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Oct 16, 2020
Computation and Language
Cryptography and Security
Machine Learning

Mischief: A Simple Black-Box Attack Against Transformer Architectures

We introduce Mischief, a simple and lightweight method to produce a class of human-readable, realistic adversarial examples for language models. We p…
Oct 16, 2020
Computation and Language

Detecting Objectifying Language in Online Professor Reviews

Student reviews often make reference to professors' physical appearances. Until recently RateMyProfessors.com, the website of this study's focus, use…
Oct 16, 2020
Computer Vision

What Can You Learn from Your Muscles? Learning Visual Representation …

Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most re…
Oct 16, 2020
Sound
Artificial Intelligence
Computer Vision

Latent Vector Recovery of Audio GANs

Advanced Generative Adversarial Networks (GANs) are remarkable in generating intelligible audio from a random latent vector. In this paper, we examin…
Oct 16, 2020
Machine Learning
Artificial Intelligence

Towards Accurate Knowledge Transfer via Target-awareness Representati…

Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity o…
Oct 16, 2020
Machine Learning
Artificial Intelligence
Multiagent Systems

Multi-Agent Collaboration via Reward Attribution Decomposition

Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2. Unfortunately, t…
Oct 16, 2020
Machine Learning

Feature Selection for Huge Data via Minipatch Learning

Feature selection often leads to increased model interpretability, faster computation, and improved model performance by discarding irrelevant or red…
Oct 16, 2020
Tissues and Organs
Machine Learning
Biological Physics
Quantitative Methods
Applications

Deep Learning Head Model for Real-time Estimation of Entire Brain D…

Objective: Many recent studies have suggested that brain deformation resulting from a head impact is linked to the corresponding clinical outcome, su…
Oct 16, 2020
Artificial Intelligence
Computation and Language

Analogous Process Structure Induction for Sub-event Sequence Predicti…

Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured…
Oct 16, 2020
Computation and Language
Machine Learning
Sound
Audio and Speech Processing

Adaptive Feature Selection for End-to-End Speech Translation

Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to f…
Oct 16, 2020
Machine Learning
Quantitative Methods

We present a novel deep neural network to model and predict PPIs from variable-length protein sequences. Our proposed framework adopts a recurrent neural network to capture contextualized and sequential information from amino acid sequences. By inco…

Interpretable Structured Learning with Sparse Gated Sequence Encoder …

Predicting protein-protein interactions (PPIs) by learning informative representations from amino acid sequences is a challenging yet important probl…
Oct 16, 2020
Machine Learning
Artificial Intelligence
Data Structures and Algorithms

Learnable Graph-regularization for Matrix Decomposition

Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields including computer vision,…
Oct 16, 2020
Machine Learning
Neural and Evolutionary Computation

For self-supervised learning, Rationality implies generalization, p…

We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation $r$ o…
Oct 16, 2020
Robotics

Direct Policy Optimization using Deterministic Sampling and Collocati…

We present an approach for approximately solving discrete-time stochastic optimal control problems by combining direct trajectory optimization, deter…
Oct 16, 2020
Machine Learning
Optimization and Control

Online non-convex optimization with imperfect feedback

We consider the problem of online learning with non-convex losses. In terms of feedback, we assume that the learner observes - or otherwise construct…
Oct 16, 2020
Machine Learning

The Ridgelet Prior: A Covariance Function Approach to Prior Specifi…

Bayesian neural networks attempt to combine the strong predictive performance of neural networks with formal quantification of uncertainty associated…
Oct 16, 2020
Machine Learning
Computers and Society

Machine Learning-Powered Mitigation Policy Optimization in Epidemio…

A crucial aspect of managing a public health crisis is to effectively balance prevention and mitigation strategies, while taking their socio-economic…
Oct 16, 2020
Human-Computer Interaction

Multi-Modal Data Collection for Measuring Health, Behavior, and Livin…

As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users' d…
Oct 16, 2020
Machine Learning

We present a novel deep graph convolutional network (HOGCN) for biomedical interaction prediction. Our proposed model adopts a higher-order graph convolutional layer to learn to mix the feature representation of neighbors at various scales. Experime…

Predicting Biomedical Interactions with Higher-Order Graph Convolutio…

Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of n…
Oct 16, 2020
Machine Learning
Data Structures and Algorithms

We presented a formal analysis of the OSE problem, as well as an FPTAS that works for a large class of neural network architectures. The time complexity for Algorithm 1 can be further improved by using a strongly convex loss (Rakhlin et al., 2012) o…

An Approximation Algorithm for Optimal Subarchitecture Extraction

We consider the problem of finding the set of architectural parameters for a chosen deep neural network which is optimal under three metrics: paramet…
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