Custom Implementation of Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Official Repository https://github.com/imankgoyal/NonDeepNetworks Overview: Depth is the hallmark of DNNs. But more depth means more sequential computation and higher latency. This begs the question — is it possible to build high-performing “non-deep” neural networks? We show that it is. We show, for the first time, that a network with a depth of just 12 can achieve top-1 accuracy over 80% on ImageNet, 96% on CIFAR10, and […]

Interesting and reproducible research works should be conserved.Unfortunately, too many model repositories provide different ways to use.It is an obstacle for people who just want to use them right away, especially for those without luxury to (re)train big deep learning models.This repository aims to wrap a collection of deep neural network models into a simple and consistent API. Installation pip install git+https://github.com/ariaghora/ezpznet It depends mainly on pytorch and torchvision. Pretrained weights Each model will download its own pretrained weight (once […]

This includes an original implementation of “MetaICL: Learning to Learn In Context” by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi. ✨ Check out our demo at qa.cs.washington.edu:2021! ✨ This README is mainly for how to reproduce MetaICL and Channel MetaICL in the paper, but also describe how to reproduce our baselines, including Multi-task zero-shot and various raw LM methods.All methods used in the paper are available in this repo (please see the below table). For any questions about […]

Code for the paper TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks by Yu Li, Min Li, Qiuxia Lai, Yannan Liu, and Qiang Xu. If you use this code, or development from it, please cite our paper: @article{yu2021testrank, title={TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks}, author={Yu Li, Min Li, Qiuxia Lai, Yannan Liu, and Qiang Xu}, journal={NeurIPS}, year={2021} } 1. Setup Install dependencies conda env create -f environment.yml Please    

What is DELTA? DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper. What can DELTA do? DELTA has been used for developing several state-of-the-art algorithms for publications and    

Projection of a central sulcus probability map using the RF-ANTs approach (right hemisphere shown). This is a Python implementation of Wu et al (2018)’s registration fusion methods to project MRI data from standard volumetric coordinates, either MNI152 or Colin27, to Freesurfer’s fsaverage. This tool already available in the original MATLAB-based version provided by Wu et al, which works well out of the box. However, given Python’s increasing stake in neuroimaging analysis, a pure Python version may be useful. A huge […]

We strongly believe in open and reproducible deep learning research. Our goal is to implement an open-source medical image segmentation library of state of the art 3D deep neural networks in PyTorch. We also implemented a bunch of data loaders of the most common medical image datasets. This project started as an MSc Thesis and is currently under further development. Although this work was initially focused on 3D multi-modal brain MRI segmentation we are slowly adding    

Overview NVIDIA Kaolin library provides a PyTorch API for working with a variety of 3D representations and includes a growing collection of GPU-optimized operations such as modular differentiable rendering, fast conversions between representations, data loading, 3D checkpoints and more. Kaolin library is part of a larger suite of tools for 3D deep learning research. For example, the Omniverse Kaolin App will allow interactive visualization of 3D checkpoints. To find out more about the Kaolin ecosystem, visit the NVIDIA Kaolin Dev […]