A Novel Neural Network-Based Malware Severity Classification System
Miles Q. Li
Benjamin Fung
2021-01-01
International Conference on Software and Data Technologies (published)
doi.org
On-the-Fly Attention Modularization for Neural Generation
Yue Dong
Chandra Bhagavatula
Ximing Lu
Jena D. Hwang
Antoine Bosselut
Jackie Cheung
Yejin Choi
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from de generation: generated… (see more) text is repetitive, generic, self-inconsistent, and lacking commonsense. The empirical analyses on sentence-level attention patterns reveal that neural text degeneration may be associated with insufficient learning of inductive biases by the attention mechanism. Our findings motivate on-the-fly attention modularization, a simple but effective method for injecting inductive biases into attention computation during inference. The resulting text produced by the language model with attention modularization can yield enhanced diversity and commonsense reasoning while maintaining fluency and coherence.
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de
Optimization of Artificial Neural Network Hyperparameters For Processing Retrospective Information
A. Rogachev
F. Scholle
Yann LeCun
Yoshua Bengio
I. L. Kashirin
M. Demchenko
. Justification of the selection of the architecture and hyperparameters of artificial neural networks (ANN), focused on solving various cla… (see more)sses of applied problems, is a scientific and methodological problem. Optimizing the selection of ANN hyperparameters allows you to improve the quality and speed of ANN training. Various methods of optimizing the selection of ANN hyper-parameters are known – the use of evolutionary calculations, genetic algorithms, etc., but they require the use of additional software. To optimize the process of selecting ANN hyperparameters, Google Research has developed the KerasTuner software tool. It is a platform for automated search of a set of optimal combinations of hyperparameters. In Kerastuner, you can use various methods - random search, Bayesian optimization, or Hyperband. In the numerical experiments conducted by the author, 14 hyperparameters were varied, including the number of blocks of convolutional layers and the filters forming them, the type of activation function, the parameters of the "dropout" layers, and others. The studied tools demonstrated high efficiency while simultaneously varying more than a dozen optimized parameters of the convolutional network. The calculation time on the Colaboratory platform for the various combined ANN architectures studied, including recurrent RNN networks, was several hours, even with the use of GPU graphics accelerators. For ANN, focused on the processing and recognition of retrospective information, an increase in the quality of recognition was achieved to 80 ... 95%.
Overview of the TREC 2021 Fair Ranking Track
Asia J. Biega
Fernando Diaz
Michael D. Ekstrand
Sebastian Kohlmeier
The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide … (see more)a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents' topical content or authors. The 2021 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2021 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.
2021-01-01
TREC (published)
doi.org
arxiv.org
Personalized Medicine for OSA Syndrome in a Nutshell: Conceptual Clarification for Integration.
Christophe Gauld
Marie Darrason
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2021-01-01
Chest (published)
doi.org
Post-Editing Extractive Summaries by Definiteness Prediction
Jad Kabbara
Jackie Cheung
Extractive summarization has been the main-stay of automatic summarization for decades. Despite all the progress, extractive summarizers sti… (see more)ll suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document. This affects the coherence and readability of extractive summaries. In this work, we propose a lightweight postediting step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases. We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries. Moreover, based on an automatic evaluation study, we provide evidence for our system’s ability to generate linguistic decisions that lead to improved extractive summaries. We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.
2021-01-01
Conference on Empirical Methods in Natural Language Processing (published)
doi.org
Predicting Unreliable Predictions by Shattering a Neural Network
Xu Ji
Razvan Pascanu
(Rex) Devon Hjelm
Andrea Vedaldi
Balaji Lakshminarayanan
Yoshua Bengio
Piecewise linear neural networks can be split into subfunctions, each with its own activation pattern, domain, and empirical error. Empirica… (see more)l error for the full network can be written as an expectation over empirical error of subfunctions. Constructing a generalization bound on subfunction empirical error indicates that the more densely a subfunction is surrounded by training samples in representation space, the more reliable its predictions are. Further, it suggests that models with fewer activation regions generalize better, and models that abstract knowledge to a greater degree generalize better, all else equal. We propose not only a theoretical framework to reason about subfunction error bounds but also a pragmatic way of approximately evaluating it, which we apply to predicting which samples the network will not successfully generalize to. We test our method on detection of misclassification and out-of-distribution samples, finding that it performs competitively in both cases. In short, some network activation patterns are associated with higher reliability than others, and these can be identified using subfunction error bounds.
