Search Results for author:
Found 7 papers, 3 papers with code
Know your audience: specializing grounded language models with listener subtraction
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.
Perceiver IO: A General Architecture for Structured Inputs & Outputs
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)
Neural spatio-temporal reasoning with object-centric self-supervised learning
Transformer-based language models have proved capable of rudimentary symbolic reasoning, underlining the effectiveness of applying self-attention computations to sets of discrete entities.
Attention over learned object embeddings enables complex visual reasoning
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
Adapting Behaviour for Learning Progress
Determining what experience to generate to best facilitate learning (i. e. exploration) is one of the distinguishing features and open challenges in reinforcement learning.
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Deep Lattice Networks and Partial Monotonic Functions
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.
