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Found 6 papers, 2 papers with code
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Our method iterates between 1) program sampling and hindsight relabeling, and 2) learning from prioritized experience replay.
Robust Scheduling with GFlowNets
Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization.
Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem.
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics.
Low-frequency vibrational spectrum of mean-field disordered systems
We study a recently introduced and exactly solvable mean-field model for the density of vibrational states $\mathcal{D}(\omega)$ of a structurally disordered system.
Disordered Systems and Neural Networks Soft Condensed Matter Statistical Mechanics
