Publications

Realistic Evaluation of Transductive Few-Shot Learning - Supplementary Material
Olivier Veilleux
Éts Montréal
Malik Boudiaf
Pablo Piantanida
Ismail Ben
Ayed Éts Montreal
In the main tables of the paper, we did not include the performances of α-TIM in the standard balanced setting. Here, we emphasize that α-… (voir plus)TIM is a generalization of TIM [1] as when α → 1 (i.e., the α-entropies tend to the Shannon entropies), α-TIM tends to TIM. Therefore, in the standard setting, where optimal hyper-parameter α is obtained over validation tasks that are balanced (as in the standard validation tasks of the original TIM and the other existing methods), the performance of α-TIM is the same as TIM. When α is tuned on balanced validation tasks, we obtain an optimal value of α very close to 1, and our α-mutual information approaches the standard mutual information. When the validation tasks are uniformly random, as in our new setting and in the validation plots we provided in the main figure, one can see that the performance of α-TIM remains competitive when we tend to balanced testing tasks (i.e., when a is increasing), but is significantly better than TIM when we tend to uniformly-random testing tasks (a = 1). These results illustrate the flexibility of α-divergences, and are in line with the technical analysis provided in the main paper.
2021-12-31
(publié)
www.semanticscholar.org
Robustness of Whittle Index Policy to Model Approximation
Amit Sinha
Aditya Mahajan
2021-12-31
Social Science Research Network (publié)
doi.org
Scalable Operator Allocation for Multirobot Assistance: A Restless Bandit Approach
Abhinav Dahiya
Nima Akbarzadeh
Aditya Mahajan
Stephen L. Smith
In this article, we consider the problem of allocating human operators in a system with multiple semiautonomous robots. Each robot is requir… (voir plus)ed to perform an independent sequence of tasks, subject to a chance of failing and getting stuck in a fault state at every task. If and when required, a human operator can assist or teleoperate a robot. Conventional dynamic programming-based techniques used to solve such problems face scalability issues due to an exponential growth of state and action spaces with the number of robots and operators. In this article, we derive conditions under which the operator allocation problem satisfies a technical condition called indexability, thereby enabling the use of the Whittle index heuristic. The conditions are easy to check, and we show that they hold for a wide range of problems of interest. Our key insight is to leverage the structure of the value function of individual robots, resulting in conditions that can be verified separately for each state of each robot. We apply these conditions to two types of transitions commonly seen in remote robot supervision systems. Through numerical simulations, we demonstrate the efficacy of Whittle index policy as a near-optimal and scalable approach that outperforms existing scalable methods.
2021-12-31
IEEE Transactions on Control of Network Systems (publié)
doi.org
arxiv.org
Self-Supervised Acoustic Anomaly Detection Via Contrastive Learning.
Hadi Hojjati
Narges Armanfard
We propose an acoustic anomaly detection algorithm based on the framework of contrastive learning. Contrastive learning is a recently propos… (voir plus)ed self-supervised approach that has shown promising results in image classification and speech recognition. However, its application in anomaly detection is underexplored. Earlier studies have demonstrated that it can achieve state-of-the-art performance in image anomaly detection, but its capability in anomalous sound detection is yet to be investigated. For the first time, we propose a contrastive learning-based framework that is suitable for acoustic anomaly detection. Since most existing contrastive learning approaches are targeted toward images, the effect of other data transformations on the performance of the algorithm is unknown. Our framework learns a representation from unlabeled data by applying audio-specific data augmentations. We show that in the resulting latent space, normal and abnormal points are distinguishable. Experiments conducted on the MIMII dataset confirm that our approach can outperform competing methods in detecting anomalies.
2021-12-31
IEEE International Conference on Acoustics, Speech and Signal Processing (inconnu)
doi.org
Sharpness-Aware Training for Accurate Inference on Noisy DNN Accelerators
Goncalo Mordido
A. Chandar
Franccois Leduc-Primeau
Energy-efficient deep neural network (DNN) accelerators are prone to non-idealities that degrade DNN performance at inference time. To miti… (voir plus)gate such degradation, existing methods typically add perturbations to the DNN weights during training to simulate inference on noisy hardware. However, this often requires knowledge about the target hardware and leads to a trade-off between DNN performance and robustness, decreasing the former to increase the latter. In this work, we show that applying sharpness-aware training by optimizing for both the loss value and the loss sharpness significantly improves robustness to noisy hardware at inference time while also increasing DNN performance. We further motivate our results by showing a high correlation between loss sharpness and model robustness. We show superior performance compared to injecting noise during training and aggressive weight clipping on multiple architectures, optimizers, datasets, and training regimes without relying on any assumptions about the target hardware. This is observed on a generic noise model as well as on accurate noise simulations from real hardware.
2021-12-31
arXiv.org (prépublication)
doi.org
Single Allocation Hub Location with Heterogeneous Economies of Scale
Borzou Rostami
Masoud Chitsaz
Okan Arslan
Gilbert Laporte
Andrea Lodi
The economies of scale in hub location is usually modeled by a constant parameter, which captures the benefits companies obtain through cons… (voir plus)olidation. In their article “Single allocation hub location with heterogeneous economies of scale,” Rostami et al. relax this assumption and consider hub-hub connection costs as piecewise linear functions of the flow amounts. This spoils the triangular inequality property of the distance matrix, making the classical flow-based model invalid and further complicates the problem. The authors tackle the challenge by building a mixed-integer quadratically constrained program and by developing a methodology based on constructing Lagrangian function, linear dual functions, and specialized polynomial-time algorithms to generate enhanced cuts. The developed method offers a new strategy in Benders-type decomposition through relaxing a set of complicating constraints in subproblems when such relaxation is tight. The results confirm the efficacy of the solution methods in solving large-scale problem instances.
