Python Neural-recording Resources

PyDEns PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks. With PyDEns one can solve PD

NeuralPDE NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using scientific machine learni

Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b

Zero-DCE and Zero-DCE++(Lite architechture for Mobile and edge Devices) Papers Abstract The paper presents a novel method, Zero-Reference Deep Curve E

1 SNAS4MTF This repo is the official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting. 1.1 The frame

关于实现的一点说明 山东大学 2020级 苏博南 www.subonan.com 文件说明 tools.py 这里面主要有两个函数： resize(a, lenb) 这其实是我找同学写的一个小算法hhh。给出一个$28\times 28$的方阵a，返回一个$lenb\times lenb$的方阵。因

A self-hosted streaming platform with Discord authentication, auto-recording and more!

Langy Langy is a reading-focused foreign language learning aid orientated towards young children. Reading is an activity that every child knows. It is

This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm. It contains the code to reproduce the results presented in the original paper: https://arxiv.org/abs/2112.03670

Neural Style Transfer Web App - [FastAPI + streamlit + Docker] NST - application based on the Perceptual Losses for Real-Time Style Transfer and Super

Neural 3D Mesh Renderer in PadddlePaddle A PaddlePaddle version of Neural Renderer, refer to its PyTorch version Install Run: pip install neural-rende

Dual Correlation Reduction Network An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022. Any

Sparse Structure Learning via Graph Neural Networks for inductive document classification Make graph dataset create co-occurrence graph for datasets.

HETU Documentation | Examples Hetu is a high-performance distributed deep learning system targeting trillions of parameters DL model training, develop

Typilus A deep learning algorithm for predicting types in Python. Please find a preprint here. This repository contains its implementation (src/) and

Detect waste AI4Good project for detecting waste in environment. www.detectwaste.ml. Our latest results were published in Waste Management journal in

SHGNN: Structure-Aware Heterogeneous Graph Neural Network The source code and dataset of the paper: SHGNN: Structure-Aware Heterogeneous Graph Neural

3D-GMPDCNN Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network PyTorch implementation of "Geological Modeling Usin

PyGAD: Genetic Algorithm in Python PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine lear

DataSelection-NMT Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts Quick update: The paper got accepted o

Programming with Neural Surrogates of Programs

Deep Semi-Supervised Teacher-Student Material Synthesizability Prediction Citation: Semi-supervised teacher-student deep neural network for materials

Centroid UNet - Locating Object Centroids in Aerial/Serial Images Introduction Centroid-UNet is deep neural network model to detect centroids from Aer

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.

Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics @WIFS2021 (Montpellier, France) Rony Abecidan, Vincent Itier, Jeremie Boulan

LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and

PyTorch implementation of normalizing flow models

CompressAI CompressAI (compress-ay) is a PyTorch library and evaluation platform for end-to-end compression research. CompressAI currently provides: c

A library for researching neural networks compression and acceleration methods.

Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition Project Page | Video | Paper Implementation for Neural-PIL. A novel method wh

Logical Neural Networks LNNs are a novel Neuro = symbolic framework designed to seamlessly provide key properties of both neural nets (learning) and s

Neural Fluid Fields This is a small library for doing fluid simulation with neural fields. Check out our review paper, Neural Fields in Visual Computi

Light Field Networks Project Page | Paper | Data | Pretrained Models Vincent Sitzmann*, Semon Rezchikov*, William Freeman, Joshua Tenenbaum, Frédo Dur

Maximum Likelihood Training of Score-Based Diffusion Models This repo contains the official implementation for the paper Maximum Likelihood Training o

Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be

A modular PyTorch library for optical flow estimation using neural networks

i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery This is a public code repository for the publication: i-SpaSP: Structured Neural Pruning

AKGNN The source code for Adaptive Kernel Graph Neural Network at AAAI2022. Please cite our paper if you think our work is helpful to you: @inproceedi

SeBoW: Self-Born Wiring for neural trees(PaddlePaddle version) This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural

Neural Descriptor Fields (NDF) PyTorch implementation for training continuous 3D neural fields to represent dense correspondence across objects, and u

Fourier Feature Networks and Neural Volume Rendering This repository is a companion to a lecture given at the University of Cambridge Engineering Depa

Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be

DiffMG This repository contains the code for our KDD 2021 Research Track paper: DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neura

Neural Flows: Efficient Alternative to Neural ODEs [arxiv] TL;DR: We directly model the neural ODE solutions with neural flows, which is much faster a

Papers search Neural search engine for ML papers. Demo Usage is simple: input an abstract, get the matching papers. The following demo also showcases

AdvRush Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21) Environmental Set-up Python == 3.6.12, PyTorch =

LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and

CRGNN Paper ： Improving the Training of Graph Neural Networks with Consistency Regularization Environments Implementing environment: GeForce RTX™ 3090

Pairwise Learning for Neural Link Prediction for OGB (PLNLP-OGB) This repository provides evaluation codes of PLNLP for OGB link property prediction t

