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Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

google-research/bert NAACL 2019

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

Deep Residual Learning for Image Recognition

tensorflow/models CVPR 2016

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

SSD: Single Shot MultiBox Detector

weiliu89/caffe 8 Dec 2015

Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference.

Proximal Policy Optimization Algorithms

labmlai/annotated_deep_learning_paper_implementations 20 Jul 2017

We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.

Rethinking the Inception Architecture for Computer Vision

tensorflow/models CVPR 2016

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.

Graph Attention Networks

PetarV-/GAT ICLR 2018

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

tensorflow/models 23 Feb 2016

Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network.

Efficient Estimation of Word Representations in Vector Space

mindspore-courses/DeepNLP-models-MindSpore 16 Jan 2013

We propose two novel model architectures for computing continuous vector representations of words from very large data sets.

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

tensorflow/models ECCV 2018

The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

tensorflow/models 11 Feb 2015

Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change.