Python Differentiable-neural-computers Resources

Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

Energy_Output_Predictor Artificial Neural network regression model to predict the energy output in a combined cycle power plant. Abstract Energy outpu

This project is a PyTorch implementation of Neural Radiance Fields (NeRF) for reproduction of results whilst running at a faster speed.

Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated. This engine can later be used for downstream tasks in NLP such as Q&A, summarization, generation, and natural language understanding (NLU).

Differentiable Wavetable Synthesis

PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN) This code implements the skeleton-based action segmentation MS-GCN model from Autom

SBINN: Systems-biology informed neural network The source code for the paper M. Daneker, Z. Zhang, G. E. Karniadakis, & L. Lu. Systems biology: Identi

This package requires jax, tensorflow, and numpy. Either tensorflow or scikit-learn can be used for loading data. To run in a nix-shell with required

A mini-scale reproduction code of the AlphaStar program. Note: the original AlphaStar is the AI proposed by DeepMind to play StarCraft II.

FewBit FewBit — a library for memory efficient training of large neural networks. Its efficiency originates from storage optimizations applied to back

DNA sequence classification by Deep Neural Network: Project Overview worked on the DNA sequence classification problem where the input is the DNA sequ

CAP-Classification-CRNN A deep learning model based on Inception modules paired with gated recurrent units (GRU) for the classification of CAP phases

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks Under construction. Description Code for Phase diagram of S

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting Recent progress in neural forecasting instigated significant improvements in the

DiffStride is a pooling layer with learnable strides. Unlike strided convolutions, average pooling or max-pooling that require cross-validating stride values at each layer, DiffStride can be initialized with an arbitrary value at each layer (e.g. (2, 2) and during training its strides will be optimized for the task at hand.

Differentiable Simulation of Soft Multi-body Systems Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin [Paper] [Code] Updates The C++ backend s

Semantic-NeRF: Semantic Neural Radiance Fields Project Page | Video | Paper | Data In-Place Scene Labelling and Understanding with Implicit Scene Repr

TDY-CNN for Text-Independent Speaker Verification Official implementation of Temporal Dynamic Convolutional Neural Network for Text-Independent Speake

Siren: Implicit Neural Representations with Periodic Activation Functions The unofficial Tensorflow 2 implementation of the paper Implicit Neural Repr

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.

Detect and Classify Brain Tumor using CNN. A system performing detection and classification by using Deep Learning Algorithms using Convolution-Neural Network (CNN).

ssnt-loss ℹ️ This is a WIP project. the implementation is still being tested. A pure PyTorch implementation of the loss described in "Online Segment t

Classification using Deep Learning Requirements PyTorch version = 1.9.1+cu111 Python version = 3.8.10 PyTorch-Lightning version = 1.4.9 Huggingface

This repository contains the official PyTorch implementation of the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015

AvatarGAN ✨ Generate Cartoon Images using DC-GAN Deep Convolutional GAN is a generative adversarial network architecture. It uses a couple of guidelin

The unified machine learning framework, enabling framework-agnostic functions, layers and libraries. Contents Overview In a Nutshell Where Next? Overv

keras-semantic-segmentation-example Example of semantic segmentation in Keras Single class example: Generated data: random ellipse with random color o

RuCLIPtiny Zero-shot image classification model for Russian language RuCLIP tiny (Russian Contrastive Language–Image Pretraining) is a neural network

This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternative libraries that can be used for this purpose, one of which is the PyTorch library.

Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

This project is a convolutional neural network (CNN) that analyzes self-generated images in a variety of languages to find etymological similarities. Specifically, the goal is to prove that computer vision can be used to identify cognates known to exist, and perhaps lead linguists to evidence of unknown cognates.

Iterative refinement graph neural network for antibody sequence-structure co-des

neural-modular Simple codebase for flexible neural net training. Allows for seamless exchange of models, dataset, and optimizers. Uses hydra for confi

Equivariant Subgraph Aggregation Networks (ESAN) This repository contains the official code of the paper Equivariant Subgraph Aggregation Networks (IC

traiNNer traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation to

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields Paper | Supplementary | Video | Poster If you find our code or paper useful, please

DIR-GNN "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Contrastive Learning for Neural Topic Model This repository contains the impleme

Random Forest classifier for neural subtypes extracted from extracellular recordings from human brain organoids.

Cite our paper if you find this project useful https://www.ijariit.com/manuscripts/v7i4/V7I4-1139.pdf Abstract Image processing technology is used in

Our product DrLeaf which not only makes the work easier but also reduces the effort and expenditure of the farmer to identify the disease and its treatment methods. We have to upload the image of an affected plant’s leaf through our website and our plant disease prediction model predicts and returns the disease name. And along with the disease name, we also provide the best suitable methods to cure the disease.

