133 Repositories
Python Meta-Balance Libraries
PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI
data2vec-pytorch PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI (F
Code for "Continuous-Time Meta-Learning with Forward Mode Differentiation" (ICLR 2022)
Continuous-Time Meta-Learning with Forward Mode Differentiation ICLR 2022 (Spotlight) - Installation - Example - Citation This repository contains the
Mizogg-Bitcoin-Tools - A Python Tools for Bitcoin Information Balance, HASH160, DEC
Mizogg-Bitcoin-Tools Tools for Bitcoin Information Balance, HASH160, DEC, Englis
This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationships.
Auto-Lambda This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationship
Auto White-Balance Correction for Mixed-Illuminant Scenes
Auto White-Balance Correction for Mixed-Illuminant Scenes Mahmoud Afifi, Marcus A. Brubaker, and Michael S. Brown York University Video Reference code
This repository contains examples of Task-Informed Meta-Learning
Task-Informed Meta-Learning This repository contains examples of Task-Informed Meta-Learning (paper). We consider two tasks: Crop Type Classification
Meta-meta-learning with evolution and plasticity
Evolve plastic networks to be able to automatically acquire novel cognitive (meta-learning) tasks
MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios
MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios This is the official TensorFlow implementation of MetaTTE in the
Noether Networks: meta-learning useful conserved quantities
Noether Networks: meta-learning useful conserved quantities This repository contains the code necessary to reproduce experiments from "Noether Network
PyTorch META-DATASET (Few-shot classification benchmark)
PyTorch META-DATASET (Few-shot classification benchmark) This repo contains a PyTorch implementation of meta-dataset and a unified implementation of s
Crypto Meta Extractor
Crypto Meta Extractor This repository contains the code which extracts some metadata of all the cryptocurrencies listed (9K) on CoinMarketCap. Coding
FSL-Mate: A collection of resources for few-shot learning (FSL).
FSL-Mate is a collection of resources for few-shot learning (FSL). In particular, FSL-Mate currently contains FewShotPapers: a paper list which tracks
ECLARE: Extreme Classification with Label Graph Correlations
ECLARE ECLARE: Extreme Classification with Label Graph Correlations @InProceedings{Mittal21b, author = "Mittal, A. and Sachdeva, N. and Agrawal
Meta Self-learning for Multi-Source Domain Adaptation: A Benchmark
Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark Project | Arxiv | YouTube | | Abstract In recent years, deep learning-based methods
Meta learning algorithms to train cross-lingual NLI (multi-task) models
Meta learning algorithms to train cross-lingual NLI (multi-task) models
Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples
Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples (WACV 2022) and Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning (TPAMI 2022 - in submission)
Create and finder all address wallet bitcoin and check balance , transaction
BTCCrackWallet Create and finder all address wallet bitcoin and check balance , transaction bitcoin wallet generator generated address wallet , public
STARCH compuets regional extreme storm physical characteristics and moisture balance based on spatiotemporal precipitation data from reanalysis or climate model data.
STARCH (Storm Tracking And Regional CHaracterization) STARCH computes regional extreme storm physical and moisture balance characteristics based on sp
Implements a polyglot REPL which supports multiple languages and shared meta-object protocol scope between REPLs.
MetaCall Polyglot REPL Description This repository implements a Polyglot REPL which shares the state of the meta-object protocol between the REPLs. Us
Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data
1 Meta-FDMIxup Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data. (ACM MM 2021) paper News! the rep
Official Implementation for Fast Training of Neural Lumigraph Representations using Meta Learning.
Fast Training of Neural Lumigraph Representations using Meta Learning Project Page | Paper | Data Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzst
Meta Learning Backpropagation And Improving It (VSML)
Meta Learning Backpropagation And Improving It (VSML) This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts
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
MT-GAN-PyTorch - PyTorch Implementation of Learning to Transfer: Unsupervised Domain Translation via Meta-Learning
MT-GAN-PyTorch PyTorch Implementation of AAAI-2020 Paper "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning" Dependency: Python
Web Scraping COVID 19 Meta Portal with Python
Web-Scraping-COVID-19-Meta-Portal-with-Python - Requests API and Beautiful Soup to scrape real-time COVID statistics from worldometer website and perform data cleaning and visual analysis in Jupyter notebook.
