1329 Repositories
Python generalized-linear-models Libraries
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation This reposi
Event queue (Equeue) dialect is an MLIR Dialect that models concurrent devices in terms of control and structure.
Event Queue Dialect Event queue (Equeue) dialect is an MLIR Dialect that models concurrent devices in terms of control and structure. Motivation The m
Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
English | 简体中文 | 繁體中文 | 한국어 State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models
Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022)
Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022) Please cite "Independent SE(3)-Equivar
This repository provides an efficient PyTorch-based library for training deep models.
An Efficient Library for Training Deep Models This repository provides an efficient PyTorch-based library for training deep models. Installation Make
Trafffic prediction analysis using hybrid models - Machine Learning
Hybrid Machine learning Model Clone the Repository Create a new Directory as assests and download the model from the below link Model Link To Start th
Tensorflow2 Keras-based Semantic Segmentation Models Implementation
Tensorflow2 Keras-based Semantic Segmentation Models Implementation
NeuralForecast is a Python library for time series forecasting with deep learning models
NeuralForecast is a Python library for time series forecasting with deep learning models. It includes benchmark datasets, data-loading utilities, evaluation functions, statistical tests, univariate model benchmarks and SOTA models implemented in PyTorch and PyTorchLightning.
Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5
NLP-Summarizer Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5 This project aimed to provide in
Multiple Linear Regression using the LinearRegression class from sklearn.linear_model library
Multiple-Linear-Regression-master - A python program to implement Multiple Linear Regression using the LinearRegression class from sklearn.linear model library
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
Probabilistic U-Net + **Update** + An improved Model (the Hierarchical Probabilistic U-Net) + LIDC crops is now available. See below. Re-implementatio
A collection of pre-trained StyleGAN2 models trained on different datasets at different resolution.
Awesome Pretrained StyleGAN2 A collection of pre-trained StyleGAN2 models trained on different datasets at different resolution. Note the readme is a
A curated list of Generative Deep Art projects, tools, artworks, and models
Generative Deep Art A curated list of Generative Deep Art projects, tools, artworks, and models Inbox Get started with making AI art in 2022 – deeplea
Semantic Segmentation Suite in TensorFlow
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.
Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b
Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift
This repository contains the official code of OSTAR in "Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift" (ICLR 2022).
Machine Learning Models were applied to predict the mass of the brain based on gender, age ranges, and head size.
Brain Weight in Humans Variations of head sizes and brain weights in humans Kaggle dataset obtained from this link by Anubhab Swain. Image obtained fr
The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"
Hierarchical Token Semantic Audio Transformer Introduction The Code Repository for "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound
This is an early in-development version of training CLIP models with hivemind.
A transformer that does not hog your GPU memory This is an early in-development codebase: if you want a stable and documented hivemind codebase, look
Constrained Language Models Yield Few-Shot Semantic Parsers
Constrained Language Models Yield Few-Shot Semantic Parsers This repository contains tools and instructions for reproducing the experiments in the pap
This GitHub repo consists of Code and Some results of project- Diabetes Treatment using Gold nanoparticles. These Consist of ML Models used for prediction Diabetes and further the basic theory and working of Gold nanoparticles.
GoldNanoparticles This GitHub repo consists of Code and Some results of project- Diabetes Treatment using Gold nanoparticles. These Consist of ML Mode
spaCy-wrap: For Wrapping fine-tuned transformers in spaCy pipelines
spaCy-wrap: For Wrapping fine-tuned transformers in spaCy pipelines spaCy-wrap is minimal library intended for wrapping fine-tuned transformers from t
Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction".
GNN_PPI Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction". Lear
In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a model using HugginFace transformers framework.
Transformers are all you need In this workshop we will be exploring NLP state of the art transformers, with SOTA models like T5 and BERT, then build a
Training DiffWave using variational method from Variational Diffusion Models.
