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Physical Intuition

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Showing 1–35 of 35 papers

TitleStatusHype
Training Compute-Optimal Large Language ModelsCode6
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
Physics-Inspired Distributed Radio Map EstimationCode1
InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductorsCode1
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledgeCode1
Constructing Custom Thermodynamics Using Deep LearningCode1
Generalizing Adam to Manifolds for Efficiently Training TransformersCode1
Leveraging 2D Data to Learn Textured 3D Mesh GenerationCode1
Can Theoretical Physics Research Benefit from Language Agents?—0
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly DetectionCode0
Large Language Models and Mathematical Reasoning Failures—0
Towards understanding how attention mechanism works in deep learning—0
A Note on Spectral Map—0
Automated design of nonreciprocal thermal emitters via Bayesian optimization—0
Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model—0
Physics-tailored machine learning reveals unexpected physics in dusty plasmasCode0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses—0
Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression—0
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial—0
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery—0
Why is AI hard and Physics simple?—0
Automated Optical Multi-layer Design via Deep Reinforcement LearningCode0
Advances in Bayesian Probabilistic Modeling for Industrial Applications—0
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational AutoencodersCode0
Portfolio Cuts: A Graph-Theoretic Framework to Diversification—0
Guiding Physical Intuition with Neural Stethoscopes—0
Biophysics at the coffee shop: lessons learned working with George Oster—0
Meta-Learning for Stochastic Gradient MCMCCode0
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object StackingCode0
What is the Machine Learning?—0
Combining Machine Learning and Physics to Understand Glassy Systems—0
Generalizable Features From Unsupervised Learning—0
Learning Physical Intuition of Block Towers by ExampleCode0
A Complete Recipe for Stochastic Gradient MCMC—0
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