pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Majority Voting in Digital Hardware
Authors: Stefan Baumgartner, Mario Huemer, Michael Lunglmayr
Date: 2021-08-09 00:00:00
Tasks: Ensemble Learning, Handwritten Digit Recognition

In recent years, machine learning methods became increasingly important for a manifold number of applications. However, they often suffer from high computational requirements impairing their efficient use in real-time systems, even when employing dedicated hardware accelerators. Ensemble learning methods are especially suitable for hardware acceleration since they can be constructed from individual learners of low complexity and thus offer large parallelization potential. For classification, the outputs of these learners are typically combined by majority voting, which often represents the bottleneck of a hardware accelerator for ensemble inference. In this work, we present a novel architecture that allows obtaining a majority decision in a number of clock cycles that is logarithmic in the number of inputs. We show, that for the example application of handwritten digit recognition a random forest processing engine employing this majority decision architecture implemented on an FPGA allows the classification of more than seven million images per second.
VeRLPy: Python Library for Verification of Digital Designs with Reinforcement Learning
Authors: Aebel Joe Shibu, Sadhana S, Shilpa N, Pratyush Kumar
Date: 2021-08-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Digital hardware is verified by comparing its behavior against a reference model on a range of randomly generated input signals. The random generation of the inputs hopes to achieve sufficient coverage of the different parts of the design. However, such coverage is often difficult to achieve, amounting to large verification efforts and delays. An alternative is to use Reinforcement Learning (RL) to generate the inputs by learning to prioritize those inputs which can more efficiently explore the design under test. In this work, we present VeRLPy an open-source library to allow RL-driven verification with limited additional engineering overhead. This contributes to two broad movements within the EDA community of (a) moving to open-source toolchains and (b) reducing barriers for development with Python support. We also demonstrate the use of VeRLPy for a few designs and establish its value over randomly generated input signals.
A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
Authors: Rayyan Ahmad Khan, Martin Kleinsteuber
Date: 2021-08-09 00:00:00
Tasks: Clustering, Node Classification

Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. HIN embedding has emerged as a promising research field for network analysis as it enables downstream tasks such as clustering and node classification. In this work, we propose \ours for joint learning of cluster embeddings as well as cluster-aware HIN embedding. We assume that the connected nodes are highly likely to fall in the same cluster, and adopt a variational approach to preserve the information in the pairwise relations in a cluster-aware manner. In addition, we deploy contrastive modules to simultaneously utilize the information in multiple meta-paths, thereby alleviating the meta-path selection problem - a challenge faced by many of the famous HIN embedding approaches. The HIN embedding, thus learned, not only improves the clustering performance but also preserves pairwise proximity as well as the high-order HIN structure. We show the effectiveness of our approach by comparing it with many competitive baselines on three real-world datasets on clustering and downstream node classification.
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces
Authors: Ziyad Sheebaelhamd, Konstantinos Zisis, Athina Nisioti, Dimitris Gkouletsos, Dario Pavllo, Jonas Kohler
Date: 2021-08-09 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Multi-agent control problems constitute an interesting area of application for deep reinforcement learning models with continuous action spaces. Such real-world applications, however, typically come with critical safety constraints that must not be violated. In order to ensure safety, we enhance the well-known multi-agent deep deterministic policy gradient (MADDPG) framework by adding a safety layer to the deep policy network. In particular, we extend the idea of linearizing the single-step transition dynamics, as was done for single-agent systems in Safe DDPG (Dalal et al., 2018), to multi-agent settings. We additionally propose to circumvent infeasibility problems in the action correction step using soft constraints (Kerrigan & Maciejowski, 2000). Results from the theory of exact penalty functions can be used to guarantee constraint satisfaction of the soft constraints under mild assumptions. We empirically find that the soft formulation achieves a dramatic decrease in constraint violations, making safety available even during the learning procedure.
Model architecture can transform catastrophic forgetting into positive transfer
Authors: Miguel Ruiz-Garcia
Date: 2021-08-09 00:00:00

The work of McCloskey and Cohen popularized the concept of catastrophic interference. They used a neural network that tried to learn addition using two groups of examples as two different tasks. In their case, learning the second task rapidly deteriorated the acquired knowledge about the previous one. We hypothesize that this could be a symptom of a fundamental problem: addition is an algorithmic task that should not be learned through pattern recognition. Therefore, other model architectures better suited for this task would avoid catastrophic forgetting. We use a neural network with a different architecture that can be trained to recover the correct algorithm for the addition of binary numbers. This neural network includes conditional clauses that are naturally treated within the back-propagation algorithm. We test it in the setting proposed by McCloskey and Cohen and training on random additions one by one. The neural network not only does not suffer from catastrophic forgetting but it improves its predictive power on unseen pairs of numbers as training progresses. We also show that this is a robust effect, also present when averaging many simulations. This work emphasizes the importance that neural network architecture has for the emergence of catastrophic forgetting and introduces a neural network that is able to learn an algorithm.
Efficient Hyperparameter Optimization for Differentially Private Deep Learning
Authors: Aman Priyanshu, Rakshit Naidu, FatemehSadat Mireshghallah, Mohammad Malekzadeh
Date: 2021-08-09 00:00:00
Tasks: Deep Learning, Hyperparameter Optimization

