pwc-archive/papers-with-abstracts huggingface dataset:
Materials Discovery using Max K-Armed Bandit
Authors: Nobuaki Kikkawa, Hiroshi Ohno
Date: 2022-12-16 00:00:00

Search algorithms for the bandit problems are applicable in materials discovery. However, the objectives of the conventional bandit problem are different from those of materials discovery. The conventional bandit problem aims to maximize the total rewards, whereas materials discovery aims to achieve breakthroughs in material properties. The max K-armed bandit (MKB) problem, which aims to acquire the single best reward, matches with the discovery tasks better than the conventional bandit. Thus, here, we propose a search algorithm for materials discovery based on the MKB problem using a pseudo-value of the upper confidence bound of expected improvement of the best reward. This approach is pseudo-guaranteed to be asymptotic oracles that do not depends on the time horizon. In addition, compared with other MKB algorithms, the proposed algorithm has only one hyperparameter, which is advantageous in materials discovery. We applied the proposed algorithm to synthetic problems and molecular-design demonstrations using a Monte Carlo tree search. According to the results, the proposed algorithm stably outperformed other bandit algorithms in the late stage of the search process when the optimal arm of the MKB could not be determined based on its expectation reward.
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs
Authors: Wenhui Cui, Haleh Akrami, Anand A. Joshi, Richard M. Leahy
Date: 2022-12-16 00:00:00
Tasks: Meta-Learning, Representation Learning, Self-Supervised Learning, Transfer Learning

Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data. However, traditional transfer learning methods often fail to generalize the pre-trained knowledge to the target task due to domain discrepancy. Self-supervised learning on graphs can increase the generalizability of graph features since self-supervision concentrates on inherent graph properties that are not limited to a particular supervised task. We propose a novel knowledge transfer strategy by integrating meta-learning with self-supervised learning to deal with the heterogeneity and scarcity of fMRI data. Specifically, we perform a self-supervised task on the source domain and apply meta-learning, which strongly improves the generalizability of the model using the bi-level optimization, to transfer the self-supervised knowledge to the target domain. Through experiments on a neurological disorder classification task, we demonstrate that the proposed strategy significantly improves target task performance by increasing the generalizability and transferability of graph-based knowledge.
Asymptotic Analysis of Deep Residual Networks
Authors: Rama Cont, Alain Rossier, Renyuan Xu
Date: 2022-12-15 00:00:00

We investigate the asymptotic properties of deep Residual networks (ResNets) as the number of layers increases. We first show the existence of scaling regimes for trained weights markedly different from those implicitly assumed in the neural ODE literature. We study the convergence of the hidden state dynamics in these scaling regimes, showing that one may obtain an ODE, a stochastic differential equation (SDE) or neither of these. In particular, our findings point to the existence of a diffusive regime in which the deep network limit is described by a class of stochastic differential equations (SDEs). Finally, we derive the corresponding scaling limits for the backpropagation dynamics.
The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources in Natural Language Understanding Systems
Authors: Akshatha Arodi, Martin Pömsl, Kaheer Suleman, Adam Trischler, Alexandra Olteanu, Jackie Chi Kit Cheung
Date: 2022-12-15 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Natural Language Understanding

Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make inferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model's pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution subtasks that require reasoning over multiple facts. These subtasks differ in terms of which knowledge sources contain the relevant facts. We also introduce subtasks where knowledge is present only at inference time using fictional knowledge. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources. Still, even the best performing models seem to have difficulties with reliably integrating knowledge presented only at inference time.
Learning Sparsity and Randomness for Data-driven Low Rank Approximation
Authors: Tiejin Chen, Yicheng Tao
Date: 2022-12-15 00:00:00

Learning-based low rank approximation algorithms can significantly improve the performance of randomized low rank approximation with sketch matrix. With the learned value and fixed non-zero positions for sketch matrices from learning-based algorithms, these matrices can reduce the test error of low rank approximation significantly. However, there is still no good method to learn non-zero positions as well as overcome the out-of-distribution performance loss. In this work, we introduce two new methods Learning Sparsity and Learning Randomness which try to learn a better sparsity patterns and add randomness to the value of sketch matrix. These two methods can be applied with any learning-based algorithms which use sketch matrix directly. Our experiments show that these two methods can improve the performance of previous learning-based algorithm for both test error and out-of-distribution test error without adding too much complexity.
Non-IID Transfer Learning on Graphs
Authors: Jun Wu, Jingrui He, Elizabeth Ainsworth
Date: 2022-12-15 00:00:00
Tasks: Generalization Bounds, GRAPH DOMAIN ADAPTATION, Link Prediction, Node Classification, Transfer Learning

Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The crucial idea is to characterize the cross-network knowledge transferability from the perspective of the Weisfeiler-Lehman graph isomorphism test. To this end, we propose a novel Graph Subtree Discrepancy to measure the graph distribution shift between source and target graphs. Then the generalization error bounds on cross-network transfer learning, including both cross-network node classification and link prediction tasks, can be derived in terms of the source knowledge and the Graph Subtree Discrepancy across domains. This thereby motivates us to propose a generic graph adaptive network (GRADE) to minimize the distribution shift between source and target graphs for cross-network transfer learning. Experimental results verify the effectiveness and efficiency of our GRADE framework on both cross-network node classification and cross-domain recommendation tasks.
Reliable Measures of Spread in High Dimensional Latent Spaces
Authors: Anna C. Marbut, Katy McKinney-Bock, Travis J. Wheeler
Date: 2022-12-15 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Understanding geometric properties of natural language processing models' latent spaces allows the manipulation of these properties for improved performance on downstream tasks. One such property is the amount of data spread in a model's latent space, or how fully the available latent space is being used. In this work, we define data spread and demonstrate that the commonly used measures of data spread, Average Cosine Similarity and a partition function min/max ratio I(V), do not provide reliable metrics to compare the use of latent space across models. We propose and examine eight alternative measures of data spread, all but one of which improve over these current metrics when applied to seven synthetic data distributions. Of our proposed measures, we recommend one principal component-based measure and one entropy-based measure that provide reliable, relative measures of spread and can be used to compare models of different sizes and dimensionalities.
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs
Authors: Alejandro Parada-Mayorga, Zhiyang Wang, Alejandro Ribeiro
Date: 2022-12-15 00:00:00
Tasks: Dimensionality Reduction

In this paper we propose a pooling approach for convolutional information processing on graphs relying on the theory of graphons and limits of dense graph sequences. We present three methods that exploit the induced graphon representation of graphs and graph signals on partitions of [0, 1]2 in the graphon space. As a result we derive low dimensional representations of the convolutional operators, while a dimensionality reduction of the signals is achieved by simple local interpolation of functions in L2([0, 1]). We prove that those low dimensional representations constitute a convergent sequence of graphs and graph signals, respectively. The methods proposed and the theoretical guarantees that we provide show that the reduced graphs and signals inherit spectral-structural properties of the original quantities. We evaluate our approach with a set of numerical experiments performed on graph neural networks (GNNs) that rely on graphon pooling. We observe that graphon pooling performs significantly better than other approaches proposed in the literature when dimensionality reduction ratios between layers are large. We also observe that when graphon pooling is used we have, in general, less overfitting and lower computational cost.
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Authors: Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang
Date: 2022-12-15 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years. In addition to learning attention in time domain, recent works also explore learning attention in frequency domains (e.g., Fourier domain, wavelet domain), given that seasonal patterns can be better captured in these domains. In this work, we seek to understand the relationships between attention models in different time and frequency domains. Theoretically, we show that attention models in different domains are equivalent under linear conditions (i.e., linear kernel to attention scores). Empirically, we analyze how attention models of different domains show different behaviors through various synthetic experiments with seasonality, trend and noise, with emphasis on the role of softmax operation therein. Both these theoretical and empirical analyses motivate us to propose a new method: TDformer (Trend Decomposition Transformer), that first applies seasonal-trend decomposition, and then additively combines an MLP which predicts the trend component with Fourier attention which predicts the seasonal component to obtain the final prediction. Extensive experiments on benchmark time-series forecasting datasets demonstrate that TDformer achieves state-of-the-art performance against existing attention-based models.
Efficient Long Sequence Modeling via State Space Augmented Transformer
Authors: Simiao Zuo, Xiaodong Liu, Jian Jiao, Denis Charles, Eren Manavoglu, Tuo Zhao, Jianfeng Gao
Date: 2022-12-15 00:00:00
Tasks: Computational Efficiency, Decoder, Language Modeling, Language Modelling, Natural Language Understanding, State Space Models, Text Generation

Transformer models have achieved superior performance in various natural language processing tasks. However, the quadratic computational cost of the attention mechanism limits its practicality for long sequences. There are existing attention variants that improve the computational efficiency, but they have limited ability to effectively compute global information. In parallel to Transformer models, state space models (SSMs) are tailored for long sequences, but they are not flexible enough to capture complicated local information. We propose SPADE, short for $\underline{\textbf{S}}$tate s$\underline{\textbf{P}}$ace $\underline{\textbf{A}}$ugmente$\underline{\textbf{D}}$ Transform$\underline{\textbf{E}}$r. Specifically, we augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers. The SSM augments global information, which complements the lack of long-range dependency issue in local attention methods. Experimental results on the Long Range Arena benchmark and language modeling tasks demonstrate the effectiveness of the proposed method. To further demonstrate the scalability of SPADE, we pre-train large encoder-decoder models and present fine-tuning results on natural language understanding and natural language generation tasks.
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Authors: Shivakanth Sujit, Pedro H. M. Braga, Jorg Bornschein, Samira Ebrahimi Kahou
Date: 2022-12-15 00:00:00
Tasks: Offline RL, reinforcement-learning, Reinforcement Learning (RL)

Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive; such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there exists no single established protocol for evaluating offline RL methods.In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.
An Empirical Study of Deep Learning Models for Vulnerability Detection
Authors: Benjamin Steenhoek, Md Mahbubur Rahman, Richard Jiles, Wei Le
Date: 2022-12-15 00:00:00
Tasks: Deep Learning, Vulnerability Detection

Deep learning (DL) models of code have recently reported great progress for vulnerability detection. In some cases, DL-based models have outperformed static analysis tools. Although many great models have been proposed, we do not yet have a good understanding of these models. This limits the further advancement of model robustness, debugging, and deployment for the vulnerability detection. In this paper, we surveyed and reproduced 9 state-of-the-art (SOTA) deep learning models on 2 widely used vulnerability detection datasets: Devign and MSR. We investigated 6 research questions in three areas, namely model capabilities, training data, and model interpretation. We experimentally demonstrated the variability between different runs of a model and the low agreement among different models' outputs. We investigated models trained for specific types of vulnerabilities compared to a model that is trained on all the vulnerabilities at once. We explored the types of programs DL may consider "hard" to handle. We investigated the relations of training data sizes and training data composition with model performance. Finally, we studied model interpretations and analyzed important features that the models used to make predictions. We believe that our findings can help better understand model results, provide guidance on preparing training data, and improve the robustness of the models. All of our datasets, code, and results are available at https://doi.org/10.6084/m9.figshare.20791240.
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection
Authors: Benjamin Steenhoek, Hongyang Gao, Wei Le
Date: 2022-12-15 00:00:00
Tasks: Deep Learning, Graph Learning, Language Modelling, Large Language Model, Vulnerability Detection

Deep learning-based vulnerability detection has shown great performance and, in some studies, outperformed static analysis tools. However, the highest-performing approaches use token-based transformer models, which are not the most efficient to capture code semantics required for vulnerability detection. Classical program analysis techniques such as dataflow analysis can detect many types of bugs based on their root causes. In this paper, we propose to combine such causal-based vulnerability detection algorithms with deep learning, aiming to achieve more efficient and effective vulnerability detection. Specifically, we designed DeepDFA, a dataflow analysis-inspired graph learning framework and an embedding technique that enables graph learning to simulate dataflow computation. We show that DeepDFA is both performant and efficient. DeepDFA outperformed all non-transformer baselines. It was trained in 9 minutes, 75x faster than the highest-performing baseline model. When using only 50+ vulnerable and several hundreds of total examples as training data, the model retained the same performance as 100% of the dataset. DeepDFA also generalized to real-world vulnerabilities in DbgBench; it detected 8.7 out of 17 vulnerabilities on average across folds and was able to distinguish between patched and buggy versions, while the highest-performing baseline models did not detect any vulnerabilities. By combining DeepDFA with a large language model, we surpassed the state-of-the-art vulnerability detection performance on the Big-Vul dataset with 96.46 F1 score, 97.82 precision, and 95.14 recall. Our replication package is located at https://doi.org/10.6084/m9.figshare.21225413 .
Learning to repeatedly solve routing problems
Authors: Mouad Morabit, Guy Desaulniers, Andrea Lodi
Date: 2022-12-15 00:00:00
Tasks: Combinatorial Optimization

In the last years, there has been a great interest in machine-learning-based heuristics for solving NP-hard combinatorial optimization problems. The developed methods have shown potential on many optimization problems. In this paper, we present a learned heuristic for the reoptimization of a problem after a minor change in its data. We focus on the case of the capacited vehicle routing problem with static clients (i.e., same client locations) and changed demands. Given the edges of an original solution, the goal is to predict and fix the ones that have a high chance of remaining in an optimal solution after a change of client demands. This partial prediction of the solution reduces the complexity of the problem and speeds up its resolution, while yielding a good quality solution. The proposed approach resulted in solutions with an optimality gap ranging from 0\% to 1.7\% on different benchmark instances within a reasonable computing time.
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers
Authors: Harry Coppock, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Kieran Baker, Jobie Budd, Richard Payne, Emma Karoune, David Hurley, Alexander Titcomb, Sabrina Egglestone, Ana Tendero Cañadas, Lorraine Butler, Radka Jersakova, Jonathon Mellor, Selina Patel, Tracey Thornley, Peter Diggle, Sylvia Richardson, Josef Packham, Björn W. Schuller, Davide Pigoli, Steven Gilmour, Stephen Roberts, Chris Holmes
Date: 2022-12-15 00:00:00

Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.
Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19
Authors: Davide Pigoli, Kieran Baker, Jobie Budd, Lorraine Butler, Harry Coppock, Sabrina Egglestone, Steven G. Gilmour, Chris Holmes, David Hurley, Radka Jersakova, Ivan Kiskin, Vasiliki Koutra, Jonathon Mellor, George Nicholson, Joe Packham, Selina Patel, Richard Payne, Stephen J. Roberts, Björn W. Schuller, Ana Tendero-Cañadas, Tracey Thornley, Alexander Titcomb
Date: 2022-12-15 00:00:00

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.