no code implementations • 16 Jun 2022 • Aaditya K. Singh, David Ding, Andrew Saxe, Felix Hill, Andrew K. Lampinen
Through controlled experiments, we show that training a speaker with two listeners that perceive differently, using our method, allows the speaker to adapt to the idiosyncracies of the listeners.
8 code implementations • ICLR 2022 • Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, Joāo Carreira
A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible.
Ranked #1 on Optical Flow Estimation on KITTI 2015 (Average End-Point Error metric)
no code implementations • 1 Jan 2021 • David Ding, Felix Hill, Adam Santoro, Matthew Botvinick
Transformer-based language models have proved capable of rudimentary symbolic reasoning, underlining the effectiveness of applying self-attention computations to sets of discrete entities.
1 code implementation • NeurIPS 2021 • David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt Botvinick
Neural networks have achieved success in a wide array of perceptual tasks but often fail at tasks involving both perception and higher-level reasoning.
Ranked #4 on Video Object Tracking on CATER
no code implementations • 14 Dec 2019 • Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero
Determining what experience to generate to best facilitate learning (i. e. exploration) is one of the distinguishing features and open challenges in reinforcement learning.
15 code implementations • 26 Aug 2019 • Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinicius Zambaldi, Satyaki Upadhyay, Julien Pérolat, Sriram Srinivasan, Finbarr Timbers, Karl Tuyls, Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Paul Muller, Timo Ewalds, Ryan Faulkner, János Kramár, Bart De Vylder, Brennan Saeta, James Bradbury, David Ding, Sebastian Borgeaud, Matthew Lai, Julian Schrittwieser, Thomas Anthony, Edward Hughes, Ivo Danihelka, Jonah Ryan-Davis
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
no code implementations • NeurIPS 2017 • Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta
We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network.