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Found 7 papers, 3 papers with code
Evaluating Agents using Social Choice Theory
We argue that many general evaluation problems can be viewed through the lens of voting theory.
Learning Python Code Suggestion with a Sparse Pointer Network
By augmenting a neural language model with a pointer network specialized in referring to predefined classes of identifiers, we obtain a much lower perplexity and a 5 percentage points increase in accuracy for code suggestion compared to an LSTM baseline.
Gated Linear Networks
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs).
Online Learning with Gated Linear Networks
This paper describes a family of probabilistic architectures designed for online learning under the logarithmic loss.
Learning Robust Real-Time Cultural Transmission without Human Data
We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents.
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
Human-Timescale Adaptation in an Open-Ended Task Space
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL).