2021-12-31
Operations Research (publié)
doi.org
Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'mah Fodiatu Yilla
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2021-12-31
arXiv.org (prépublication)
doi.org
Source-summary Entity Aggregation in Abstractive Summarization.
José-ángel González
Annie Priyadarshini Louis
Jackie CK Cheung
2021-12-31
COLING (publié)
dblp.uni-trier.de
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Yikang Shen
Zhouhan Lin
Athul Paul Jacob
Alessandro Sordoni
Aaron Courville
Yoshua Bengio
In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances,… (voir plus) for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shift-reduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models in the CTB dataset.
2021-12-31
Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
A Synchro-Set-Aided Breadth-First Sphere Decoder for Polar-Coded MIMO Systems
Huayi Zhou
Xiangyun Deng
Yiqian Cai
Yifei Shen
Minhua Yang
Warren J. Gross
Xiaohu You
Chuan Zhang
The joint optimization of multiple-input-multiple-output (MIMO) detection and polar decoding has become a research hotspot for future commun… (voir plus)ication systems. The error-correction performance of the separate detection and decoding (SDD) is far from the Shannon capacity, which cannot meet the requirements of communication scenarios such as ultra-reliable and low latency communications (URLLC). The existing joint detection and decoding (JDD) using breadth-first sphere decoding (BFSD) improves the reliability over SDD but still has a huge performance loss on low-rate codes. In this paper, JDD using synchro-set-aided BFSD (SA-BFSD) is proposed to greatly improve the error-correction performance for polar-coded MIMO systems. We first propose a method to generate the symbol synchro sets through the concept of frozen symbols, then refine the symbol synchro sets based on the characteristics analysis of the channel matrix. We optimize the enumerating order of the symbols and reduce the enumerating levels. The frame error rate (FER) and the bit error rate of the proposed algorithms are significantly improved especially for the low-rate codes. The proposed SA-BFSD JDD achieves an up to 7.8 dB performance gain over BFSD at FER
2021-12-31
IEEE Transactions on Signal Processing (publié)
doi.org
TaHiD: Tackling Data Hiding in Fake News Detection with News Propagation Networks
Adrien Benamira
Benjamin Devillers
Etienne Lesot
Ayush K. Ray
Manal Saadi
Fragkiskos D 587
Steven Bird
Ewan Klein
Edward Loper
Nat-593
Carlos Castillo
Marcelo Mendoza
Barbara Poblete
Daryna Dementieva
Alexander Panchenko
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Ashish Vaswani
Noam M. Shazeer … (voir 8 de plus)
Niki Parmar
Adriana Romero
Pietro Lio’
Yoshua Bengio
Yaqing Wang
Fenglong Ma
Zhiwei Jin
Ye Yuan
Fake news with detrimental societal effects has 001 attracted extensive attention and research. De-002 spite early success, the state-of-the… (voir plus)-art meth-003 ods fall short of considering the propagation 004 of news. News propagates at different times 005 through different mediums, including users, 006 comments, and sources, which form the news 007 propagation network. Moreover, the serious 008 problem of data hiding arises, which means 009 that fake news publishers disguise fake news 010 as real to confuse users by deleting comments 011 that refute the rumor or deleting the news itself 012 when it has been spread widely. Existing meth-013 ods do not consider the propagation of news 014 and fail to identify what matters in the process, 015 which leads to fake news hiding in the prop-016 agation network and escaping from detection. 017 Inspired by the propagation of news, we pro-018 pose a novel fake news detection framework 019 named TaHiD, which models the propagation 020 as a heterogeneous dynamic graph and contains 021 the propagation attention module to measure 022 the influence of different propagation. Exper-023 iments demonstrate that TaHiD extracts use-024 ful information from the news propagation net-025 work and outperforms state-of-the-art methods 026 on several benchmark datasets for fake news 027 detection. Additional studies also show that 028 TaHiD is capable of identifying fake news in 029 the case of data hiding. 030
2021-12-31
(publié)
www.semanticscholar.org
Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline
Massimo Caccia
Jonas Mueller
Taesup Kim
Laurent Charlin
Rasool Fakoor
We study task-agnostic continual reinforcement learning (TACRL) in which standard RL challenges are compounded with partial observability st… (voir plus)emming from task agnosticism, as well as additional difficulties of continual learning (CL), i.e., learning on a non-stationary sequence of tasks. Here we compare TACRL methods with their soft upper bounds prescribed by previous literature: multi-task learning (MTL) methods which do not have to deal with non-stationary data distributions, as well as task-aware methods, which are allowed to operate under full observability . We consider a previously unexplored and straightforward baseline for TACRL, replay-based recurrent RL (3RL), in which we augment an RL algorithm with recurrent mechanisms to address partial observability and experience replay mechanisms to address catastrophic forgetting in CL. Studying empirical performance in a sequence of RL tasks, we find surprising occurrences of 3RL matching and overcoming the MTL and task-aware soft upper bounds. We lay out hypotheses that could explain this inflection point of continual and task-agnostic learning research. Our hypotheses are empirically tested in continuous control tasks via a large-scale study of the popular multi-task and continual learning benchmark Meta-World. By analyzing different training statistics including gradient conflict, we find evidence that 3RL’s outperformance stems from its ability to quickly infer how new tasks relate with the previous ones, enabling forward transfer.
2021-12-31
arXiv.org (prépublication)
doi.org