pwc-archive/papers-with-abstracts huggingface dataset:
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities
Authors: Andreas Kirsch, Yarin Gal
Date: 2022-08-01 00:00:00
Tasks: Active Learning, Informativeness

Recently proposed methods in data subset selection, that is active learning and active sampling, use Fisher information, Hessians, similarity matrices based on gradients, and gradient lengths to estimate how informative data is for a model's training. Are these different approaches connected, and if so, how? We revisit the fundamentals of Bayesian optimal experiment design and show that these recently proposed methods can be understood as approximations to information-theoretic quantities: among them, the mutual information between predictions and model parameters, known as expected information gain or BALD in machine learning, and the mutual information between predictions of acquisition candidates and test samples, known as expected predictive information gain. We develop a comprehensive set of approximations using Fisher information and observed information and derive a unified framework that connects seemingly disparate literature. Although Bayesian methods are often seen as separate from non-Bayesian ones, the sometimes fuzzy notion of "informativeness" expressed in various non-Bayesian objectives leads to the same couple of information quantities, which were, in principle, already known by Lindley (1956) and MacKay (1992).
Learning to generate Reliable Broadcast Algorithms
Authors: Diogo Vaz, David R. Matos, Miguel L. Pardal, Miguel Correia
Date: 2022-07-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Modern distributed systems are supported by fault-tolerant algorithms, like Reliable Broadcast and Consensus, that assure the correct operation of the system even when some of the nodes of the system fail. However, the development of distributed algorithms is a manual and complex process, resulting in scientific papers that usually present a single algorithm or variations of existing ones. To automate the process of developing such algorithms, this work presents an intelligent agent that uses Reinforcement Learning to generate correct and efficient fault-tolerant distributed algorithms. We show that our approach is able to generate correct fault-tolerant Reliable Broadcast algorithms with the same performance of others available in the literature, in only 12,000 learning episodes.
Online Decentralized Frank-Wolfe: From theoretical bound to applications in smart-building
Authors: Angan Mitra, Nguyen Kim Thang, Tuan-Anh Nguyen, Denis Trystram, Paul Youssef
Date: 2022-07-31 00:00:00

The design of decentralized learning algorithms is important in the fast-growing world in which data are distributed over participants with limited local computation resources and communication. In this direction, we propose an online algorithm minimizing non-convex loss functions aggregated from individual data/models distributed over a network. We provide the theoretical performance guarantee of our algorithm and demonstrate its utility on a real life smart building.
Formal guarantees for heuristic optimization algorithms used in machine learning
Authors: Xiaoyu Li
Date: 2022-07-31 00:00:00
Tasks: BIG-bench Machine Learning, Stochastic Optimization

Recently, Stochastic Gradient Descent (SGD) and its variants have become the dominant methods in the large-scale optimization of machine learning (ML) problems. A variety of strategies have been proposed for tuning the step sizes, ranging from adaptive step sizes to heuristic methods to change the step size in each iteration. Also, momentum has been widely employed in ML tasks to accelerate the training process. Yet, there is a gap in our theoretical understanding of them. In this work, we start to close this gap by providing formal guarantees to a few heuristic optimization methods and proposing improved algorithms. First, we analyze a generalized version of the AdaGrad (Delayed AdaGrad) step sizes in both convex and non-convex settings, showing that these step sizes allow the algorithms to automatically adapt to the level of noise of the stochastic gradients. We show for the first time sufficient conditions for Delayed AdaGrad to achieve almost sure convergence of the gradients to zero. Moreover, we present a high probability analysis for Delayed AdaGrad and its momentum variant in the non-convex setting. Second, we analyze SGD with exponential and cosine step sizes, which are empirically successful but lack theoretical support. We provide the very first convergence guarantees for them in the smooth and non-convex setting, with and without the Polyak-{\L}ojasiewicz (PL) condition. We also show their good property of adaptivity to noise under the PL condition. Third, we study the last iterate of momentum methods. We prove the first lower bound in the convex setting for the last iterate of SGD with constant momentum. Moreover, we investigate a class of Follow-The-Regularized-Leader-based momentum algorithms with increasing momentum and shrinking updates. We show that their last iterate has optimal convergence for unconstrained convex stochastic optimization problems.
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning
Authors: Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber, Radu Teodorescu
Date: 2022-07-31 00:00:00
Tasks: BIG-bench Machine Learning, CPU, GPU

DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to cause incorrect results that can be beneficial to an attacker or damaging to the victim. Recent works have proposed approximate computation as a defense mechanism against machine learning attacks. We show that these approaches, while successful for a range of inputs, are insufficient to address stronger, high-confidence adversarial attacks. To address this, we propose DNNSHIELD, a hardware-accelerated defense that adapts the strength of the response to the confidence of the adversarial input. Our approach relies on dynamic and random sparsification of the DNN model to achieve inference approximation efficiently and with fine-grain control over the approximation error. DNNSHIELD uses the output distribution characteristics of sparsified inference compared to a dense reference to detect adversarial inputs. We show an adversarial detection rate of 86% when applied to VGG16 and 88% when applied to ResNet50, which exceeds the detection rate of the state of the art approaches, with a much lower overhead. We demonstrate a software/hardware-accelerated FPGA prototype, which reduces the performance impact of DNNSHIELD relative to software-only CPU and GPU implementations.
Scrutinizing Shipment Records To Thwart Illegal Timber Trade
Authors: Debanjan Datta, Sathappan Muthiah, John Simeone, Amelia Meadows, Naren Ramakrishnan
Date: 2022-07-31 00:00:00
Tasks: Anomaly Detection, Contrastive Learning, Unsupervised Anomaly Detection

Timber and forest products made from wood, like furniture, are valuable commodities, and like the global trade of many highly-valued natural resources, face challenges of corruption, fraud, and illegal harvesting. These grey and black market activities in the wood and forest products sector are not limited to the countries where the wood was harvested, but extend throughout the global supply chain and have been tied to illicit financial flows, like trade-based money laundering, document fraud, species mislabeling, and other illegal activities. The task of finding such fraudulent activities using trade data, in the absence of ground truth, can be modelled as an unsupervised anomaly detection problem. However existing approaches suffer from certain shortcomings in their applicability towards large scale trade data. Trade data is heterogeneous, with both categorical and numerical attributes in a tabular format. The overall challenge lies in the complexity, volume and velocity of data, with large number of entities and lack of ground truth labels. To mitigate these, we propose a novel unsupervised anomaly detection -- Contrastive Learning based Heterogeneous Anomaly Detection (CHAD) that is generally applicable for large-scale heterogeneous tabular data. We demonstrate our model CHAD performs favorably against multiple comparable baselines for public benchmark datasets, and outperforms them in the case of trade data. More importantly we demonstrate our approach reduces assumptions and efforts required hyperparameter tuning, which is a key challenging aspect in an unsupervised training paradigm. Specifically, our overarching objective pertains to detecting suspicious timber shipments and patterns using Bill of Lading trade record data. Detecting anomalous transactions in shipment records can enable further investigation by government agencies and supply chain constituents.
Adaptive Edge Offloading for Image Classification Under Rate Limit
Authors: Jiaming Qiu, Ruiqi Wang, Ayan Chakrabarti, Roch Guerin, Chenyang Lu
Date: 2022-07-31 00:00:00
Tasks: Classification, image-classification, Image Classification

This paper considers a setting where embedded devices are used to acquire and classify images. Because of limited computing capacity, embedded devices rely on a parsimonious classification model with uneven accuracy. When local classification is deemed inaccurate, devices can decide to offload the image to an edge server with a more accurate but resource-intensive model. Resource constraints, e.g., network bandwidth, however, require regulating such transmissions to avoid congestion and high latency. The paper investigates this offloading problem when transmissions regulation is through a token bucket, a mechanism commonly used for such purposes. The goal is to devise a lightweight, online offloading policy that optimizes an application-specific metric (e.g., classification accuracy) under the constraints of the token bucket. The paper develops a policy based on a Deep Q-Network (DQN), and demonstrates both its efficacy and the feasibility of its deployment on embedded devices. Of note is the fact that the policy can handle complex input patterns, including correlation in image arrivals and classification accuracy. The evaluation is carried out by performing image classification over a local testbed using synthetic traces generated from the ImageNet image classification benchmark. Implementation of this work is available at https://github.com/qiujiaming315/edgeml-dqn.
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers
Authors: Ji Xin, Raphael Tang, Zhiying Jiang, YaoLiang Yu, Jimmy Lin
Date: 2022-07-31 00:00:00
Tasks: Quantization

