367 Repositories
Python abstract-meaning-representation Libraries
Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"
The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami · Rayhane Mama · Ragavan Thurairatn
[CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation
RCIL [CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation Chang-Bin Zhang1, Jia-Wen Xiao1, Xialei Liu1, Ying-Cong Chen2
(CVPR 2022 Oral) Official implementation for "Surface Representation for Point Clouds"
RepSurf - Surface Representation for Point Clouds [CVPR 2022 Oral] By Haoxi Ran* , Jun Liu, Chengjie Wang ( * : corresponding contact) The pytorch off
[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning
Crafting Better Contrastive Views for Siamese Representation Learning (CVPR 2022 Oral) 2022-03-29: The paper was selected as a CVPR 2022 Oral paper! 2
code for TCL: Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022
Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022 News (03/16/2022) upload retrieval checkpoints finetuned on COCO and Flickr T
Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.
6D Rotation Representation for Unconstrained Head Pose Estimation (Pytorch) Paper Thorsten Hempel and Ahmed A. Abdelrahman and Ayoub Al-Hamadi, "6D Ro
PyGCL: A PyTorch Library for Graph Contrastive Learning
PyGCL is a PyTorch-based open-source Graph Contrastive Learning (GCL) library, which features modularized GCL components from published papers, standa
The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".
LEAR The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction". See below for an overview of
CompleX Group Interactions (XGI) provides an ecosystem for the analysis and representation of complex systems with group interactions.
XGI CompleX Group Interactions (XGI) is a Python package for the representation, manipulation, and study of the structure, dynamics, and functions of
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
LightHuBERT LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT | Github | Huggingface | SUPER
[ICLR 2022] Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
AMOS This repository contains the scripts for fine-tuning AMOS pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: Pretraining Text Encoders wi
[CVPR 2022 Oral] Rethinking Minimal Sufficient Representation in Contrastive Learning
Rethinking Minimal Sufficient Representation in Contrastive Learning PyTorch implementation of Rethinking Minimal Sufficient Representation in Contras
Code for the Findings of NAACL 2022(Long Paper): AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks
AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks arXiv link: upcoming To be published in Findings of NA
[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".
CORE This is the official PyTorch implementation for the paper: Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao. CORE: Simple and Effective Sess
[arXiv22] Disentangled Representation Learning for Text-Video Retrieval
Disentangled Representation Learning for Text-Video Retrieval This is a PyTorch implementation of the paper Disentangled Representation Learning for T
Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
BiDR Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval. Requirements torch==
Tensorflow 1.13.X implementation for our NN paper: Wei Xia, Sen Wang, Ming Yang, Quanxue Gao, Jungong Han, Xinbo Gao: Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation. Neural Networks 145: 1-9 (2022)
Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation Simple implementation of our paper MVGC. The d
Digan - Official PyTorch implementation of Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
DIGAN (ICLR 2022) Official PyTorch implementation of "Generating Videos with Dyn
PyTorch implementation of the Crafting Better Contrastive Views for Siamese Representation Learning
Crafting Better Contrastive Views for Siamese Representation Learning This is the official PyTorch implementation of the ContrastiveCrop paper: @artic
PyTorch-Geometric Implementation of MarkovGNN: Graph Neural Networks on Markov Diffusion
MarkovGNN This is the official PyTorch-Geometric implementation of MarkovGNN paper under the title "MarkovGNN: Graph Neural Networks on Markov Diffusi
CATE: Computation-aware Neural Architecture Encoding with Transformers
CATE: Computation-aware Neural Architecture Encoding with Transformers Code for paper: CATE: Computation-aware Neural Architecture Encoding with Trans
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation
FIRA is a learning-based commit message generation approach, which first represents code changes via fine-grained graphs and then learns to generate commit messages automatically.
This library attempts to abstract the handling of Sigma rules in Python
This library attempts to abstract the handling of Sigma rules in Python. The rules are parsed using a schema defined with pydantic, and can be easily loaded from YAML files into a structured Python object.