2021-01-01
arXiv.org (preprint)
openreview.net
openreview.net
Preferential Temporal Difference Learning
Nishanth Anand
Doina Precup
2021-01-01
ICML (published)
proceedings.mlr.press
arxiv.org
Pretraining Representations for Data-Efficient Reinforcement Learning
Max Schwarzer
Nitarshan Rajkumar
Michael Noukhovitch
Ankesh Anand
Laurent Charlin
(Rex) Devon Hjelm
Philip Bachman
Aaron Courville
Data efficiency is a key challenge for deep reinforcement learning. We address this problem by using unlabeled data to pretrain an encoder w… (see more)hich is then finetuned on a small amount of task-specific data. To encourage learning representations which capture diverse aspects of the underlying MDP, we employ a combination of latent dynamics modelling and unsupervised goal-conditioned RL. When limited to 100k steps of interaction on Atari games (equivalent to two hours of human experience), our approach significantly surpasses prior work combining offline representation pretraining with task-specific finetuning, and compares favourably with other pretraining methods that require orders of magnitude more data. Our approach shows particular promise when combined with larger models as well as more diverse, task-aligned observational data -- approaching human-level performance and data-efficiency on Atari in our best setting.
openreview.net
RAFFIC V IS : Fighting Human Trafficking through Visualization
Catalina Vajiac
Andreas Olligschlaeger
Yifei Li
Pratheeksha Nair
Meng-Chieh Lee
Namyong Park
Reihaneh Rabbany
Duen Horng Chau
Christos Faloutsos
Law enforcement can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected ads. Given such cl… (see more)usters, how can we interactively visualize potential evidence for law enforcement and domain experts? We present TRAFFICVIS, which, to our knowledge, is the first interface for cluster-level HT detection and labeling. It builds on state-of-the-art HT clustering algorithms by incorporating metadata as a signal of organized and potentially suspicious activity. Also, domain experts can label clusters as HT, spam, and more, efficiently creating labeled datasets to enable further HT research. TRAFFICVIS has been built in close collaboration with domain experts, who estimate that TRAFFICVIS provides a median 36x speedup over manual labeling.
Randomized Exploration in Reinforcement Learning with General Value Function Approximation
Haque Ishfaq
Qiwen Cui
Viet Bang Nguyen
Alex Ayoub
Zhuoran Yang
Zhaoran Wang
Doina Precup
Lin Yang
2021-01-01
International Conference on Machine Learning (published)
proceedings.mlr.press
Randomized Least Squares Policy Optimization
Haque Ishfaq
Zhuoran Yang
Andrei-Stefan Lupu
Viet Bang Nguyen
Lewis Liu
Riashat Islam
Zhaoran Wang
Doina Precup
Policy Optimization (PO) methods with function approximation are one of the most popular classes of Reinforcement Learning (RL) algorithms. … (see more)However, designing provably efficient policy optimization algorithms remains a challenge. Recent work in this area has focused on incorporating upper confidence bound (UCB)-style bonuses to drive exploration in policy optimization. In this paper, we present Randomized Least Squares Policy Optimization (RLSPO) which is inspired by Thompson Sampling. We prove that, in an episodic linear kernel MDP setting, RLSPO achieves (cid:101) O ( d 3 / 2 H 3 / 2 √ T ) worst-case (frequentist) regret, where H is the number of episodes, T is the total number of steps and d is the feature dimension. Finally, we evaluate RLSPO empirically and show that it is competitive with existing provably efficient PO algorithms.