Meta Learning for Semi-Supervised Few-Shot Classification
few-shot-ssl-public Code for paper Meta-Learning for Semi-Supervised Few-Shot Classification. [arxiv] Dependencies cv2 numpy pandas python 2.7 / 3.5+
Official PyTorch Implementation of "Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs". NeurIPS 2020.
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs This repository is the implementation of SELAR. Dasol Hwang* , Jinyoung Pa
Basic infrastructure for writing scripts in Python
Base Script Python is an excellent language that makes writing scripts very straightforward. Over the course of writing many scripts, we realized that
MetaMove is written in Python3 and aims at easing batch renaming operations based on file meta data.
MetaMove MetaMove is written in Python3 and aims at easing batch renaming operations based on file meta data. MetaMove abuses eval combined with f-str
Generate bitcoin public and private keys and check if they match a filelist of existing addresses that have a nonzero balance
btc-heist Running Install deps, i.e., python3 -m pip install -r requirements.txt Download the CSV dump of all bitcoin addresses with a balance and cut
Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]
Offline Meta-Reinforcement Learning with Advantage Weighting (MACAW) MACAW code used for the experiments in the ICML 2021 paper. Installing the enviro
Auxiliary Raw Net (ARawNet) is a ASVSpoof detection model taking both raw waveform and handcrafted features as inputs, to balance the trade-off between performance and model complexity.
Overview This repository is an implementation of the Auxiliary Raw Net (ARawNet), which is ASVSpoof detection system taking both raw waveform and hand
Reference code for the paper "Cross-Camera Convolutional Color Constancy" (ICCV 2021)
Cross-Camera Convolutional Color Constancy, ICCV 2021 (Oral) Mahmoud Afifi1,2, Jonathan T. Barron2, Chloe LeGendre2, Yun-Ta Tsai2, and Francois Bleibe
Reimplementation of NeurIPS'19: "Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting" by Shu et al.
[Re] Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Reimplementation of NeurIPS'19: "Meta-Weight-Net: Learning an Explicit Mapping
Artifacts for paper "MMO: Meta Multi-Objectivization for Software Configuration Tuning"
MMO: Meta Multi-Objectivization for Software Configuration Tuning This repository contains the data and code for the following paper that is currently
FinRL-Meta: A Universe for Data-Driven Financial Reinforcement Learning. 🔥
FinRL-Meta: A Universe of Market Environments. FinRL-Meta is a universe of market environments for data-driven financial reinforcement learning. Users
[KDD 2021, Research Track] DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
DiffMG This repository contains the code for our KDD 2021 Research Track paper: DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neura
NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
Meta-Weight-Net NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Official Pytorch implementation for noisy labels). The
[NeurIPS 2020] Code for the paper "Balanced Meta-Softmax for Long-Tailed Visual Recognition"
Balanced Meta-Softmax Code for the paper Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu
[CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition (CVPR 2021) arXiv Prerequisite PyTorch = 1.2.0 Python3 torchvision PIL argpar
Applying curriculum to meta-learning for few shot classification
Curriculum Meta-Learning for Few-shot Classification We propose an adaptation of the curriculum training framework, applicable to state-of-the-art met
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning This is the official repository of "Camera Distortion-
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)
[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio
Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021)
Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021) In this repository we provide PyTorch implementations for GeMCL; a
A very basic starter bot based on CryptoKKing with a small balance
starterbot A very basic starter bot based on CryptoKKing with a small balance, use at your own risk. I have since upgraded this script significantly a
Forecasting prices using Facebook/Meta's Prophet model
CryptoForecasting using Machine and Deep learning (Part 1) CryptoForecasting using Machine Learning The main aspect of predicting the stock-related da
CLI tool to show the current crypto balance
CryptoBoard The simple python CLI tool for one currency to show the current crypto balance yours purchases. That's all. Data source is from https://ww
Code accompanying paper: Meta-Learning to Improve Pre-Training
Meta-Learning to Improve Pre-Training This folder contains code to run experiments in the paper Meta-Learning to Improve Pre-Training, NeurIPS 2021. P
Resources related to EMNLP 2021 paper "FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations"
FAME: Feature-based Adversarial Meta-Embeddings This is the companion code for the experiments reported in the paper "FAME: Feature-Based Adversarial
(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification"
SSR (NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductivefew-shot classification" [Paper] [Project webpage]
Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"
L2F - Learning to Forget for Meta-Learning Sungyong Baik, Seokil Hong, Kyoung Mu Lee Source code for CVPR 2020 paper "Learning to Forget for Meta-Lear
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.
