical methods and deep learning techniques is discussed
in the study. | [
"Roshan Vasu Muddaluru",
"Sharvaani Ravikumar Thoguluva",
"Shruti Prabha",
"Dr. Peeta Basa Pati",
"Ms. Roshni M Balakrishnan"
] | 2023-09-26 19:21:09 | http://arxiv.org/abs/2309.15216v1 | http://arxiv.org/pdf/2309.15216v1 | 2309.15216v1 |
Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting | Machine learning for time-series forecasting remains a key area of research.
Despite successful application of many machine learning techniques, relating
computational efficiency to forecast error remains an under-explored domain.
This paper addresses this topic through a series of real-time experiments to
quantify the relationship between computational cost and forecast error using
meteorological nowcasting as an example use-case. We employ a variety of
popular regression techniques (XGBoost, FC-MLP, Transformer, and LSTM) for
multi-horizon, short-term forecasting of three variables (temperature, wind
speed, and cloud cover) for multiple locations. During a 5-day live experiment,
4000 data sources were streamed for training and inferencing 144 models per
hour. These models were parameterized to explore forecast error for two
computational cost minimization methods: a novel auto-adaptive data reduction
technique (Variance Horizon) and a performance-based concept drift-detection
mechanism. Forecast error of all model variations were benchmarked in real-time
against a state-of-the-art numerical weather prediction model. Performance was
assessed using classical and novel evaluation metrics. Results indicate that
using the Variance Horizon reduced computational usage by more than 50\%, while
increasing between 0-15\% in error. Meanwhile, performance-based retraining
reduced computational usage by up to 90\% while \emph{also} improving forecast
error by up to 10\%. Finally, the combination of both the Variance Horizon and
performance-based retraining outperformed other model configurations by up to
99.7\% when considering error normalized to computational usage. | [
"Elin Törnquist",
"Wagner Costa Santos",
"Timothy Pogue",
"Nicholas Wingle",
"Robert A. Caulk"
] | 2023-09-26 19:10:00 | http://arxiv.org/abs/2309.15207v1 | http://arxiv.org/pdf/2309.15207v1 | 2309.15207v1 |
ICML 2023 Topological Deep Learning Challenge : Design and Results | This paper presents the computational challenge on topological deep learning
that was hosted within the ICML 2023 Workshop on Topology and Geometry in
Machine Learning. The competition asked participants to provide open-source
implementations of topological neural networks from the literature by
contributing to the python packages TopoNetX (data processing) and TopoModelX
(deep learning). The challenge attracted twenty-eight qualifying submissions in
its two-month duration. This paper describes the design of the challenge and
summarizes its main findings. | [
"Mathilde Papillon",
"Mustafa Hajij",
"Florian Frantzen",
"Josef Hoppe",
"Helen Jenne",
"Johan Mathe",
"Audun Myers",
"Theodore Papamarkou",
"Michael T. Schaub",
"Ghada Zamzmi",
"Tolga Birdal",
"Tamal Dey",
"Tim Doster",
"Tegan Emerson",
"Gurusankar Gopalakrishnan",
"Devendra Govil",
"Vincent Grande",
"Aldo Guzmán-Sáenz",
"Henry Kvinge",
"Neal Livesay",
"Jan Meisner",
"Soham Mukherjee",
"Shreyas N. Samaga",
"Karthikeyan Natesan Ramamurthy",
"Maneel Reddy Karri",
"Paul Rosen",
"Sophia Sanborn",
"Michael Scholkemper",
"Robin Walters",
"Jens Agerberg",
"Georg Bökman",
"Sadrodin Barikbin",
"Claudio Battiloro",
"Gleb Bazhenov",
"Guillermo Bernardez",
"Aiden Brent",
"Sergio Escalera",
"Simone Fiorellino",
"Dmitrii Gavrilev",
"Mohammed Hassanin",
"Paul Häusner",
"Odin Hoff Gardaa",
"Abdelwahed Khamis",
"Manuel Lecha",
"German Magai",
"Tatiana Malygina",
"Pavlo Melnyk",
"Rubén Ballester",
"Kalyan Nadimpalli",
"Alexander Nikitin",
"Abraham Rabinowitz",
"Alessandro Salatiello",
"Simone Scardapane",
"Luca Scofano",
"Suraj Singh",
"Jens Sjölund",
"Pavel Snopov",
"Indro Spinelli",
"Lev Telyatnikov",
"Lucia Testa",
"Maosheng Yang",
"Yixiao Yue",
"Olga Zaghen",
"Ali Zia",
"Nina Miolane"
] | 2023-09-26 18:49:30 | http://arxiv.org/abs/2309.15188v2 | http://arxiv.org/pdf/2309.15188v2 | 2309.15188v2 |
Monitoring Machine Learning Models: Online Detection of Relevant Deviations | Machine learning models are essential tools in various domains, but their
performance can degrade over time due to changes in data distribution or other
factors. On one hand, detecting and addressing such degradations is crucial for
maintaining the models' reliability. On the other hand, given enough data, any
arbitrary small change of quality can be detected. As interventions, such as
model re-training or replacement, can be expensive, we argue that they should
only be carried out when changes exceed a given threshold. We propose a
sequential monitoring scheme to detect these relevant changes. The proposed
method reduces unnecessary alerts and overcomes the multiple testing problem by
accounting for temporal dependence of the measured model quality. Conditions
for consistency and specified asymptotic levels are provided. Empirical
validation using simulated and real data demonstrates the superiority of our
approach in detecting relevant changes in model quality compared to benchmark
methods. Our research contributes a practical solution for distinguishing
between minor fluctuations and meaningful degradations in machine learning
model performance, ensuring their reliability in dynamic environments. | [
"Florian Heinrichs"
] | 2023-09-26 18:46:37 | http://arxiv.org/abs/2309.15187v1 | http://arxiv.org/pdf/2309.15187v1 | 2309.15187v1 |
Conservative World Models | Zero-shot reinforcement learning (RL) promises to provide agents that can
perform any task in an environment after an offline pre-training phase.