Tuning the hyperparameters in the differentially private stochastic gradient descent (DPSGD) is a fundamental challenge. Unlike the typical SGD, private datasets cannot be used many times for hyperparameter search in DPSGD; e.g., via a grid search. Therefore, there is an essential need for algorithms that, within a given search space, can find near-optimal hyperparameters for the best achievable privacy-utility tradeoffs efficiently. We formulate this problem into a general optimization framework for establishing a desirable privacy-utility tradeoff, and systematically study three cost-effective algorithms for being used in the proposed framework: evolutionary, Bayesian, and reinforcement learning. Our experiments, for hyperparameter tuning in DPSGD conducted on MNIST and CIFAR-10 datasets, show that these three algorithms significantly outperform the widely used grid search baseline. As this paper offers a first-of-a-kind framework for hyperparameter tuning in DPSGD, we discuss existing challenges and open directions for future studies. As we believe our work has implications to be utilized in the pipeline of private deep learning, we open-source our code at https://github.com/AmanPriyanshu/DP-HyperparamTuning.
Collapsing the Decision Tree: the Concurrent Data Predictor
Authors: Cristian Alb
Date: 2021-08-09 00:00:00

A family of concurrent data predictors is derived from the decision tree classifier by removing the limitation of sequentially evaluating attributes. By evaluating attributes concurrently, the decision tree collapses into a flat structure. Experiments indicate improvements of the prediction accuracy.
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications
Authors: Daniel Lee, Georgy Noarov, Mallesh Pai, Aaron Roth
Date: 2021-08-09 00:00:00
Tasks: Multiobjective Optimization

We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. Even though the learner's objective is not convex-concave (and so the minimax theorem does not apply), we give a simple algorithm that can compete with the setting in which the adversary must announce their action first, with optimally diminishing regret. We demonstrate the power of our framework by using it to (re)derive optimal bounds and efficient algorithms across a variety of domains, ranging from multicalibration to a large set of no regret algorithms, to a variant of Blackwell's approachability theorem for polytopes with fast convergence rates. As a new application, we show how to ``(multi)calibeat'' an arbitrary collection of forecasters -- achieving an exponentially improved dependence on the number of models we are competing against, compared to prior work.
Low-Complexity Algorithm for Restless Bandits with Imperfect Observations
Authors: Keqin Liu, Richard Weber, Chengzhong Zhang
Date: 2021-08-09 00:00:00
Tasks: Stochastic Optimization

We consider a class of restless bandit problems that finds a broad application area in reinforcement learning and stochastic optimization. We consider $N$ independent discrete-time Markov processes, each of which had two possible states: 1 and 0 (`good' and `bad'). Only if a process is both in state 1 and observed to be so does reward accrue. The aim is to maximize the expected discounted sum of returns over the infinite horizon subject to a constraint that only $M$ $(<N)$ processes may be observed at each step. Observation is error-prone: there are known probabilities that state 1 (0) will be observed as 0 (1). From this one knows, at any time $t$, a probability that process $i$ is in state 1. The resulting system may be modeled as a restless multi-armed bandit problem with an information state space of uncountable cardinality. Restless bandit problems with even finite state spaces are PSPACE-HARD in general. We propose a novel approach for simplifying the dynamic programming equations of this class of restless bandits and develop a low-complexity algorithm that achieves a strong performance and is readily extensible to the general restless bandit model with observation errors. Under certain conditions, we establish the existence (indexability) of Whittle index and its equivalence to our algorithm. When those conditions do not hold, we show by numerical experiments the near-optimal performance of our algorithm in the general parametric space. Furthermore, we theoretically prove the optimality of our algorithm for homogeneous systems.
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning
Authors: Wanqi Xue, Wei Qiu, Bo An, Zinovi Rabinovich, Svetlana Obraztsova, Chai Kiat Yeo
Date: 2021-08-09 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent studies in multi-agent communicative reinforcement learning (MACRL) have demonstrated that multi-agent coordination can be greatly improved by allowing communication between agents. Meanwhile, adversarial machine learning (ML) has shown that ML models are vulnerable to attacks. Despite the increasing concern about the robustness of ML algorithms, how to achieve robust communication in multi-agent reinforcement learning has been largely neglected. In this paper, we systematically explore the problem of adversarial communication in MACRL. Our main contributions are threefold. First, we propose an effective method to perform attacks in MACRL, by learning a model to generate optimal malicious messages. Second, we develop a defence method based on message reconstruction, to maintain multi-agent coordination under message attacks. Third, we formulate the adversarial communication problem as a two-player zero-sum game and propose a game-theoretical method R-MACRL to improve the worst-case defending performance. Empirical results demonstrate that many state-of-the-art MACRL methods are vulnerable to message attacks, and our method can significantly improve their robustness.
EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations
Authors: Robert Buechler, Emmanuel Balogun, Arun Majumdar, Ram Rajagopal
Date: 2021-08-09 00:00:00