Jittor: a Just-in-time(JIT) deep learning framework Quickstart | Install | Tutorial | Chinese Jittor is a high-performance deep learning framework bas
Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)
MeTAL - Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral) Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaes
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. Hyperactive: is very easy to lear
Naszilla is a Python library for neural architecture search (NAS)
A repository to compare many popular NAS algorithms seamlessly across three popular benchmarks (NASBench 101, 201, and 301). You can implement your ow
Robust Self-augmentation for NER with Meta-reweighting
Robust Self-augmentation for NER with Meta-reweighting
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.
PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int
TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL, and utterance id
TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL
Official implementation of Meta-StyleSpeech and StyleSpeech
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code
StackNet is a computational, scalable and analytical Meta modelling framework
StackNet This repository contains StackNet Meta modelling methodology (and software) which is part of my work as a PhD Student in the computer science
CNN Based Meta-Learning for Noisy Image Classification and Template Matching
CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to
A meta plugin for processing timelapse data timepoint by timepoint in napari
napari-time-slicer A meta plugin for processing timelapse data timepoint by timepoint. It enables a list of napari plugins to process 2D+t or 3D+t dat
M.E.A.T. - Mobile Evidence Acquisition Toolkit
M.E.A.T. - Mobile Evidence Acquisition Toolkit Meet M.E.A.T! From Jack Farley - BlackStone Discovery This toolkit aims to help forensicators perform d
Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech
Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech This repository is the official implementation of "Meta-TTS: Meta-Learning for Few
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems This is our experimental code for RecSys 2021 paper "Learning
Open Source Discord bot with many cool features like Weather, Balance, Avatar, User, Server, RP-commands, Gif search, YouTube search, VK post search etc.
Сокобот Дискорд бот с открытым исходным кодом. Содержит в себе экономику, полезные команды (!аватар, !юзер, !сервер и тд.), рп-команды (!обнять, !глад
Watches your earnings on EarnApp and notifies you when you earned balance or received an payout.
EarnApp-Earning-Monitor Watches your earnings on EarnApp and notifies you when you earned balance or received an payout. Installation Install Python3
Official implementation of Generalized Data Weighting via Class-level Gradient Manipulation (NeurIPS 2021).
Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas
Source code of the paper Meta-learning with an Adaptive Task Scheduler.
ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the
Generalized Data Weighting via Class-level Gradient Manipulation
Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas
Companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsura et al.
META-RS This is the companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsu
Open source single image super-resolution toolbox containing various functionality for training a diverse number of state-of-the-art super-resolution models. Also acts as the companion code for the IEEE signal processing letters paper titled 'Improving Super-Resolution Performance using Meta-Attention Layers’.
Deep-FIR Codebase - Super Resolution Meta Attention Networks About This repository contains the main coding framework accompanying our work on meta-at
Meta-Learning Sparse Implicit Neural Representations (NeurIPS 2021)
Meta-SparseINR Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, N
Learning where to learn - Gradient sparsity in meta and continual learning
Learning where to learn - Gradient sparsity in meta and continual learning In this paper, we investigate gradient sparsity found by MAML in various co
Code of the paper "Multi-Task Meta-Learning Modification with Stochastic Approximation".
Multi-Task Meta-Learning Modification with Stochastic Approximation This repository contains the code for the paper "Multi-Task Meta-Learning Modifica
This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).
NeurIPS 2021 (Spotlight): Task-Adaptive Neural Network Search with Meta-Contrastive Learning This is an official PyTorch implementation of Task-Adapti
This repository is the code of the paper "Sparse Spatial Transformers for Few-Shot Learning".