LitMatter A template for rapid experimentation and scaling deep learning models on molecular and crystal graphs. How to use Clone this repository and

yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re

This is code of RID-Noise. Reproduce RID-Noise Results Toy tasks Please refer to the notebook ridnoise.ipynb to view experiments on three toy tasks. B

Neural Activation Coding This repository contains the code for the paper "Unsupervised Representation Learning via Neural Activation Coding" published

CCasGNN A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics,

Graph Neural Controlled Differential Equations for Traffic Forecasting Setup Python environment for STG-NCDE Install python environment $ conda env cr

NL-CSNet-Pytorch Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021. Note: this repo only shows the strategy of

CRGNN Paper ： Improving the Training of Graph Neural Networks with Consistency Regularization Environments Implementing environment: GeForce RTX™ 3090

Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks Work accepted at NeurIPS'21 [paper, video]. If you use this code in

Transformer-in-Transformer An Implementation of the Transformer in Transformer paper by Han et al. for image classification, attention inside local pa

OCT-GAN: Neural ODE-based Conditional Tabular GANs (OCT-GAN) Code for reproducing the experiments in the paper: Jayoung Kim*, Jinsung Jeon*, Jaehoon L

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

Papers about explainability of GNNs

:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

neural-style An implementation of neural style in TensorFlow. This implementation is a lot simpler than a lot of the other ones out there, thanks to T

Neural Turing Machine in Tensorflow Tensorflow implementation of Neural Turing Machine. This implementation uses an LSTM controller. NTM models with m

Deep Q&A Table of Contents Presentation Installation Running Chatbot Web interface Results Pretrained model Improvements Upgrade Presentation This wor

#colornet Neural Network to colorize grayscale images Results Grayscale Prediction Ground Truth Eiji K used colornet for anime colorization Sources Au

Neural Caption Generator with Attention Tensorflow implementation of "Show

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convo

Character-Aware Neural Language Models Tensorflow implementation of Character-Aware Neural Language Models. The original code of author can be found h

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

tf_cnnvis A blog post describing the library: https://medium.com/@falaktheoptimist/want-to-look-inside-your-cnn-we-have-just-the-right-tool-for-you-ad

TensorFlow implementation of 3D Convolutional Neural Networks for Speaker Verification - Official Project Page - Pytorch Implementation This repositor

Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks - Official Project Page This repository contains the code develope

Hierarchical Attentive Recurrent Tracking This is an official Tensorflow implementation of single object tracking in videos by using hierarchical atte

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models This is an unofficial Tensorflow implementation of Attend, Infear, Repeat (AIR)

MusicGenreClassification Academic research in the field of Deep Learning (Deep Neural Networks) and Sound Processing, Tel Aviv University. Featured in

**This project is no longer active. Please check out TensorFlow.js.** The Keras.js demos still work but is no longer updated. Run Keras models in the

A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose Paper | Website | Data A-NeRF: Articulated Neural Radiance F

Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

NL-CSNet-Pytorch Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021. Note: this repo only shows the strategy of

Training Structured Neural Networks Through Manifold Identification and Variance Reduction This repository is a pytorch implementation of the Regulari

License The majority of VocBench is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Wavenet, Para

Steerable discovery of neural audio effects Christian J. Steinmetz and Joshua D. Reiss Abstract Applications of deep learning for audio effects often

pytorch_clip_guided_loss: Pytorch implementation of the CLIP guided loss for Text-To-Image, Image-To-Image, or Image-To-Text generation. A simple libr

Graph Posterior Network This is the official code repository to the paper Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classifica

Alternatives to Deep Neural Networks for Function Approximations in Finance Code companion repo Overview This is a repository of Python code to go wit

Spiking Neural Network for Computer Vision using SpikingJelly framework and Pytorch-Lightning

CAPGNN Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning" Paper URL: https://ar

Fast Neural Representations for Direct Volume Rendering Sebastian Weiss, Philipp Hermüller, Rüdiger Westermann This repository contains the code and s

UniNAS A highly modular PyTorch framework with a focus on Neural Architecture Search (NAS). under development (which happens mostly on our internal Gi

TC-GNN (Running Sparse GNN on Dense Tensor Core on Ampere GPU) Cite this project and paper. @inproceedings{TC-GNN, title={TC-GNN: Accelerating Spars

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural ne

Decoupled Spatial-Temporal Graph Neural Networks Code for our paper: Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.

ZH-EN NMT Chinese to English Neural Machine Translation This project is inspired by Stanford's CS224N NMT Project Dataset used in this project: News C

NLP-Pytorch-Assignment An assignment from my grad-level data mining course (before I started personal projects) demonstrating some experience with NLP

ELimNet ELimNet: Eliminating Layers in a Neural Network Pretrained with Large Dataset for Downstream Task Removed top layers from pretrained Efficient

DiffWave DiffWave is a fast, high-quality neural vocoder and waveform synthesizer. It starts with Gaussian noise and converts it into speech via itera

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

Neural HMMs are all you need (for high-quality attention-free TTS) Shivam Mehta, Éva Székely, Jonas Beskow, and Gustav Eje Henter This is the official