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Keyword: Monte Carlo tree search ×Clear all

5 claims from 3 papers

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

   Bryant, Pozzati, Zhu, Shenoy, Kundrotas and Elofsson · Nature Communications · 2022

   The authors predict large protein complexes by assembling AlphaFold-predicted subcomponents with Monte Carlo tree search, and add a scoring function, mpDockQ, to judge whether assemblies are complete and how accurate.

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   1. UncheckedUsing Monte Carlo tree search on predicted subcomponents, the authors assembled 91 of 175 complexes of 10–30 chains, with a median TM-score of 0.51.“We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51.”
   2. UncheckedThe authors built a scoring function, mpDockQ, which they say can tell whether assembled protein complexes are complete and estimate how accurate they are.“We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy.”
   3. UncheckedIn this study, large protein complexes with symmetry were assembled accurately from predicted parts, while asymmetrical complexes remained difficult to assemble.“We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging.”

2. Computer Science › Advanced Neural Network Applications

   Channel Pruning via Lookahead Search Guided Reinforcement Learning

   Wang and Li · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · 2022

   The paper presents a channel pruning method that uses reinforcement learning guided by Monte Carlo tree search to choose which filters to remove, tested on three image datasets.

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   1. UncheckedTests on MNIST, CIFAR-10 and ILSVRC-2012 are reported to show the new channel pruning method works better than existing traditional and automated methods.“Experiments on MNIST, CIFAR-10, and ILSVRC-2012 validate the effectiveness of our approach compared to both traditional and automated existing channel pruning approaches.”

3. Computer Science › AI-based Problem Solving and Planning

   Reasoning with Language Model is Planning with World Model

   Hao, Gu, Ma et al. · arXiv (Cornell University) · 2023

   The paper proposes RAP, which has a language model act as both world model and reasoning agent with tree search, and reports it beating strong prompting baselines on planning, maths and logic tasks.

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   1. UncheckedIn a plan generation setting, the RAP method on LLAMA-33B is reported to beat chain-of-thought prompting on GPT-4, by 33% relative.“RAP on LLAMA-33B surpasses CoT on GPT-4 with 33% relative improvement in a plan generation setting.”