Variational DiffWave Training DiffWave using variational method from Variational Diffusion Models. Quick Start python train_distributed.py discrete_10
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
DART Implementation for ICLR2022 paper Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners. Environment [email protected] Use pi
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构。 文档地址:https://basecls.readthedocs.io 安装 安装环境 BaseCls 需要 Python = 3.6。 BaseCls 依赖 M
NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models
NaturalCC NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models for many software engineering tasks,
smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the COVID-19 case by Storvik et al
smc.covid smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectiou
An Approach to Explore Logistic Regression Models
User-centered Regression An Approach to Explore Logistic Regression Models This tool applies the potential of Attribute-RadViz in identifying correlat
Official repository for the paper "On Evaluation Metrics for Graph Generative Models"
On Evaluation Metrics for Graph Generative Models Authors: Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham Taylor This is the offic
This is the source code for the experiments related to the paper Unsupervised Audio Source Separation Using Differentiable Parametric Source Models
Unsupervised Audio Source Separation Using Differentiable Parametric Source Models This is the source code for the experiments related to the paper Un
PyTorch implementation of our paper How robust are discriminatively trained zero-shot learning models?
How robust are discriminatively trained zero-shot learning models? This repository contains the PyTorch implementation of our paper How robust are dis
Check out the StyleGAN repo and place it in the same directory hierarchy as the present repo
Variational Model Inversion Attacks Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani Most commands are in run_scripts. W
Main Results on ImageNet with Pretrained Models
This repository contains Pytorch evaluation code, training code and pretrained models for the following projects: SPACH (A Battle of Network Structure
Title: Graduate-Admissions-Predictor
The purpose of this project is create a predictive model capable of identifying the probability of a person securing an admit based on their personal profile parameters. Simplified visualisations have been created for understanding the data. 80% accuracy was achieved on the test set.
The Dual Memory is build from a simple CNN for the deep memory and Linear Regression fro the fast Memory
Simple-DMA a simple Dual Memory Architecture for classifications. based on the paper Dual-Memory Deep Learning Architectures for Lifelong Learning of
Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch
Semantic Segmentation Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch Features Applicable to followin
Twitter bot that uses NLP models to summarize news articles referenced in a user's twitter timeline
Twitter-News-Summarizer Twitter bot that uses NLP models to summarize news articles referenced in a user's twitter timeline 1.) Extracts all tweets fr
OptiPLANT is a cloud-based based system that empowers professional and non-professional data scientists to build high-quality predictive models
OptiPLANT OptiPLANT is a cloud-based based system that empowers professional and non-professional data scientists to build high-quality predictive mod
Hitters Linear Regression - Hitters Linear Regression With Python
Hitters_Linear_Regression Kullanacağımız veri seti Carnegie Mellon Üniversitesi'
This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language Models"
GreaseLM: Graph REASoning Enhanced Language Models This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language
This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning
Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning This repository contains pre-trained models and some evaluation
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis
TweebankNLP This repo contains the new Tweebank-NER dataset and off-the-shelf Twitter-Stanza pipeline for state-of-the-art Tweet NLP, as described in
Revisiting Weakly Supervised Pre-Training of Visual Perception Models
SWAG: Supervised Weakly from hashtAGs This repository contains SWAG models from the paper Revisiting Weakly Supervised Pre-Training of Visual Percepti
On Out-of-distribution Detection with Energy-based Models
On Out-of-distribution Detection with Energy-based Models This repository contains the code for the experiments conducted in the paper On Out-of-distr
This repository contains code to train and render Mixture of Volumetric Primitives (MVP) models
Mixture of Volumetric Primitives -- Training and Evaluation This repository contains code to train and render Mixture of Volumetric Primitives (MVP) m
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali
Boltzmann visualization - Visualize the Boltzmann distribution for simple quantum models of molecular motion
Boltzmann visualization - Visualize the Boltzmann distribution for simple quantum models of molecular motion
f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation
f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation [Paper] [PyTorch] [MXNet] [Video] This repository provides code for training
BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions
BERTopic BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable
COVID-VIT: Classification of Covid-19 from CT chest images based on vision transformer models
COVID-ViT COVID-VIT: Classification of Covid-19 from CT chest images based on vision transformer models This code is to response to te MIA-COV19 compe
Pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments
Cascaded-FCN This repository contains the pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments the liver and its lesions out of
Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets
Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets (including obl
This repository contains code, network definitions and pre-trained models for working on remote sensing images using deep learning
Deep learning for Earth Observation This repository contains code, network definitions and pre-trained models for working on remote sensing images usi
A collection of semantic image segmentation models implemented in TensorFlow
A collection of semantic image segmentation models implemented in TensorFlow. Contains data-loaders for the generic and medical benchmark datasets.
