Pytorch implementation of “Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling” (https://arxiv.org/pdf/1609.01454.pdf) Intent prediction and slot filling are performed in two branches based on Encoder-Decoder model. dataset (Atis) You can get data from here Requirements Train python3 train.py –data_path ‘your data path e.g. ./data/atis-2.train.w-intent.iob’ Result GitHub https://github.com/DSKSD/RNN-for-Joint-NLU    

EpyNN is written in pure Python/NumPy. If you use EpyNN in academia, please cite: Malard F., Danner L., Rouzies E., Meyer J. G., Lescop E., Olivier-Van Stichelen S. EpyNN: Educational python for Neural Networks, 2021, Submitted. Documentation Please visit https://epynn.net/ for extensive documentation. Purpose EpyNN is intended for teachers, students, scientists, or more generally anyone with minimal skills in Python programming who wish to understand and build from basic implementations of Neural Network architectures. Although EpyNN can be used for […]

The VeriNet toolkit is a state-of-the-art sound and complete symbolic interval propagation based toolkit for verification of neural networks. VeriNet won second place overall and was the most performing among toolkits not using GPUs in the 2nd international verification of neural networks competition. VeriNet is devloped at the Verification of Autonomous Systems (VAS) group, Imperial College London. Relevant Publications. VeriNet is developed as part of the following publications: Efficient Neural Network Verification via Adaptive Refinement and Adversarial Search DEEPSPLIT: An […]

GitHub – salesforce/nonauto-nmt: PyTorch Implementation of “Non-Autoregressive Neural Machine Translation” PyTorch Implementation of “Non-Autoregressive Neural Machine Translation” – GitHub – salesforce/nonauto-nmt: PyTorch Implementation of “Non-Autoregressive Neural Machine Translation&…    

This repository implements a capsule model IntentCapsNet-ZSL on the SNIPS-NLU dataset in Python 3 with PyTorch, first introduced in the paper Zero-shot User Intent Detection via Capsule Neural Networks. The code aims to follow PyTorch best practices, using torch instead of numpy where possible, and using .cuda() for GPU computation. Feel free to contribute via pull requests. Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu. Zero-shot User Intent Detection via Capsule Neural Networks. In Proceedings of the […]

This repo provides PyTorch Implementation of MSG-Net (ours) and Neural Style (Gatys et al. CVPR 2016), which has been included by ModelDepot. We also provide Torch implementation and MXNet implementation. Tabe of content MSG-Net Multi-style Generative Network for Real-time Transfer [arXiv] [project] Hang Zhang, Kristin Dana @article{zhang2017multistyle, title={Multi-style Generative Network for Real-time Transfer}, author={Zhang, Hang and Dana, Kristin}, journal={arXiv preprint arXiv:1703.06953}, year={2017} } Stylize Images Using Pre-trained MSG-Net Download the pre-trained model

[arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no reference source code so this is a non-official version. 2. Enveriments python 3.7+ pytorch 1.7.1 pillow apex opencv-python You can see this repo to find how to install the apex 3. DataSet Trainig /data/home/imagenet/train/xxx.jpeg, 0 /data/home/imagenet/train/xxx.jpeg, 1 … /data/home/imagenet/train/xxx.jpeg, 999 Testing /data/home/imagenet/test/xxx.jpeg, 0 /data/home/imagenet/test/xxx.jpeg, 1 … /data/home/imagenet/test/xxx.jpeg, 999 4. Training & Inference

Unofficial pytorch implementation of paper “One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing” Driving | FOMM | Ours: Free-View: Train: python run.py –config config/vox-256.yaml –device_ids 0,1,2,3,4,5,6,7 Demo: python demo.py –config config/vox-256.yaml –checkpoint path/to/checkpoint –source_image path/to/source –driving_video path/to/driving –relative –adapt_scale –find_best_frame free-view (e.g. yaw=20, pitch=roll=0): python demo.py –config config/vox-256.yaml –checkpoint    

A scikit-learn compatible neural network library that wraps PyTorch. Resources Examples To see more elaborate examples, look here. import numpy as np from sklearn.datasets import make_classification from torch import nn from skorch import NeuralNetClassifier X, y = make_classification(1000, 20, n_informative=10, random_state=0) X = X.astype(np.float32)    

Train longer, generalize better – Big batch training This is a code repository used to generate the results appearing in “Train longer, generalize better: closing the generalization gap in large batch training of neural networks” By Elad Hoffer, Itay Hubara and Daniel Soudry. It is based off convNet.pytorch with some helpful options such as: Training on several datasets Complete logging of trained experiment Graph visualization of the training/validation loss and accuracy Definition of preprocessing and optimization regime for each model […]