Neural Radiance Flow for 4D View Synthesis and Video Processing

[ICCV’21] Neural Radiance Flow for 4D View Synthesis and Video Processing Datasets The pouring dataset used for experiments can be download here and the iGibson dataset used inexperiments can be downloaded here Pouring Dataset Please download and extract each dataset at data/nerf_synthetic/. Please use the following command to train python run_nerf.py –config=configs/pour_baseline.txt After running model for 200,000 iterations, move the model to a new folder pour_dataset_flow and then use the following commandto train with flow consistency python run_nerf.py –config=configs/pour_baseline_flow.txt Gibson […]

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Fast Coreference Resolution in spaCy with Neural Networks

NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy’s NLP pipeline and extensible to new training datasets. For a brief introduction to coreference resolution and NeuralCoref, please refer to our blog post. NeuralCoref is written in Python/Cython and comes with a pre-trained statistical model for English only. NeuralCoref is accompanied by a visualization client NeuralCoref-Viz, a web interface powered by a REST server that can […]

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An Open-Source Package for Neural Relation Extraction (NRE)

We have a DEMO website (http://opennre.thunlp.ai/). Try it out! OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement relation extraction models. This package is designed for the following groups: New to relation extraction: We have hand-by-hand tutorials and detailed documents that can not only enable you to use relation extraction tools, but also help you better understand the research progress in this field. Developers: Our easy-to-use interface and high-performance implementation can acclerate your deployment in […]

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An assignment on creating a minimalist neural network toolkit for CS11-747

by Graham Neubig, Zhisong Zhang, and Divyansh Kaushik This is an exercise in developing a minimalist neural network toolkit for NLP, part of Carnegie Mellon University’s CS11-747: Neural Networks for NLP. The most important files it contains are the following: minnn.py: This is what you’ll need to implement. It implements a very minimalist version of a dynamic neural network toolkit (like PyTorch or Dynet). Some code is provided, but important functionality is not included. classifier.py: training code for a Deep […]

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Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

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    

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Educational python for Neural Networks

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 […]

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The VeriNet toolkit for verification of neural networks

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 […]

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GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

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 […]

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Neural Style and MSG-Net in PyTorch

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

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