tailintalent
Assistant Professor @ Westlake University. Research interests: AI + Science. Previously Stanford CS, MIT, Peking University
Westlake University
tailintalent's Stars
pengsida/learning_research
本人的科研经验
MarkFzp/act-plus-plus
Imitation learning algorithms with Co-training for Mobile ALOHA: ACT, Diffusion Policy, VINN
buaacyw/MeshAnything
From anything to mesh like human artists. Official impl. of "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers"
DiffEqML/torchdyn
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
facebookresearch/diplomacy_cicero
Code for Cicero, an AI agent that plays the game of Diplomacy with open-domain natural language negotiation.
atong01/conditional-flow-matching
TorchCFM: a Conditional Flow Matching library
test-time-training/ttt-lm-pytorch
Official PyTorch implementation of Learning to (Learn at Test Time): RNNs with Expressive Hidden States
pdebench/PDEBench
PDEBench: An Extensive Benchmark for Scientific Machine Learning
jeanfeydy/geomloss
Geometric loss functions between point clouds, images and volumes
lululxvi/deeponet
Learning nonlinear operators via DeepONet
zdhNarsil/Awesome-GFlowNets
A curated list of resources about generative flow networks (GFlowNets).
weymouth/lily-pad
Real-time two-dimensional fluid dynamics simulations in Processing. Initiated by Dr G D Weymouth:
mods333/energy-based-scene-graph
Code release for Energy-Based Learning for Scene Graph Genertaion
samacqua/LARC
Language-annotated Abstraction and Reasoning Corpus
Dragon-Zhuang/BPPO
Author's Pytorch implementation of ICLR2023 paper Behavior Proximal Policy Optimization (BPPO).
computational-imaging/GraphPDE
yilundu/ebm_compositionality
[NeurIPS'20] Code for the Paper Compositional Visual Generation and Inference with Energy Based Models
snap-stanford/lamp
[ICLR23] First deep learning-based surrogate model that jointly learns the evolution model and optimizes computational cost via remeshing
snap-stanford/zeroc
ZeroC is a neuro-symbolic method that trained with elementary visual concepts and relations, can zero-shot recognize and acquire more complex, hierarchical concepts, even across domains
shib0li/DNN-MFBO
Multi-fidelity Bayesian Optimization via Deep Neural Nets
AI4Science-WestlakeU/cindm
[ICLR24] CinDM uses compositional generative models to design boundaries and initial states significantly more complex than the ones seen in training for physical simulation
snap-stanford/le_pde
LE-PDE accelerates PDEs' forward simulation and inverse optimization via latent global evolution, achieving significant speedup with SOTA accuracy
AI4Science-WestlakeU/beno
[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values
AI4Science-WestlakeU/le-pde-uq
[AAAI24] LE-PDE-UQ endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems.
AI4Science-WestlakeU/frontiers_in_AI_course
AI4Science-WestlakeU/research_toolbox
A useful toolbox for research.
snap-stanford/ViRel
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
tailintalent/pytorch_net
Efficient construction and manipulation of nets with PyTorch
tailintalent/concept_library
Library for hierarchical concept composition and reasoning
AI4Science-WestlakeU/standard_repo