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Found 13 papers, 6 papers with code
Neural Networks and the Chomsky Hierarchy
Reliable generalization lies at the heart of safe ML and AI.
Randomized Positional Encodings Boost Length Generalization of Transformers
Transformers have impressive generalization capabilities on tasks with a fixed context length.
Learning Universal Predictors
Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data.
Language Modeling Is Compression
We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning.
Memory-Based Meta-Learning on Non-Stationary Distributions
Memory-based meta-learning is a technique for approximating Bayes-optimal predictors.
Distributional Bellman Operators over Mean Embeddings
We propose a novel algorithmic framework for distributional reinforcement learning, based on learning finite-dimensional mean embeddings of return distributions.
Meta-trained agents implement Bayes-optimal agents
Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution.
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains.
Model-Free Risk-Sensitive Reinforcement Learning
Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.
Stochastic Approximation of Gaussian Free Energy for Risk-Sensitive Reinforcement Learning
Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.
Your Policy Regularizer is Secretly an Adversary
Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy.
Beyond Bayes-optimality: meta-learning what you know you don't know
This is in contrast to risk-sensitive agents, which additionally exploit the higher-order moments of the return, and ambiguity-sensitive agents, which act differently when recognizing situations in which they lack knowledge.
Grandmaster-Level Chess Without Search
Unlike traditional chess engines that rely on complex heuristics, explicit search, or a combination of both, we train a 270M parameter transformer model with supervised learning on a dataset of 10 million chess games.