On Evaluation Metrics for Graph Generative Models
On Evaluation Metrics for Graph Generative Models Authors: Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham Taylor This is the offic
Creating Multi Task Models With Keras
Creating Multi Task Models With Keras About The Project! I used the keras and Tensorflow Library, To build a Deep Learning Neural Network to Creating
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch
Curvipy - The Python package for visualizing curves and linear transformations in a super simple way
Curvipy - The Python package for visualizing curves and linear transformations in a super simple way
Feature engineering and machine learning: together at last
Feature engineering and machine learning: together at last! Lambdo is a workflow engine which significantly simplifies data analysis by unifying featu
Neuron class provides LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neurons learned with Gradient descent or LeLevenberg–Marquardt algorithm
Neuron class provides LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neurons learned with Gradient descent or LeLevenberg–Marquardt algorithm
Pydrawer: The Python package for visualizing curves and linear transformations in a super simple way
pydrawer 📐 The Python package for visualizing curves and linear transformations in a super simple way. ✏️ Installation Install pydrawer package with
OCR-D wrapper for detectron2 based segmentation models
ocrd_detectron2 OCR-D wrapper for detectron2 based segmentation models Introduction Installation Usage OCR-D processor interface ocrd-detectron2-segm
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents [Project Page] [Paper] [Video] Wenlong Huang1, Pieter Abbee
TLXZoo - Pre-trained models based on TensorLayerX
Pre-trained models based on TensorLayerX. TensorLayerX is a multi-backend AI fra
Blackstone is a spaCy model and library for processing long-form, unstructured legal text
Blackstone Blackstone is a spaCy model and library for processing long-form, unstructured legal text. Blackstone is an experimental research project f
The Official PyTorch Implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 spotlight paper)
Official PyTorch implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 Spotlight Paper) Zhisheng
PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet
PyTorch Image Classification Following papers are implemented using PyTorch. ResNet (1512.03385) ResNet-preact (1603.05027) WRN (1605.07146) DenseNet
T‘rex Park is a Youzan sponsored project. Offering Chinese NLP and image models pretrained from E-commerce datasets
T‘rex Park is a Youzan sponsored project. Offering Chinese NLP and image models pretrained from E-commerce datasets (product titles, images, comments, etc.).
TweebankNLP - Pre-trained Tweet NLP Pipeline (NER, tokenization, lemmatization, POS tagging, dependency parsing) + Models + Tweebank-NER
TweebankNLP This repo contains the new Tweebank-NER dataset and Twitter-Stanza p
ColossalAI-Examples - Examples of training models with hybrid parallelism using ColossalAI
ColossalAI-Examples This repository contains examples of training models with Co
Pipeline for employing a Lightweight deep learning models for LOW-power systems
PL-LOW A high-performance deep learning model lightweight pipeline that gradually lightens deep neural networks in order to utilize high-performance d
🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"
PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)" Unofficial PyTorch Implementation of Progressi
cl;asification problem using classification models in supervised learning
wine-quality-predition---classification cl;asification problem using classification models in supervised learning Wine Quality Prediction Analysis - C
Mortality risk prediction for COVID-19 patients using XGBoost models
Mortality risk prediction for COVID-19 patients using XGBoost models Using demographic and lab test data received from the HM Hospitales in Spain, I b
A handy tool for common machine learning models' hyper-parameter tuning.
