Articles About Deep Learning

TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks

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    

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A deep learning based natural language and speech processing platform

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    

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Python implementation of Wu et al (2018)’s registration fusion

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 […]

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A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

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    

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A PyTorch Library for Accelerating 3D Deep Learning Research

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 […]

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One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective

ArXiv (pdf) Official pytorch implementation of the paper: “One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective” NeurIPS 2021 Released on September 29, 2021 This paper proposes a novel deep hashing model with only a single learning objective which is a simplification from most state of the art papers generally use lots of losses and regularizer. Specifically, it maximizes the cosine similarity between the continuous codes and their corresponding binary orthogonal codes to ensure both […]

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State-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch. Design Architecture As shown below, each pretraining/fine-tuning model is decomposed into two modules: Encoder and Head. Encoder Encoder has Embedding and Backbone. Embedding makes continuous/categorical features tokenized or simply normalized. Backbone processes the tokenized features. Pretraining/Fine-tuning Head Pretraining/Fine-tuning Head uses Encoder module for training. Implemented Methods Available Modules Encoder – Embedding FeatureEmbedding TabTransformerEmbedding Encoder – Backbone MLPBackbone FTTransformerBackbone SAINTBackbone Model – Head Model – Pretraining […]

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Deep learning with dynamic computation graphs in TensorFlow

TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the structure of the computation graph depends on the structure of the input data. For example, this model implements TreeLSTMs for sentiment analysis on parse trees of arbitrary shape/size/depth. Fold implements dynamic batching. Batches of arbitrarily shaped computation graphs are transformed to produce a static computation graph. This graph has the same structure regardless of what input it receives, and can be executed efficiently by TensorFlow. […]

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