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification
Towards Representation Learning for Atmospheric Dynamics (AtmoDist)
Towards Representation Learning for Atmospheric Dynamics (AtmoDist) The prediction of future climate scenarios under anthropogenic forcing is critical
Pytorch implementation of TailCalibX : Feature Generation for Long-tail Classification
TailCalibX : Feature Generation for Long-tail Classification by Rahul Vigneswaran, Marc T. Law, Vineeth N. Balasubramanian, Makarand Tapaswi [arXiv] [
Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF
Semantic-NeRF: Semantic Neural Radiance Fields Project Page | Video | Paper | Data In-Place Scene Labelling and Understanding with Implicit Scene Repr
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification Introduction. This package includes the pyth
An implementation of an abstract algebra for music tones (pitches).
nbdev template Use this template to more easily create your nbdev project. If you are using an older version of this template, and want to upgrade to
Computer Vision Paper Reviews with Key Summary of paper, End to End Code Practice and Jupyter Notebook converted papers
Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 The repository provides 100+ Pap
RRL: Resnet as representation for Reinforcement Learning
Resnet as representation for Reinforcement Learning (RRL) is a simple yet effective approach for training behaviors directly from visual inputs. We demonstrate that features learned by standard image classification models are general towards different task, robust to visual distractors, and when used in conjunction with standard Imitation Learning or Reinforcement Learning pipelines can efficiently acquire behaviors directly from proprioceptive inputs.
System-oriented IR evaluations are limited to rather abstract understandings of real user behavior
Validating Simulations of User Query Variants This repository contains the scripts of the experiments and evaluations, simulated queries, as well as t
PyTorch implementation for the paper Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime
Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime Created by Prarthana Bhattacharyya. Disclaimer: This is n
Implementation for "Conditional entropy minimization principle for learning domain invariant representation features"
Implementation for "Conditional entropy minimization principle for learning domain invariant representation features". The code is reproduced from thi
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation Prerequisites This repo is built upon a local copy of transfo
Bootstrapped Representation Learning on Graphs
Bootstrapped Representation Learning on Graphs This is the PyTorch implementation of BGRL Bootstrapped Representation Learning on Graphs The main scri
"Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion"(WWW 2021)
STAR_KGC This repo contains the source code of the paper accepted by WWW'2021. "Structure-Augmented Text Representation Learning for Efficient Knowled
CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation
CSKG: The CommonSense Knowledge Graph CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation: AT
Compute execution plan: A DAG representation of work that you want to get done. Individual nodes of the DAG could be simple python or shell tasks or complex deeply nested parallel branches or embedded DAGs themselves.
Hello from magnus Magnus provides four capabilities for data teams: Compute execution plan: A DAG representation of work that you want to get done. In
CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning
CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning This repository contains the code and relevant instructions
This is a small program that prints a user friendly, visual representation, of your current bsp tree
bspcq, q for query A bspc analyzer (utility for bspwm) This is a small program that prints a user friendly, visual representation, of your current bsp
Llvlir - Low Level Variable Length Intermediate Representation
Low Level Variable Length Intermediate Representation Low Level Variable Length
An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions
Agar.io_Q-Learning_AI An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available act
TART - A PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions
TART This project is a PyTorch implementation for Transition Matrix Representati
Code for paper: Towards Tokenized Human Dynamics Representation
Video Tokneization Codebase for video tokenization, based on our paper Towards Tokenized Human Dynamics Representation. Prerequisites (tested under Py
Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.
Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.
A natural language processing model for sequential sentence classification in medical abstracts.
NLP PubMed Medical Research Paper Abstract (Randomized Controlled Trial) A natural language processing model for sequential sentence classification in
Source code for the BMVC-2021 paper "SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation".
SimReg: A Simple Regression Based Framework for Self-supervised Knowledge Distillation Source code for the paper "SimReg: Regression as a Simple Yet E
Eff video representation - Efficient video representation through neural fields
Neural Residual Flow Fields for Efficient Video Representations 1. Download MPI
Transform Python source code into it's most compact representation
Python Minifier Transforms Python source code into it's most compact representation. Try it out! python-minifier currently supports Python 2.7 and Pyt
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition Usage First, install PyTorch 1.7.1+, torchvision 0.8.2
The dynamics of representation learning in shallow, non-linear autoencoders
The dynamics of representation learning in shallow, non-linear autoencoders The package is written in python and uses the pytorch implementation to ML
Repository for the AugmentedPCA Python package.
