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Found 6 papers, 2 papers with code
The unknotting number, hard unknot diagrams, and reinforcement learning
We have developed a reinforcement learning agent that often finds a minimal sequence of unknotting crossing changes for a knot diagram with up to 200 crossings, hence giving an upper bound on the unknotting number.
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of nodes) that maximize the number of edges without having 3- or 4-cycles.
Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network
Quantum error-correction is a prerequisite for reliable quantum computation.
A Deep Learning Approach for Characterizing Major Galaxy Mergers
Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation.
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts.
Massively Parallel Methods for Deep Reinforcement Learning
We present the first massively distributed architecture for deep reinforcement learning.
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