Papers with Code 아카이브
Physical Intuition
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection
Large Language Models and Mathematical Reasoning Failures
Physics-Inspired Distributed Radio Map Estimation
Towards understanding how attention mechanism works in deep learning
A Note on Spectral Map
Automated design of nonreciprocal thermal emitters via Bayesian optimization
InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors
Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Physics-tailored machine learning reveals unexpected physics in dusty plasmas
Constructing Custom Thermodynamics Using Deep Learning
Generalizing Adam to Manifolds for Efficiently Training Transformers
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning
Training Compute-Optimal Large Language Models
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery
Why is AI hard and Physics simple?
Automated Optical Multi-layer Design via Deep Reinforcement Learning
Leveraging 2D Data to Learn Textured 3D Mesh Generation
Advances in Bayesian Probabilistic Modeling for Industrial Applications
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
Portfolio Cuts: A Graph-Theoretic Framework to Diversification
Biophysics at the coffee shop: lessons learned working with George Oster
Meta-Learning for Stochastic Gradient MCMC
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
What is the Machine Learning?
Combining Machine Learning and Physics to Understand Glassy Systems
Generalizable Features From Unsupervised Learning
Learning Physical Intuition of Block Towers by Example
A Complete Recipe for Stochastic Gradient MCMC
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.