Danish Foundation Models

no code implementations • 13 Nov 2023 • Kenneth Enevoldsen, Lasse Hansen, Dan S. Nielsen, Rasmus A. F. Egebæk, Søren V. Holm, Martin C. Nielsen, Martin Bernstorff, Rasmus Larsen, Peter B. Jørgensen, Malte Højmark-Bertelsen, Peter B. Vahlstrup, Per Møldrup-Dalum, Kristoffer Nielbo

Large language models, sometimes referred to as foundation models, have transformed multiple fields of research.

Programmatic Policy Extraction by Iterative Local Search

no code implementations • 18 Jan 2022 • Rasmus Larsen, Mikkel Nørgaard Schmidt

Combining imitation-projection and dataset aggregation with a local search heuristic, we present a simple and direct approach to extracting a programmatic policy from a pretrained neural policy.

reinforcement-learning Reinforcement Learning (RL)

Reinforcement Learning of Causal Variables Using Mediation Analysis

no code implementations • 29 Oct 2020 • Tue Herlau, Rasmus Larsen

To our knowledge, this is the first attempt to apply causal analysis in a reinforcement learning setting without strict restrictions on the number of states.

General Reinforcement Learning reinforcement-learning +1

Device Placement Optimization with Reinforcement Learning

1 code implementation • ICML 2017 • Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices.

Language Modelling Machine Translation +3