There exists a wide variety of efficiency methods for natural language processing (NLP) tasks, such as pruning, distillation, dynamic inference, quantization, etc. We can consider an efficiency method as an operator applied on a model. Naturally, we may construct a pipeline of multiple efficiency methods, i.e., to apply multiple operators on the model sequentially. In this paper, we study the plausibility of this idea, and more importantly, the commutativity and cumulativeness of efficiency operators. We make two interesting observations: (1) Efficiency operators are commutative -- the order of efficiency methods within the pipeline has little impact on the final results; (2) Efficiency operators are also cumulative -- the final results of combining several efficiency methods can be estimated by combining the results of individual methods. These observations deepen our understanding of efficiency operators and provide useful guidelines for their real-world applications.
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination
Authors: Abdalkarim Mohtasib, Gerhard Neumann, Heriberto Cuayahuitl
Date: 2022-07-31 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning robotic tasks in the real world is still highly challenging and effective practical solutions remain to be found. Traditional methods used in this area are imitation learning and reinforcement learning, but they both have limitations when applied to real robots. Combining reinforcement learning with pre-collected demonstrations is a promising approach that can help in learning control policies to solve robotic tasks. In this paper, we propose an algorithm that uses novel techniques to leverage offline expert data using offline and online training to obtain faster convergence and improved performance. The proposed algorithm (AWET) weights the critic losses with a novel agent advantage weight to improve over the expert data. In addition, AWET makes use of an automatic early termination technique to stop and discard policy rollouts that are not similar to expert trajectories -- to prevent drifting far from the expert data. In an ablation study, AWET showed improved and promising performance when compared to state-of-the-art baselines on four standard robotic tasks.
Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift
Authors: Brian Xiang, Abdelrahman Abdelmonsef
Date: 2022-07-31 00:00:00
Tasks: Benchmarking, EEG, Electroencephalogram (EEG)

The main challenges of using electroencephalogram (EEG) signals to make eye-tracking (ET) predictions are the differences in distributional patterns between benchmark data and real-world data and the noise resulting from the unintended interference of brain signals from multiple sources. Increasing the robustness of machine learning models in predicting eye-tracking position from EEG data is therefore integral for both research and consumer use. In medical research, the usage of more complicated data collection methods to test for simpler tasks has been explored to address this very issue. In this study, we propose a fine-grain data approach for EEG-ET data collection in order to create more robust benchmarking. We train machine learning models utilizing both coarse-grain and fine-grain data and compare their accuracies when tested on data of similar/different distributional patterns in order to determine how susceptible EEG-ET benchmarks are to differences in distributional data. We apply a covariate distributional shift to test for this susceptibility. Results showed that models trained on fine-grain, vector-based data were less susceptible to distributional shifts than models trained on coarse-grain, binary-classified data.
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
Authors: Sergio A. Balanya, Juan Maroñas, Daniel Ramos
Date: 2022-07-31 00:00:00
Tasks: Inductive Bias, Medical Diagnosis

In this paper, we study the post-hoc calibration of modern neural networks, a problem that has drawn a lot of attention in recent years. Many calibration methods of varying complexity have been proposed for the task, but there is no consensus about how expressive these should be. We focus on the task of confidence scaling, specifically on post-hoc methods that generalize Temperature Scaling, we call these the Adaptive Temperature Scaling family. We analyse expressive functions that improve calibration and propose interpretable methods. We show that when there is plenty of data complex models like neural networks yield better performance, but are prone to fail when the amount of data is limited, a common situation in certain post-hoc calibration applications like medical diagnosis. We study the functions that expressive methods learn under ideal conditions and design simpler methods but with a strong inductive bias towards these well-performing functions. Concretely, we propose Entropy-based Temperature Scaling, a simple method that scales the confidence of a prediction according to its entropy. Results show that our method obtains state-of-the-art performance when compared to others and, unlike complex models, it is robust against data scarcity. Moreover, our proposed model enables a deeper interpretation of the calibration process.
Unitary Approximate Message Passing for Matrix Factorization
Authors: Zhengdao Yuan, Qinghua Guo, Yonina C. Eldar, Yonghui Li
Date: 2022-07-31 00:00:00
Tasks: Compressive Sensing, Dictionary Learning, Variational Inference

We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas. Leveraging variational inference (VI) and unitary approximate message passing (UAMP), we develop a Bayesian approach to MF with an efficient message passing implementation, called UAMPMF. With proper priors imposed on the factor matrices, UAMPMF can be used to solve many problems that can be formulated as MF, such as non negative matrix factorization, dictionary learning, compressive sensing with matrix uncertainty, robust principal component analysis, and sparse matrix factorization. Extensive numerical examples are provided to show that UAMPMF significantly outperforms state-of-the-art algorithms in terms of recovery accuracy, robustness and computational complexity.
Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI
Authors: Semen Budennyy, Vladimir Lazarev, Nikita Zakharenko, Alexey Korovin, Olga Plosskaya, Denis Dimitrov, Vladimir Arkhipkin, Ivan Oseledets, Ivan Barsola, Ilya Egorov, Aleksandra Kosterina, Leonid Zhukov
Date: 2022-07-31 00:00:00

The size and complexity of deep neural networks continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce an open-source package eco2AI to help data scientists and researchers to track energy consumption and equivalent CO2 emissions of their models in a straightforward way. In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. We encourage research community to search for new optimal Artificial Intelligence (AI) architectures with a lower computational cost. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization
Authors: Bin Liu, Jiwen Wang, Ruirui Wang, Yaxu Wang, Guangzu Zhao
Date: 2022-07-31 00:00:00
Tasks: Decision Making

