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A latent text-to-image diffusion model
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
A game theoretic approach to explain the output of any machine learning model.
Instruct-tune LLaMA on consumer hardware
LAVIS - A One-stop Library for Language-Vision Intelligence
links to conference publications in graph-based deep learning
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
[ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models
Simple image captioning model
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
Graph Transformer Networks (Authors' PyTorch implementation for the NeurIPS 19 paper)
Official implementation of the paper “Inversion-Based Style Transfer with Diffusion Models” (CVPR 2023)
Diffusion models of protein structure; trigonometry and attention are all you need!
Precision Medicine Knowledge Graph (PrimeKG)
A python wrapper for the Visual Genome API
The LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and Python. The Toolbox realizes LRP functionality for the Caffe D…
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
Repository for Deep Structural Causal Models for Tractable Counterfactual Inference
Causal Graphical Models in Python
ToolQA, a new dataset to evaluate the capabilities of LLMs in answering challenging questions with external tools. It offers two levels (easy/hard) across eight real-life scenarios.
Find and fix bugs in natural language machine learning models using adaptive testing.
code for paper "Graph Structure of Neural Networks"
Pytorch Implementation of recent visual attribution methods for model interpretability
ClariQ: SCAI Workshop data challenge on conversational search clarification.
Parameterized Explainer for Graph Neural Network
Tools for training explainable models using attribution priors.
Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" …
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)
understanding model mistakes with human annotations