[NeurIPS2023,ICML2024] Multiparameter Persistence for Machine Learning

Official implementation of "Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships", NeurIPS 2023.

Analysis of effectiveness of transformers in TSF and implementation of FreTS from NeurIPS-2023

This repository is the code of our paper "DiffUTE: Universal Text Editing Diffusion Model" (NeurIPS'2023).

multispy is a lsp client library in Python intended to be used to build applications around language servers.

[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

Official implementation of the NeurIPS 2023 paper titled "Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples"

[NeurIPS2023] PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas(or 360-degree image)

Official implementation for "Tailoring Self-Attention for Graph via Rooted Subtrees" (NeurIPS2023)

[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, TinyLlama, etc.

Official PyTorch Implementation for Advancing Bayesian Optimization via Learning Correlated Latent Space (CoBO)

Advances on machine learning of graphs, covering the reading list of recent top academic conferences.

[NeurIPS 2023 : GLFRONTIERS Workshop] GAD-EBM : Graph Anomaly Detection using Energy-Based Models

This repository contains the implementation for our work "Topology-Aware Uncertainty for Image Segmentation", accepted to NeurIPS 2023.

An end-to-end multimodal framework incorporating explicit knowledge graphs and OOD-detection. (NeurIPS23)

Code and Data artifact for NeurIPS 2023 paper - "Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context". `multispy` is a lsp client library in Python intended to be used to build applications around language servers.

Code for paper "Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement​", Neurips 2023

A Python 3 package for identifying distribution shifts (a.k.a feature-shifts) between datasets. Official implementation of the paper: "iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models".

[Neurips 2023] Official pytorch implementation of "Addressing Negative Transfer in Diffusion Models"

Official Repository of NeurIPS 2023 - MedFM Challenge