The decision-making of TBM operating parameters has an important guiding significance for TBM safe and efficient construction, and it has been one of the research hotpots in the field of TBM tunneling. For this purpose, this paper introduces rock-breaking rules into machine learning method, and a rock-machine mapping dual-driven by physical-rule and data-mining is established with high accuracy. This dual-driven mappings are subsequently used as objective function and constraints to build a decision-making method for TBM operating parameters. By searching the revolution per minute and penetration corresponding to the extremum of the objective function subject to the constraints, the optimal operating parameters can be obtained. This method is verified in the field of the Second Water Source Channel of Hangzhou, China, resulting in the average penetration rate increased by 11.3%, and the total cost decreased by 10.0%, which proves the practicability and effectiveness of the developed decision-making model.
An Experimental Study on Learning Correlated Equilibrium in Routing Games
Authors: Yixian Zhu, Ketan Savla
Date: 2022-07-31 00:00:00

We study route choice in a repeated routing game where an uncertain state of nature determines link latency functions, and agents receive private route recommendation. The state is sampled in an i.i.d. manner in every round from a publicly known distribution, and the recommendations are generated by a randomization policy whose mapping from the state is known publicly. In a one-shot setting, the agents are said to obey recommendation if it gives the smallest travel time in a posteriori expectation. A plausible extension to repeated setting is that the likelihood of following recommendation in a round is related to regret from previous rounds. If the regret is of satisficing type with respect to a default choice and is averaged over past rounds and over all agents, then the asymptotic outcome under an obedient recommendation policy coincides with the one-shot outcome. We report findings from an experiment with one participant at a time engaged in repeated route choice decision on computer. In every round, the participant is shown travel time distribution for each route, a route recommendation generated by an obedient policy, and a rating suggestive of average experience of previous participants with the quality of recommendation. Upon entering route choice, the actual travel times are revealed. The participant evaluates the quality of recommendation by submitting a review. This is combined with historical reviews to update rating for the next round. Data analysis from 33 participants each with 100 rounds suggests moderate negative correlation between the display rating and the average regret, and a strong positive correlation between the rating and the likelihood of following recommendation. Overall, under obedient recommendation policy, the rating converges close to its maximum value by the end of the experiments in conjunction with very high frequency of following recommendations.
Neural Correlates of Face Familiarity Perception
Authors: Evan Ehrenberg, Kleovoulos Leo Tsourides, Hossein Nejati, Ngai-Man Cheung, Pawan Sinha
Date: 2022-07-31 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Face Recognition

In the domain of face recognition, there exists a puzzling timing discrepancy between results from macaque neurophysiology on the one hand and human electrophysiology on the other. Single unit recordings in macaques have demonstrated face identity specific responses in extra-striate visual cortex within 100 milliseconds of stimulus onset. In EEG and MEG experiments with humans, however, a consistent distinction between neural activity corresponding to unfamiliar and familiar faces has been reported to emerge around 250 ms. This points to the possibility that there may be a hitherto undiscovered early correlate of face familiarity perception in human electrophysiological traces. We report here a successful search for such a correlate in dense MEG recordings using pattern classification techniques. Our analyses reveal markers of face familiarity as early as 85 ms after stimulus onset. Low-level attributes of the images, such as luminance and color distributions, are unable to account for this early emerging response difference. These results help reconcile human and macaque data, and provide clues regarding neural mechanisms underlying familiar face perception.
What Do Deep Neural Networks Find in Disordered Structures of Glasses?
Authors: Norihiro Oyama, Shihori Koyama, Takeshi Kawasaki
Date: 2022-07-31 00:00:00

Glass transitions are widely observed in various types of soft matter systems. However, the physical mechanism of these transitions remains {elusive}, despite years of ambitious research. In particular, an important unanswered question is whether the glass transition is accompanied by a divergence of the correlation lengths of the characteristic static structures. In this study, we develop a deep-neural-network-based method that is used to extract the characteristic local meso-structures solely from instantaneous {particle} configurations without any {information} about the dynamics. We first train a neural network to classify configurations of liquids and glasses correctly. Then, we obtain the characteristic structures by quantifying the grounds for the decisions made by the network using Gradient-weighted Class Activation Mapping (Grad-CAM). We considered two qualitatively different glass-forming binary systems, and through comparisons with several established structural indicators, we demonstrate that our system can be used to identify characteristic structures that depend on the details of the systems. Moreover, the extracted structures are remarkably correlated with the nonequilibrium aging dynamics in thermal fluctuations.