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Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher Reale, Rebecca Russell, Louis Kim, peter chin
Security vulnerabilities in software programs pose serious risks to computer systems.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Ye Jia, Yu Zhang, Ron Weiss, Quan Wang, Jonathan Shen, Fei Ren, zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu
Neural Information Processing Systems http://nips.cc/
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee
Modern Visual Question Answering (VQA) models have been shown to rely heavily on superficial correlations between question and answer words learned during training - e.g .
Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
Crucially, the neural model uses miniKanren's
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodrรญguez Lรณpez, Alexandre Lacoste
In the current work we aim to close this gap.
Representation Learning of Compositional Data
Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
We consider the problem of learning a low dimensional representation for compositional data.
US economy adds 119,000 jobs in September as unemployment rate rises
Does'America First' make the US weaker? Who is Marjorie Taylor Greene? United States job growth accelerated in September despite a cooling job market as the unemployment rate rose. Nonfarm payrolls grew by 119,000 jobs after a downwardly revised 4,000 drop in August, according to the Bureau of Labor Statistics (BLS) report released on Thursday. The healthcare sector had the most gains, totalling 43,000 jobs in September.
Balanced Policy Evaluation and Learning
Neural Information Processing Systems http://nips.cc/
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
In this paper, we consider online F-measure optimization (OFO). Unlike traditional performance metrics (e.g., classification error rate), F-measure is non-decomposable over training examples and is a non-convex function of model parameters, making it much more difficult to be optimized in an online fashion. Most existing results of OFO usually suffer from high memory/computational costs and/or lack statistical consistency guarantee for optimizing F-measure at the population level.
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi
While recent developments in autonomous vehicle (A V) technology highlight substantial progress, we lack tools for rigorous and scalable testing.