Research Area
Year Published

669 Results

April 15, 2019

Active Exploration in Markov Decision Processes

International Conference on Artificial Intelligence and Statistics (AISTATS)

We introduce the active exploration problem in Markov decision processes (MDPs). Each state of the MDP is characterized by a random value and the learner should gather samples to estimate the mean value of each state as accurately as possible.

By: Jean Tarbouriech, Alessandro Lazaric

March 27, 2019

Evaluation of real-time sound propagation engines in a virtual reality framework

AES International Conference on Immersive and Interactive Audio

Sound propagation in an enclosed space is a combination of several wave phenomena, such as direct sound, specular reflections, scattering, diffraction, or air absorption, among others. Achieving realistic and immersive audio in games and virtual reality (VR) requires real-time modeling of these phenomena.

By: Sebastià V. Amengual Garí, Carl Schissler, Ravish Mehra, Shawn Featherly, Philip W. Robinson
Areas: AR/VR

March 14, 2019

On the Pitfalls of Measuring Emergent Communication


In this paper, we examine a few intuitive existing metrics for measuring communication, and show that they can be misleading. Specifically, by training deep reinforcement learning agents to play simple matrix games augmented with a communication channel, we find a scenario where agents appear to communicate (their messages provide information about their subsequent action), and yet the messages do not impact the environment or other agent in any way.

By: Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, Yann Dauphin

March 12, 2019

Convolutional neural networks for mesh-based parcellation of the cerebral cortex

Medical Imaging with Deep Learning (MIDL)

We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-of-the-art and baselines, highlighting the potential and applicability of these methods to tackle neuroimaging challenges, paving the road towards a better characterization of brain diseases.

By: Guillem Cucurull, Konrad Wagstyl, Arantxa Casanova, Petar Velickovic, Estrid Jakobsen, Michal Drozdzal, Adriana Romero, Alan Evans, Yoshua Bengio

February 20, 2019

BOLT: A Practical Binary Optimizer for Data Centers and Beyond

International Symposium on Code Generation and Optimization (CGO)

In this paper, we present BOLT, a post-link optimizer built on top of the LLVM framework. Utilizing sample-based profiling, BOLT boosts the performance of real-world applications even for highly optimized binaries built with both feedback-driven optimizations (FDO) and link-time optimizations (LTO).

By: Maksim Panchenko, Rafael Auler, Bill Nell, Guilherme Ottoni

February 16, 2019

Machine Learning at Facebook: Understanding Inference at the Edge

IEEE International Symposium on High-Performance Computer Architecture (HPCA)

This paper takes a data-driven approach to present the opportunities and design challenges faced by Facebook in order to enable machine learning inference locally on smartphones and other edge platforms.

By: Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang

February 13, 2019

SapFix: Automated End-to-End Repair at Scale

International Conference on Software Engineering (ICSE)

We report our experience with SAPFIX: the first deployment of automated end-to-end fault fixing, from test case design through to deployed repairs in production code.

By: Alexandru Marginean, Johannes Bader, Satish Chandra, Mark Harman, Yue Jia, Ke Mao, Alexander Mols, Andrew Scott

February 1, 2019

Separation Logic

Communications of the ACM (CACM)

In joint work with John Reynolds and others we developed Separation Logic as a formalism for reasoning about programs that mutate data structures. From a special logic for heaps it gradually evolved into a general theory for modular reasoning about concurrent as well as sequential programs.

By: Peter O'Hearn

January 30, 2019

Large-Scale Visual Relationship Understanding

AAAI Conference on Artificial Intelligence (AAAI)

Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved.

By: Ji Zhang, Yannis Kalantidis, Marcus Rohrbach, Manohar Paluri, Ahmed Elgammal, Mohamed Elhoseiny

January 30, 2019

Memorize or generalize? Searching for a compositional RNN in a haystack

IJCAI-ECAI Workshop: Architectures and Evaluation for Generality, Autonomy & Progress in AI

Neural networks are very powerful learning systems, but they do not readily generalize from one task to the other. This is partly due to the fact that they do not learn in a compositional way, that is, by discovering skills that are shared by different tasks, and recombining them to solve new problems. In this paper, we explore the compositional generalization capabilities of recurrent neural networks (RNNs).

By: Adam Liška, Germán Kruszewski, Marco Baroni