Search Results for author:
Found 18 papers, 9 papers with code
On the efficient representation and execution of deep acoustic models
In this paper we present a simple and computationally efficient quantization scheme that enables us to reduce the resolution of the parameters of a neural network from 32-bit floating point values to 8-bit integer values.
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models
We develop streaming keyword spotting systems using a recurrent neural network transducer (RNN-T) model: an all-neural, end-to-end trained, sequence-to-sequence model which jointly learns acoustic and language model components.
Lightweight Adaptive Mixture of Neural and N-gram Language Models
It is often the case that the best performing language model is an ensemble of a neural language model with n-grams.
GenEval: A Benchmark Suite for Evaluating Generative Models
In this work, we aim at addressing this problem by introducing a new benchmark evaluation suite, dubbed \textit{GenEval}.
Real or Fake? Learning to Discriminate Machine from Human Generated Text
Energy-based models (EBMs), a. k. a.
PHYRE: A New Benchmark for Physical Reasoning
The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles.
Ranked #3 on
Visual Reasoning
on PHYRE-1B-Within
Language Models as Knowledge Bases?
Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks.
Residual Energy-Based Models for Text
Current large-scale auto-regressive language models display impressive fluency and can generate convincing text.
Residual Energy-Based Models for Text Generation
In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level.
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
This paper presents ReBeL, a general framework for self-play reinforcement learning and search that provably converges to a Nash equilibrium in any two-player zero-sum game.
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings.
Physical Reasoning Using Dynamics-Aware Models
A common approach to solving physical reasoning tasks is to train a value learner on example tasks.
Ranked #1 on
Visual Reasoning
on PHYRE-1B-Within
No-Press Diplomacy from Scratch
Additionally, we extend our methods to full-scale no-press Diplomacy and for the first time train an agent from scratch with no human data.
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior.
Self-Explaining Deviations for Coordination
Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world.
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while simultaneously training an agent that responds well to this human model.
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
Towards Measuring the Representation of Subjective Global Opinions in Language Models
We first build a dataset, GlobalOpinionQA, comprised of questions and answers from cross-national surveys designed to capture diverse opinions on global issues across different countries.