The nexus between transportation, the power grid, and consumer behavior is more pronounced than ever before as the race to decarbonize the transportation sector intensifies. Electrification in the transportation sector has led to technology shifts and rapid deployment of electric vehicles (EVs). The potential increase in stochastic and spatially heterogeneous charging load presents a unique challenge that is not well studied, and will have significant impacts on grid operations, emissions, and system reliability if not managed effectively. Realistic scenario generators can help operators prepare, and machine learning can be leveraged to this end. In this work, we develop generative adversarial networks (GANs) to learn distributions of electric vehicle (EV) charging sessions and disentangled representations. We show that this model structure successfully parameterizes unlabeled temporal and power patterns without supervision and is able to generate synthetic data conditioned on these parameters. We benchmark the generation capability of this model with Gaussian Mixture Models (GMMs), and empirically show that our proposed model framework is better at capturing charging distributions and temporal dynamics.
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization
Authors: Mostafa Pashazadeh, Kui Wu
Date: 2021-08-08 00:00:00
Tasks: Combinatorial Optimization, Graph Neural Network, reinforcement-learning, Reinforcement Learning (RL)

Combinatorial optimization problems (COPs) on the graph with real-life applications are canonical challenges in Computer Science. The difficulty of finding quality labels for problem instances holds back leveraging supervised learning across combinatorial problems. Reinforcement learning (RL) algorithms have recently been adopted to solve this challenge automatically. The underlying principle of this approach is to deploy a graph neural network (GNN) for encoding both the local information of the nodes and the graph-structured data in order to capture the current state of the environment. Then, it is followed by the actor to learn the problem-specific heuristics on its own and make an informed decision at each state for finally reaching a good solution. Recent studies on this subject mainly focus on a family of combinatorial problems on the graph, such as the travel salesman problem, where the proposed model aims to find an ordering of vertices that optimizes a given objective function. We use the security-aware phone clone allocation in the cloud as a classical quadratic assignment problem (QAP) to investigate whether or not deep RL-based model is generally applicable to solve other classes of such hard problems. Extensive empirical evaluation shows that existing RL-based model may not generalize to QAP.
Generalizing Dynamic Mode Decomposition: Balancing Accuracy and Expressiveness in Koopman Approximations
Authors: Masih Haseli, Jorge Cortés
Date: 2021-08-08 00:00:00

This paper tackles the data-driven approximation of unknown dynamical systems using Koopman-operator methods. Given a dictionary of functions, these methods approximate the projection of the action of the operator on the finite-dimensional subspace spanned by the dictionary. We propose the Tunable Symmetric Subspace Decomposition algorithm to refine the dictionary, balancing its expressiveness and accuracy. Expressiveness corresponds to the ability of the dictionary to describe the evolution of as many observables as possible and accuracy corresponds to the ability to correctly predict their evolution. Based on the observation that Koopman-invariant subspaces give rise to exact predictions, we reason that prediction accuracy is a function of the degree of invariance of the subspace generated by the dictionary and provide a data-driven measure to measure invariance proximity. The proposed algorithm iteratively prunes the initial functional space to identify a refined dictionary of functions that satisfies the desired level of accuracy while retaining as much of the original expressiveness as possible. We provide a full characterization of the algorithm properties and show that it generalizes both Extended Dynamic Mode Decomposition and Symmetric Subspace Decomposition. Simulations on planar systems show the effectiveness of the proposed methods in producing Koopman approximations of tunable accuracy that capture relevant information about the dynamical system.
Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences
Authors: Darshan Deshpande, Harshavardhan Abichandani
Date: 2021-08-08 00:00:00
Tasks: Audio Compression, Quantization, Spectral Reconstruction