Overview This Python package provides implementations of Augmented Principal Component Analysis (AugmentedPCA) - a family of linear factor models that
This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents".
Introduction This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents". If
A minimal ascii-representation of your local weather.
Ascii-Weather A simple, ascii-based weather visualizer for the terminal. The ascii-art updates to match the current weather and conditions. Uses ipinf
Official respository for "Band-limited Coordinate Networks for Multiscale Scene Representation"
BACON: Band-limited Coordinate Networks for Multiscale Scene Representation Project Page | Video | Paper Official PyTorch implementation of BACON. BAC
Bacon - Band-limited Coordinate Networks for Multiscale Scene Representation
BACON: Band-limited Coordinate Networks for Multiscale Scene Representation Proj
A small Python library which gives you the IEEE-754 representation of a floating point number.
ieee754 ieee754 is small Python library which gives you the IEEE-754 representation of a floating point number. You can specify a precision given in t
Python meta class and abstract method library with restrictions.
abcmeta Python meta class and abstract method library with restrictions. This library provides a restricted way to validate abstract methods. The Pyth
Datasets, tools, and benchmarks for representation learning of code.
The CodeSearchNet challenge has been concluded We would like to thank all participants for their submissions and we hope that this challenge provided
Python3 / PyTorch implementation of the following paper: Fine-grained Semantics-aware Representation Enhancement for Self-supervisedMonocular Depth Estimation. ICCV 2021 (oral)
FSRE-Depth This is a Python3 / PyTorch implementation of FSRE-Depth, as described in the following paper: Fine-grained Semantics-aware Representation
Audio-to-symbolic Arrangement via Cross-modal Music Representation Learning
Automatic Audio-to-symbolic Arrangement This is the repository of the project "Audio-to-symbolic Arrangement via Cross-modal Music Representation Lear
Self-supervised learning optimally robust representations for domain generalization.
OptDom: Learning Optimal Representations for Domain Generalization This repository contains the official implementation for Optimal Representations fo
PyTorch code for ICPR 2020 paper Future Urban Scene Generation Through Vehicle Synthesis
Future urban scene generation through vehicle synthesis This repository contains Pytorch code for the ICPR2020 paper "Future Urban Scene Generation Th
Code accompanying the paper Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs (Chen et al., CVPR 2020, Oral).
Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs This repository contains PyTorch implementation of our pa
Computer-Vision-Paper-Reviews - Computer Vision Paper Reviews with Key Summary along Papers & Codes
Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 50+ Papers across Computer Visio
Awesome Graph Classification - A collection of important graph embedding, classification and representation learning papers with implementations.
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers
VQMIVC - Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion (Interspeech
VAST - Visualise Abstract Syntax Trees for Python
VAST VAST - Visualise Abstract Syntax Trees for Python. VAST generates ASTs for a given Python script and builds visualisations of them. Install Insta
Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019
USSS_ICCV19 Code for Universal Semi Supervised Semantic Segmentation accepted to ICCV 2019. Full Paper available at https://arxiv.org/abs/1811.10323.
AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Ne
Scaling and Benchmarking Self-Supervised Visual Representation Learning
FAIR Self-Supervision Benchmark is deprecated. Please see VISSL, a ground-up rewrite of benchmark in PyTorch. FAIR Self-Supervision Benchmark This cod
PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE).
