HaotianXiangsti

AERO-GNN

The official repository of "Towards Deep Attention in Graph Neural Networks: Problems and Remedies," published in ICML 2023.

ASTGCN-r-pytorch

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI 2019, pytorch version

ASTGNN

This is a Pytorch implementation of ASTGNN. Now the corresponding paper is available online at https://ieeexplore.ieee.org/document/9346058.

Bayesian-Invariant-Risk-Minmization

This is the code for the paper Bayesian Invariant Risk Minmization of CVPR 2022.

CatHeartbeat

conformal_classification

Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

Convex-Optimization-Final-Project

ddpm-ipa-protein-generation

Implementation of the DDPM + IPA (invariant point attention) for protein generation, as outlined in the paper "Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models"

diffusion_priors

Using pre-trained Diffusion models as priors for inference tasks

DUE

Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".

HaotianXiangsti/AERO-GNN

The official repository of "Towards Deep Attention in Graph Neural Networks: Problems and Remedies," published in ICML 2023.

HaotianXiangsti/ASTGCN-r-pytorch

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI 2019, pytorch version

HaotianXiangsti/ASTGNN

This is a Pytorch implementation of ASTGNN. Now the corresponding paper is available online at https://ieeexplore.ieee.org/document/9346058.

HaotianXiangsti/Bayesian-Invariant-Risk-Minmization

This is the code for the paper Bayesian Invariant Risk Minmization of CVPR 2022.

HaotianXiangsti/CatHeartbeat

HaotianXiangsti/conformal_classification

Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

HaotianXiangsti/Convex-Optimization-Final-Project

HaotianXiangsti/ddpm-ipa-protein-generation

Implementation of the DDPM + IPA (invariant point attention) for protein generation, as outlined in the paper "Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models"

HaotianXiangsti/diffusion_priors

Using pre-trained Diffusion models as priors for inference tasks

HaotianXiangsti/DUE

Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".

HaotianXiangsti/EECS_6998_E11

Repo for course project of EECS_6998_E11

HaotianXiangsti/eks

Ensembling and kalman smoothing for pose estimation

HaotianXiangsti/guided-diffusion

HaotianXiangsti/haotianxiang.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

HaotianXiangsti/lightning-pose

Accelerated pose estimation and tracking using semi-supervised convolutional networks.

HaotianXiangsti/multimodal_supercon

Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia

HaotianXiangsti/Personal_Website

HaotianXiangsti/SafeGraph_baseline

HaotianXiangsti/Secrete-Recipe

HaotianXiangsti/Segment-and-Track-Anything

An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) for key-frame segmentation and Associating Objects with Transformers (AOT) for efficient tracking and propagation purposes.

HaotianXiangsti/Track-Anything

Track-Anything is a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything, XMem, and E2FGVI.

HaotianXiangsti/uncertainty-baselines

High-quality implementations of standard and SOTA methods on a variety of tasks.

HaotianXiangsti/v-diffusion-jax

v objective diffusion inference code for JAX.

HaotianXiangsti/ZIN_official

This is the implementation for the NeurIPS 2022 paper: ZIN: When and How to Learn Invariance Without Environment Partition?