🌟 Sparse Spatial Transformers for Few-Shot Learning This code implements the Sparse Spatial Transformers for Few-Shot Learning(SSFormers). Our code i
Polynomial-time Meta-Interpretive Learning
Louise - polynomial-time Program Learning Getting help with Louise Louise's author can be reached by email at [email protected]. Please use this email t
[TPAMI 2021] iOD: Incremental Object Detection via Meta-Learning
Incremental Object Detection via Meta-Learning To appear in an upcoming issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence (T
This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".
ST This is the code of NeurIPS 2021 paper "Towards Enabling Meta-Learning from Target Models". If you use any content of this repo for your work, plea
Meta graph convolutional neural network-assisted resilient swarm communications
Resilient UAV Swarm Communications with Graph Convolutional Neural Network This repository contains the source codes of Resilient UAV Swarm Communicat
BMVC 2021: This is the github repository for "Few Shot Temporal Action Localization using Query Adaptive Transformers" accepted in British Machine Vision Conference (BMVC) 2021, Virtual
FS-QAT: Few Shot Temporal Action Localization using Query Adaptive Transformer Accepted as Poster in BMVC 2021 This is an official implementation in P
Bayesian Meta-Learning Through Variational Gaussian Processes
vmgp This is the repository of Vivek Myers and Nikhil Sardana for our CS 330 final project, Bayesian Meta-Learning Through Variational Gaussian Proces
This python project contains a class FileProcessor which allows one to grab a file and get some meta data and header information from it
This python project contains a class FileProcessor which allows one to grab a file and get some meta data and header information from it. In the current state, it outputs a PrettyTable to txt file as well as the raw data from that table into a csv.
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)
[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio
A hifiasm fork for metagenome assembly using Hifi reads.
hifiasm_meta - de novo metagenome assembler, based on hifiasm, a haplotype-resolved de novo assembler for PacBio Hifi reads.
Temporal Meta-path Guided Explainable Recommendation (WSDM2021)
Temporal Meta-path Guided Explainable Recommendation (WSDM2021) TMER Code of paper "Temporal Meta-path Guided Explainable Recommendation". Requirement
ProMP: Proximal Meta-Policy Search
ProMP: Proximal Meta-Policy Search Implementations corresponding to ProMP (Rothfuss et al., 2018). Overall this repository consists of two branches: m
Simple (but Strong) Baselines for POMDPs
Recurrent Model-Free RL is a Strong Baseline for Many POMDPs Welcome to the POMDP world! This repo provides some simple baselines for POMDPs, specific
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination (ICCV 2021) Dataset License This work is l
Official Pytorch implementation of Meta Internal Learning
Official Pytorch implementation of Meta Internal Learning
Run with one command grafana, prometheus, and a python script to collect and display cryptocurrency prices and track your wallet balance.
CryptoWatch Track your favorite crypto coin price and your wallet balance. Install Create .env: ADMIN_USER=admin ADMIN_PASSWORD=admin Configure you
A CRM department in a local bank works on classify their lost customers with their past datas. So they want predict with these method that average loss balance and passive duration for future.
Rule-Based-Classification-in-a-Banking-Case. A CRM department in a local bank works on classify their lost customers with their past datas. So they wa
EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections Ruiqi Zhong, Kristy Lee*, Zheng Zhang*, Dan Klein EMN
PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)
pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv
Official implementation of Meta-StyleSpeech and StyleSpeech
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code
Code for the ICML 2021 paper "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation", Haoxiang Wang, Han Zhao, Bo Li.
Bridging Multi-Task Learning and Meta-Learning Code for the ICML 2021 paper "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Trainin
An official TensorFlow implementation of “CLCC: Contrastive Learning for Color Constancy” accepted at CVPR 2021.
CLCC: Contrastive Learning for Color Constancy (CVPR 2021) Yi-Chen Lo*, Chia-Che Chang*, Hsuan-Chao Chiu, Yu-Hao Huang, Chia-Ping Chen, Yu-Lin Chang,
Meta-learning for NLP
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks Code for training the meta-learning models and fine-tuning on downstr
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
This repository is the official PyTorch implementation of Meta-Balance. Find the paper on arxiv MetaBalance: High-Performance Neural Networks for Clas