Automating the Search for Artificial Life with Foundation Models
With the recent Nobel Prize awarded for radical advances in protein discovery, foundation
models (FMs) for exploring large combinatorial spaces promise to revolutionize many …
models (FMs) for exploring large combinatorial spaces promise to revolutionize many …
Learning useful representations of recurrent neural network weight matrices
Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers. The
program of an RNN is its weight matrix. How to learn useful representations of RNN weights …
program of an RNN is its weight matrix. How to learn useful representations of RNN weights …
Reinforcement learning with general evaluators and generators of policies
F Faccio - 2024 - sonar.ch
Reinforcement Learning (RL) is a subfield of Artificial Intelligence that studies how machines
can make decisions by learning from their interactions with an environment. The key aspect …
can make decisions by learning from their interactions with an environment. The key aspect …
Endless minds most beautiful: building open-ended linguistic autotelic agents with deep reinforcement learning and language models
L Teodorescu - 2023 - hal.science
AI has made immense progress in the past 10 years, brought about by the increasing
availability of computation, data, and by the invention of flexible algorithmic paradigms to …
availability of computation, data, and by the invention of flexible algorithmic paradigms to …
ACES: Generating a Diversity of Challenging Programming Puzzles with Autotelic Generative Models
The ability to invent novel and interesting problems is a remarkable feature of human
intelligence that drives innovation, art, and science. We propose a method that aims to …
intelligence that drives innovation, art, and science. We propose a method that aims to …