With active research in audio compression techniques yielding substantial breakthroughs, spectral reconstruction of low-quality audio waves remains a less indulged topic. In this paper, we propose a novel approach for reconstructing higher frequencies from considerably longer sequences of low-quality MP3 audio waves. Our technique involves inpainting audio spectrograms with residually stacked autoencoder blocks by manipulating individual amplitude and phase values in relation to perceptual differences. Our architecture presents several bottlenecks while preserving the spectral structure of the audio wave via skip-connections. We also compare several task metrics and demonstrate our visual guide to loss selection. Moreover, we show how to leverage differential quantization techniques to reduce the initial model size by more than half while simultaneously reducing inference time, which is crucial in real-world applications.
Deep Neural Network for DrawiNg Networks, (DNN)^2
Authors: Loann Giovannangeli, Frederic Lalanne, David Auber, Romain Giot, Romain Bourqui
Date: 2021-08-08 00:00:00

By leveraging recent progress of stochastic gradient descent methods, several works have shown that graphs could be efficiently laid out through the optimization of a tailored objective function. In the meantime, Deep Learning (DL) techniques achieved great performances in many applications. We demonstrate that it is possible to use DL techniques to learn a graph-to-layout sequence of operations thanks to a graph-related objective function. In this paper, we present a novel graph drawing framework called (DNN)^2: Deep Neural Network for DrawiNg Networks. Our method uses Graph Convolution Networks to learn a model. Learning is achieved by optimizing a graph topology related loss function that evaluates (DNN)^2 generated layouts during training. Once trained, the (DNN)^ model is able to quickly lay any input graph out. We experiment (DNN)^2 and statistically compare it to optimization-based and regular graph layout algorithms. The results show that (DNN)^2 performs well and are encouraging as the Deep Learning approach to Graph Drawing is novel and many leads for future works are identified.
Using Biological Variables and Social Determinants to Predict Malaria and Anemia among Children in Senegal
Authors: Boubacar Sow, Hiroki Suguri, Hamid Mukhtar, Hafiz Farooq Ahmad
Date: 2021-08-08 00:00:00
Tasks: BIG-bench Machine Learning

Integrating machine learning techniques in healthcare becomes very common nowadays, and it contributes positively to improving clinical care and health decisions planning. Anemia and malaria are two life-threatening diseases in Africa that affect the red blood cells and reduce hemoglobin production. This paper focuses on analyzing child health data in Senegal using four machine learning algorithms in Python: KNN, Random Forests, SVM, and Na\"ive Bayes. Our task aims to investigate large-scale data from The Demographic and Health Survey (DHS) and to find out hidden information for anemia and malaria. We present two classification models for the two blood disorders using biological variables and social determinants. The findings of this research will contribute to improving child healthcare in Senegal by eradicating anemia and malaria, and decreasing the child mortality rate.
MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting
Authors: Yaobin Xu, Weitang Liu, Zhongyi Jiang, Zixuan Xu, Tingyun Mao, Lili Chen, Mingwei Zhou
Date: 2021-08-08 00:00:00
Tasks: Graph Neural Network

Traffic forecasting is a core element of intelligent traffic monitoring system. Approaches based on graph neural networks have been widely used in this task to effectively capture spatial and temporal dependencies of road networks. However, these approaches can not effectively define the complicated network topology. Besides, their cascade network structures have limitations in transmitting distinct features in the time and space dimensions. In this paper, we propose a Multi-adaptive Spatiotemporal-flow Graph Neural Network (MAF-GNN) for traffic speed forecasting. MAF-GNN introduces an effective Multi-adaptive Adjacency Matrices Mechanism to capture multiple latent spatial dependencies between traffic nodes. Additionally, we propose Spatiotemporal-flow Modules aiming to further enhance feature propagation in both time and space dimensions. MAF-GNN achieves better performance than other models on two real-world datasets of public traffic network, METR-LA and PeMS-Bay, demonstrating the effectiveness of the proposed approach.
A Look at the Evaluation Setup of the M5 Forecasting Competition
Authors: Hansika Hewamalage, Pablo Montero-Manso, Christoph Bergmeir, Rob J Hyndman
Date: 2021-08-08 00:00:00
Tasks: Benchmarking, Decision Making

Forecast evaluation plays a key role in how empirical evidence shapes the development of the discipline. Domain experts are interested in error measures relevant for their decision making needs. Such measures may produce unreliable results. Although reliability properties of several metrics have already been discussed, it has hardly been quantified in an objective way. We propose a measure named Rank Stability, which evaluates how much the rankings of an experiment differ in between similar datasets, when the models and errors are constant. We use this to study the evaluation setup of the M5. We find that the evaluation setup of the M5 is less reliable than other measures. The main drivers of instability are hierarchical aggregation and scaling. Price-weighting reduces the stability of all tested error measures. Scale normalization of the M5 error measure results in less stability than other scale-free errors. Hierarchical levels taken separately are less stable with more aggregation, and their combination is even less stable than individual levels. We also show positive tradeoffs of retaining aggregation importance without affecting stability. Aggregation and stability can be linked to the influence of much debated magic numbers. Many of our findings can be applied to general hierarchical forecast benchmarking.