Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Berkeley Website and video: https://alexyu.net/plenoxels arXiv: https://arxiv.org/abs/2112.05131 Note: This is a preliminary release. We have not carefully tested everything,but feel that it would be better to first put the code out there. Also, despite the name, it’s not strictly intended to be a successor of svox Citation: @misc{yu2021plenoxels, title={Plenoxels: Radiance Fields without Neural Networks}, author={{Alex Yu and Sara Fridovich-Keil} and Matthew Tancik and Qinhong Chen and Benjamin […]

The purpose of this project is to emulate arbitrary sounds with FM synthesis, where the parameters of the FM synth are learned by optimization. This idea was conceived and implemented during the Neural Audio Synthesis Hackathon 2021. Thanks to Ben Hayes for organizing the workshop and to Mia Chiquier for pointing me towards SIREN! Architecture Please refer to FMNet and Envelope in synth.py for the actual architectural details. This model takes as input a list of time steps t_1, t_2, […]

Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Berkeley Website and video: https://alexyu.net/plenoxels arXiv: https://arxiv.org/abs/2112.05131 Note: This JAX implementation is intended to be high-level and user-serviceable, but is much slower (roughly 1 hour per epoch) than the CUDA implementation https://github.com/sxyu/svox2 (roughly 1 minute per epoch), and there is not perfect feature alignment between the two versions. This JAX version can likely be sped up significantly, and we may push performance improvements and extra features in […]

This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies. This codebase contains a re-implementation of Nerfies using JAX, building on JaxNeRF. We have been careful to match implementation details and have reproduced the original results presented in the paper. Demo We provide an easy-to-get-started demo using Google Colab! These Colabs will allow you to train a basic version of our method using Cloud TPUs (or GPUs) on Google Colab. Note that due to limited compute resources available, these […]

A pairs trade relies on the fact that – if the correlation between two stocks is atleast 95% they follow a similar trend. But due to temporary market conditions like the announcement of results in a company or due to mishaps in another company, the stock direction of that company may deviate from the mean value. Given the striking similarities between the two banks used for this capstone, a change in the business environment of one bank, affects the paired […]

IMDB-text-classification-sentiment-prediction Easy to start.Use deep nerual network to predict the sentiment of movie review.Various methods, word2vec, tf-idf and df to generate text vectors.Various models including lstm and cov1d.Achieve f1 score 92. Run python text_wash.py in the data_process folder Run python data_process_word2vec.py, python data_process_tf_manual.py, python data_process_tfidf.py under the data_process folder, this step takes a long time Use bash main.sh word2vec to perform text classification training using word2vec word vectors as training data, and use bash view.sh word2vec to view the redirected […]

[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 […]

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 […]

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 […]

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 […]