Deep Learning
Neural Ordinary Differential Equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David K. Duvenaud
Neural Information Processing Systems http://nips.cc/
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.
Learning to Solve SMT Formulas
Mislav Balunovic, Pavol Bielik, Martin Vechev
We present a new approach for learning to solve SMT formulas. We phrase the challenge of solving SMT formulas as a tree search problem where at each step a transformation is applied to the input formula until the formula is solved.
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
Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Josh Tenenbaum
The model combines techniques from deep learning and program synthesis.
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.
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias
Neural Information Processing Systems http://nips.cc/
Improving Explorability in Variational Inference with Annealed Variational Objectives Chin-Wei Huang,?, 1 Shawn Tan
In our experiments, we demonstrate our method's
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.
Complex Gated Recurrent Neural Networks
Recurrent neural networks (RNNs) are widely used for processing time series and sequential information.