Physical Intuition
11 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Physical Intuition
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
BIG-bench
Most implemented papers
Learning Physical Intuition of Block Towers by Example
facebook/UETorch
•
•
Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
ogroth/shapestacks
•
•
ECCV 2018
Physical intuition is pivotal for intelligent agents to perform complex tasks.
Meta-Learning for Stochastic Gradient MCMC
WenboGong/MetaSGMCMC
•
•
ICLR 2019
Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has become increasingly popular for simulating posterior samples in large-scale Bayesian modeling.
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
yabozkurt/gmvae
•
•
While variational autoencoders ensure continuity of the embedding by assuming a unimodal Gaussian prior, this is at odds with the multi-basin free-energy landscapes that typically arise from the identification of meaningful collective variables.
Leveraging 2D Data to Learn Textured 3D Mesh Generation
pmh47/textured-mesh-gen
•
•
CVPR 2020
Thus, it learns to generate meshes that when rendered, produce images similar to those in its training set.
Automated Optical Multi-layer Design via Deep Reinforcement Learning
In this work, we frame the multi-layer optical design task as a sequence generation problem.
Generalizing Adam to Manifolds for Efficiently Training Transformers
In this work a new approach is presented that leverages the special structure of the manifolds which are relevant for optimization of neural networks, such as the Stiefel manifold, the symplectic Stiefel manifold, the Grassmann manifold and the symplectic Grassmann manifold: all of these are homogeneous spaces and as such admit a global tangent space representation.
Constructing Custom Thermodynamics Using Deep Learning
mlds-nus/deeplearningcustomthermodynamics
•
•
Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate.
