Deep Learning

Frameworks for Neural Networks and Deep Learning.

Newest releases

patrick-kidger A micro-library as a convenience for turning SymPy expressions into PyTorch Modules.

CW-Huang "Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization"

aiqc Framework for local, reproducible, batched deep learning for research

shunsukesaito This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"

replicate Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook.

VITA-Group [preprint] "Sandwich Batch Normalization" by Xinyu Gong, Wuyang Chen, Tianlong Chen and Zhangyang Wang

ermongroup PyTorch implementation for the ICLR 2020 paper "Understanding the Limitations of Variational Mutual Information Estimators"

epfml Abstract: Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep neural networks.

ChuanMeng Code for SIGIR-2020 full paper: DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation

pvjosue Optimization framework developed in Pytorch, allowing calibration, and joint optimization of optics and deep learning models.

Elin24 Awesome Low-light Enhancement

googlecreativelab Say hello to Alto, a little teachable object! Alto is designed to demonstrate the most basic aspects of machine learning (ML) by building a machine you can teach yourself. Alto uses the Coral USB Accelerator and Raspberry Pi to he

Speech-AI Asteroid is a Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code that supports a large range of datasets and architectures, and a set of recipes to repro

signals-dev The GreenGuard project is a collection of end-to-end solutions for machine learning problems commonly found in monitoring wind energy production systems. Most tasks utilize sensor data emanating from monitoring systems. We utilize

fosfrancesco A dataset of 222 digital musical scores aligned with 1068 performances (more than 92 hours) of Western classical piano music.

AWehenkel Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tractable Jacobian.

gistvision MOCA (Modular Object-Centric Approach) addresses the task of long horizon instruction following with a modular architecture that decouples a task into visual perception and action policy prediction.

whai362 This repository contains PyTorch evaluation code, training code and pretrained models for PVT (Pyramid Vision Transformer). Like ResNet, PVT is a pure transformer backbone that can be easily plugged in most downstream task models.

uniBruce MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation [ECCV2020]

AlanChou PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.

JohanSamir Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research

google-research This repository contains the paired word-tag data required to train a word-to-label sequence tagger (as described in our paper). In addition, the generated images from the proposed TReCS model are also provided for the LN-COCO and

shaoshengsong QuarkDet lightweight object detection in PyTorch .Real-Time Object Detection on Mobile Devices.

openai This is the official PyTorch package for the discrete VAE used for DALL·E.

rlabbe Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises incl

LAMDA-NJU DF21 is an implementation of Deep Forest 2021.2.1

mrdbourke Code and files to go along with CS329s machine learning model deployment tutorial.

facebookresearch RLStructures is a library to facilitate the implementation of new reinforcement learning algorithms. It includes a library, a tutorial, and different RL algorithms provided as examples.

PRBonn Code for Langer et al. "Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks", IROS, 2020.

myeonghak Deep Graph Library(DGL)은 기존의 DL 프레임워크(e.g. PyTorch, MXNet, Gluon 등)위에 그래프 뉴럴 네트워크 모델을 간편하게 구현하기 위한 Python 패키지입니다. DGL은 아키텍쳐 디자인 상에서 NetworkX의 API와 패러다임을 따르고 지향하고 있습니다.

safreita1 TIGER is a Python toolbox to conduct graph vulnerability and robustness research. TIGER contains numerous state-of-the-art methods to help users conduct graph vulnerability and robustness analysis on graph structured data.

nathanaelbosch ProbNumDiffEq.jl provides probabilistic ODE solvers for the DifferentialEquations.jl ecosystem.

n2cholas A Flax (Linen) implementation of ResNet (He, Kaiming, et al. 2015), ResNet-D (He, Tong et al. 2020), and ResNest (Zhang, Hang et al. 2020). The code is modular so you can mix and match the various stem, residual, and bottleneck im