Common machine learning models' hyperparameter tuning This repo is for a collection of hyper-parameter tuning for "common" machine learning models, in
Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters"
Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters" Pipeline of CLIP-Adapter CLIP-Adapter is a drop-in modul
This project is an Algorithm Visualizer where a user can visualize algorithms like Bubble Sort, Merge Sort, Quick Sort, Selection Sort, Linear Search and Binary Search.
Algo_Visualizer This project is an Algorithm Visualizer where a user can visualize common algorithms like "Bubble Sort", "Merge Sort", "Quick Sort", "
The original weights of some Caffe models, ported to PyTorch.
pytorch-caffe-models This repo contains the original weights of some Caffe models, ported to PyTorch. Currently there are: GoogLeNet (Going Deeper wit
A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.
A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.
Mixed Neural Likelihood Estimation for models of decision-making
Mixed neural likelihood estimation for models of decision-making Mixed neural likelihood estimation (MNLE) enables Bayesian parameter inference for mo
ML models and internal tensors 3D visualizer
The free Zetane Viewer is a tool to help understand and accelerate discovery in machine learning and artificial neural networks. It can be used to ope
Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other
ML_Model_implementaion Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other dectree_model: Implementation o
Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data
VIMuRe Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data. If you use this code please cite this article (preprint). De
As a part of the HAKE project, includes the reproduced SOTA models and the corresponding HAKE-enhanced versions (CVPR2020).
HAKE-Action HAKE-Action (TensorFlow) is a project to open the SOTA action understanding studies based on our Human Activity Knowledge Engine. It inclu
Implementing SYNTHESIZER: Rethinking Self-Attention in Transformer Models using Pytorch
Implementing SYNTHESIZER: Rethinking Self-Attention in Transformer Models using Pytorch Reference Paper URL Author: Yi Tay, Dara Bahri, Donald Metzler
Official code repository of the paper Linear Transformers Are Secretly Fast Weight Programmers.
Linear Transformers Are Secretly Fast Weight Programmers This repository contains the code accompanying the paper Linear Transformers Are Secretly Fas
Node-level Graph Regression with Deep Gaussian Process Models
Node-level Graph Regression with Deep Gaussian Process Models Prerequests our implementation is mainly based on tensorflow 1.x and gpflow 1.x: python
Meta learning algorithms to train cross-lingual NLI (multi-task) models
Meta learning algorithms to train cross-lingual NLI (multi-task) models
Exploring Machine Learning Models for detecting anomalous behavior in credit-card transactions. It's crucial that credit-card companies are able to recognize fraudulent activity so that customers are not charged for items they didn't purchase.
Credit Card Fraud Detection Came across this mocked-up dataset of customer transactions at [Capital One Recruitment Challenge](https://github.com/Capi
Unofficial JAX implementations of Deep Learning models
JAX Models Table of Contents About The Project Getting Started Prerequisites Installation Usage Contributing License Contact About The Project The JAX
This is the official implementation of our proposed SwinMR
SwinMR This is the official implementation of our proposed SwinMR: Swin Transformer for Fast MRI Please cite: @article{huang2022swin, title={Swi
Public Models considered for emotion estimation from EEG
Emotion-EEG Set of models for emotion estimation from EEG. Composed by the combination of two deep-learing models learning together (RNN and CNN) with
Simple Tensorflow implementation of Toward Spatially Unbiased Generative Models (ICCV 2021)
Spatial unbiased GANs — Simple TensorFlow Implementation [Paper] : Toward Spatially Unbiased Generative Models (ICCV 2021) Abstract Recent image gener
Pytorch implementation of MLP-Mixer with loading pre-trained models.
MLP-Mixer-Pytorch PyTorch implementation of MLP-Mixer: An all-MLP Architecture for Vision with the function of loading official ImageNet pre-trained p
Generic Foreground Segmentation in Images
Pixel Objectness The following repository contains pretrained model for pixel objectness. Please visit our project page for the paper and visual resul
Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.
Computational Design and Dynamics of Soft Systems · This is a repository that contains the source code for generating the lecture notes, handouts, exe
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.