Forward-backward (FB) representations represent remarkable progress towards
this ideal, achieving 85% of the performance of task-specific agents in this
setting. However, such performance is contingent on access to large and diverse
datasets for pre-training, which cannot be expected for most real problems.
Here, we explore how FB performance degrades when trained on small datasets
that lack diversity, and mitigate it with conservatism, a well-established
feature of performant offline RL algorithms. We evaluate our family of methods
across various datasets, domains and tasks, reaching 150% of vanilla FB
performance in aggregate. Somewhat surprisingly, conservative FB algorithms
also outperform the task-specific baseline, despite lacking access to reward
labels and being required to maintain policies for all tasks. Conservative FB
algorithms perform no worse than FB on full datasets, and so present little
downside over their predecessor. Our code is available open-source via
https://enjeeneer.io/projects/conservative-world-models/. | [
"Scott Jeen",
"Tom Bewley",
"Jonathan M. Cullen"
] | 2023-09-26 18:20:20 | http://arxiv.org/abs/2309.15178v1 | http://arxiv.org/pdf/2309.15178v1 | 2309.15178v1 |
Revealing the Power of Spatial-Temporal Masked Autoencoders in Multivariate Time Series Forecasting | Multivariate time series (MTS) forecasting involves predicting future time
series data based on historical observations. Existing research primarily
emphasizes the development of complex spatial-temporal models that capture
spatial dependencies and temporal correlations among time series variables
explicitly. However, recent advances have been impeded by challenges relating
to data scarcity and model robustness. To address these issues, we propose
Spatial-Temporal Masked Autoencoders (STMAE), an MTS forecasting framework that
leverages masked autoencoders to enhance the performance of spatial-temporal
baseline models. STMAE consists of two learning stages. In the pretraining
stage, an encoder-decoder architecture is employed. The encoder processes the
partially visible MTS data produced by a novel dual-masking strategy, including
biased random walk-based spatial masking and patch-based temporal masking.
Subsequently, the decoders aim to reconstruct the masked counterparts from both
spatial and temporal perspectives. The pretraining stage establishes a
challenging pretext task, compelling the encoder to learn robust
spatial-temporal patterns. In the fine-tuning stage, the pretrained encoder is
retained, and the original decoder from existing spatial-temporal models is
appended for forecasting. Extensive experiments are conducted on multiple MTS
benchmarks. The promising results demonstrate that integrating STMAE into
various spatial-temporal models can largely enhance their MTS forecasting
capability. | [
"Jiarui Sun",
"Yujie Fan",
"Chin-Chia Michael Yeh",
"Wei Zhang",
"Girish Chowdhary"
] | 2023-09-26 18:05:19 | http://arxiv.org/abs/2309.15169v1 | http://arxiv.org/pdf/2309.15169v1 | 2309.15169v1 |
SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem | In this work, we consider the optimization process of minibatch stochastic
gradient descent (SGD) on a 2-layer neural network with data separated by a
quadratic ground truth function. We prove that with data drawn from the
$d$-dimensional Boolean hypercube labeled by the quadratic ``XOR'' function $y
= -x_ix_j$, it is possible to train to a population error $o(1)$ with $d
\:\text{polylog}(d)$ samples. Our result considers simultaneously training both
layers of the two-layer-neural network with ReLU activations via standard
minibatch SGD on the logistic loss. To our knowledge, this work is the first to
give a sample complexity of $\tilde{O}(d)$ for efficiently learning the XOR
function on isotropic data on a standard neural network with standard training.
Our main technique is showing that the network evolves in two phases: a
$\textit{signal-finding}$ phase where the network is small and many of the
neurons evolve independently to find features, and a $\textit{signal-heavy}$
phase, where SGD maintains and balances the features. We leverage the
simultaneous training of the layers to show that it is sufficient for only a
small fraction of the neurons to learn features, since those neurons will be
amplified by the simultaneous growth of their second layer weights. | [
"Margalit Glasgow"
] | 2023-09-26 17:57:44 | http://arxiv.org/abs/2309.15111v2 | http://arxiv.org/pdf/2309.15111v2 | 2309.15111v2 |
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models | We investigate the internal behavior of Transformer-based Large Language
Models (LLMs) when they generate factually incorrect text. We propose modeling
factual queries as Constraint Satisfaction Problems and use this framework to
investigate how the model interacts internally with factual constraints.
Specifically, we discover a strong positive relation between the model's
attention to constraint tokens and the factual accuracy of its responses. In
our curated suite of 11 datasets with over 40,000 prompts, we study the task of
predicting factual errors with the Llama-2 family across all scales (7B, 13B,
70B). We propose SAT Probe, a method probing self-attention patterns, that can
predict constraint satisfaction and factual errors, and allows early error
identification. The approach and findings demonstrate how using the mechanistic
understanding of factuality in LLMs can enhance reliability. | [
"Mert Yuksekgonul",
"Varun Chandrasekaran",
"Erik Jones",
"Suriya Gunasekar",
"Ranjita Naik",
"Hamid Palangi",
"Ece Kamar",
"Besmira Nushi"
] | 2023-09-26 17:48:55 | http://arxiv.org/abs/2309.15098v1 | http://arxiv.org/pdf/2309.15098v1 | 2309.15098v1 |
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