NLP - Movie review sentiment analysis The colab notebook contains the code for building a LSTM Recurrent Neural Network that gives 87-88% accuracy on

GNN_PPI Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction". Lear

1st place solution in CCF BDCI 2021 ULSEG challenge This is the source code of the 1st place solution for ultrasound image angioma segmentation task (

machine-learning-with-graphs My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs Course materials can be

Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set This is the repository for the Deep Learning proje

Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 The repository provides 100+ Pap

DART Implementation for ICLR2022 paper Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners. Environment [email protected] Use pi

NANSY: Unofficial Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations Notice Papers' D

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021 [Projec

Self-supervised learning Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive loss

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu

CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels Accurate pressure drop estimat

JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation This the repository for this paper. Find extensions of this w

GAN-based Matrix Factorization for Recommender Systems This repository contains the datasets' splits, the source code of the experiments and their res

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration Stefan Abi-Karam*, Yuqi He*, Rishov Sarkar*, Lakshmi Sathidevi, Zihang Qiao, Co

Approximate Multiplier by HEAM What's HEAM? HEAM is a general optimization method to generate high-efficiency approximate multipliers for specific app

Dual-Contrastive-Learning A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation". Y

Fast Differentiable Matrix Sqrt Root Geometric Interpretation of Matrix Square Root and Inverse Square Root This repository constains the official Pyt

On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape

Leaf: Multiple-Choice Question Generation Easy to use and understand multiple-choice question generation algorithm using T5 Transformers. The applicat

Neural Architecture Search for Spiking Neural Networks Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks] (https

Interpolation-ResNet Invited paper for WSA 2021, called 'Low Complexity Channel estimation with Neural Network Solutions'. Low complexity residual con

GNDC For submission to IEEE TKDE. Overview Here we provide the implementation of GND-Nets (Graph Neural Diffusion Networks) in TensorFlow. The reposit

Unsupervised Audio Source Separation Using Differentiable Parametric Source Models This is the source code for the experiments related to the paper Un

Code for Multimodal Neural SLAM for Interactive Instruction Following Code structure The code is adapted from E.T. and most training as well as data p

This is part of the code to reproduce the results of the paper Do Neural Networks for Segmentation Understand Insideness? [pdf] by K. Villalobos (*),

Explanatory Learning This is the official repository for "Explanatory Learning: Beyond Empiricism in Neural Networks". Datasets Download the datasets

Post-training Quantization for Neural Networks with Provable Guarantees Authors: Jinjie Zhang ([email protected]), Yixuan Zhou ([email protected]) and Ray

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search DropNAS, a grouped operation dropout method for one-level DARTS, with better

Audio Source Separation is the process of separating a mixture into isolated sounds from individual sources (e.g. just the lead vocals).

This is a Kaggle competition in which we have to identify if the given lesion image is malignant or not for Melanoma which is a type of skin cancer.

What can linearized neural networks actually say about generalization? This is the source code to reproduce the experiments of the NeurIPS 2021 paper

GreaseLM: Graph REASoning Enhanced Language Models This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language

Neural Hash Encoding This is a work in progress reimplementation of Instant Neural Graphics Primitives Currently this can train an implicit representa

Neural Tangent Generalization Attacks (NTGA) ICML 2021 Video | Paper | Quickstart | Results | Unlearnable Datasets | Competitions | Citation Overview

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

The current workflow simulates brain functional connectivity (FC) from structural connectivity (SC) with a neural mass model. Gradient descent is applied to optimize the parameters in the neural mass model.

Neural Network Just a basic Neural Network module Usage Example Importing Module

Predicting Auction Sale Price using the kaggle bulldozer auction sales data: Modeling with Ensembles vs Neural Network The performances of tree ensemb

CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair

A library for benchmarking, developing and deploying deep learning anomaly detection algorithms Key Features • Getting Started • Docs • License Introd

This repo supplements our Survey on Transformers in Medical Imaging Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat,

SegPC-2021 This is the official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation by

Lung Segmentation (2D) Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images. Demo See the application of the

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

ild-cnn This is supplementary material for the manuscript: "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neur

Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA). Master's thesis documents. Bibliography, experiments and reports.

Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

This repo has been deprecated because whole things are re-implemented by using Chainer and I did refactoring for many codes. So please check this newe

This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset and my original methods that are publi

self-driving-car In this repository I will share the source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree. Hope this might

NNProject - DeepMask This is a Keras-based Python implementation of DeepMask- a complex deep neural network for learning object segmentation masks. Th

Segment axon and myelin from microscopy data using deep learning. Written in Python. Using the TensorFlow framework. Based on a convolutional neural network architecture. Pixels are classified as either axon, myelin or background.

[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper) Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang PDF:

Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline. The pipeline accepts english text as input and returns the French translation.

Generative design of breakwaters usign deep convolutional neural network as a surrogate model This repository contains the code for the paper "Generat

Example-CNN-Project This is a simple project showing how to implement deep neural networks using CNN algorithm. The dataset is taken from this link: h

Disaster Classificiation using CNNs bunnysaini/Disaster-Classificiation Goal To prepare an image processing model to classify the type of disaster bas