GRACE The official PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE). For a thorough resource collection of self-superv
Graph Representation Learning via Graphical Mutual Information Maximization
GMI (Graphical Mutual Information) Graph Representation Learning via Graphical Mutual Information Maximization (Peng Z, Huang W, Luo M, et al., WWW 20
Contrastive Multi-View Representation Learning on Graphs
Contrastive Multi-View Representation Learning on Graphs This work introduces a self-supervised approach based on contrastive multi-view learning to l
Code for ICDM2020 full paper: "Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning"
Subg-Con Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning (Jiao et al., ICDM 2020): https://arxiv.org/abs/2009.10273 Over
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
Graph-InfoClust-GIC [PAKDD 2021] PAKDD'21 version Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs Preprint version Graph InfoClu
Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible
Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible, to be the most reliable with the least complexity possible
Align and Prompt: Video-and-Language Pre-training with Entity Prompts
ALPRO Align and Prompt: Video-and-Language Pre-training with Entity Prompts [Paper] Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, Steven C.H
Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization Authors: Fan-yun Sun, Jordan Hoffm
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation.
Pretrain-Recsys This is our Tensorflow implementation for our WSDM 2021 paper: Bowen Hao, Jing Zhang, Hongzhi Yin, Cuiping Li, Hong Chen. Pre-Training
Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"
GPT-GNN: Generative Pre-Training of Graph Neural Networks GPT-GNN is a pre-training framework to initialize GNNs by generative pre-training. It can be
Autoregressive Predictive Coding: An unsupervised autoregressive model for speech representation learning
Autoregressive Predictive Coding This repository contains the official implementation (in PyTorch) of Autoregressive Predictive Coding (APC) proposed
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)
Self-labelling via simultaneous clustering and representation learning 🆗 🆗 🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvis
PyTorch implementation of MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
MoCo: Momentum Contrast for Unsupervised Visual Representation Learning This is a PyTorch implementation of the MoCo paper: @Article{he2019moco, aut
Unsupervised Representation Learning by Invariance Propagation
Unsupervised Learning by Invariance Propagation This repository is the official implementation of Unsupervised Learning by Invariance Propagation. Pre
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
Learning to Classify Images without Labels This repo contains the Pytorch implementation of our paper: SCAN: Learning to Classify Images without Label
Official PyTorch implementation of the paper "Self-Supervised Relational Reasoning for Representation Learning", NeurIPS 2020 Spotlight.
Official PyTorch implementation of the paper: "Self-Supervised Relational Reasoning for Representation Learning" (2020), Patacchiola, M., and Storkey,
Official implementation of ACMMM'20 paper 'Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework'
Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework Official code for paper, Self-supervised Video Representation Le
Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".
PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".
code for our ECCV-2020 paper: Self-supervised Video Representation Learning by Pace Prediction
Video_Pace This repository contains the code for the following paper: Jiangliu Wang, Jianbo Jiao and Yunhui Liu, "Self-Supervised Video Representation
Video Representation Learning by Recognizing Temporal Transformations. In ECCV, 2020.
Video Representation Learning by Recognizing Temporal Transformations [Project Page] Simon Jenni, Givi Meishvili, and Paolo Favaro. In ECCV, 2020. Thi
[NeurIPS'20] Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.
CoCLR: Self-supervised Co-Training for Video Representation Learning This repository contains the implementation of: InfoNCE (MoCo on videos) UberNCE
[arXiv 2020] Video Representation Learning with Visual Tempo Consistency
Video Representation Learning with Visual Tempo Consistency [Paper] [Project Page] News Full codebae is coming soon Pretained Models For now, we provi
Official Pytorch implementation for AAAI2021 paper (RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning)
RSPNet Official Pytorch implementation for AAAI2021 paper "RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning" [Suppleme
Code for "Unsupervised State Representation Learning in Atari"
Unsupervised State Representation Learning in Atari Ankesh Anand*, Evan Racah*, Sherjil Ozair*, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm This
A small script written in Python3 that generates a visual representation of the Mandelbrot set.
Mandelbrot Set Generator A small script written in Python3 that generates a visual representation of the Mandelbrot set. Abstract The colors in the ou
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition (AGRA, ACM 2020, Oral)
Cross Domain Facial Expression Recognition Benchmark Implementation of papers: Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchm
Backend routes and database for an abstract theoretical app that relates a database of courses, users, and assignments.
Backend routes and database for an abstract theoretical app that relates a database of courses, users, and assignments.
jiant is an NLP toolkit
🚨 Update 🚨 : As of 2021/10/17, the jiant project is no longer being actively maintained. This means there will be no plans to add new models, tasks,