AIGeeks

Papers

Natural Language Processing

Distributed Word Representations

• 2017-11
  • Faruqui and Dyer – 2014 – Improving vector space word representations using multilingual correlation [pdf] [note]
  • Maaten and Hinton – 2008 – Visualizing data using t-SNE [pdf] [pdf (annotated)] [note]
  • Ling et al. – 2015 – Finding function in form: Compositional character models for open vocabulary word representation [pdf] [pdf (annotated)] [note]
  • Bojanowski et al. – 2016 – Enriching word vectors with subword information [pdf] [pdf (annotated)] [note]
• 2017-12
  • Bengio and Senécal – 2003 – Quick Training of Probabilistic Neural Nets by Importance Sampling [pdf] [pdf(annotated)] [note]
• references
  • word2vec(tensorflow)
  • subword-based word vector
  • Chinese Word Vectors 中文词向量
  • Tencent AI Lab Embedding Corpus for Over 8 Million Chinese Words and Phrases

Distributed Sentence Representations

• 2017-11
  • Le and Mikolov – 2014 – Distributed representations of sentences and documents [pdf] [pdf (annotated)] [note]
• 2018-12
  • Li and Hovy – 2014 – A Model of Coherence Based on Distributed Sentence Representation [pdf] [pdf (annotated)] [note]
  • Kiros et al. – 2015 – Skip-Thought Vectors [pdf] [pdf (annotated)] [note]
  • Hill et al. – 2016 – Learning Distributed Representations of Sentences from Unlabelled Data [pdf] [pdf (annotated)] [note]
  • Arora et al. – 2016 – A simple but tough-to-beat baseline for sentence embeddings [pdf] [pdf (annotated)] [note]
  • Pagliardini et al. – 2017 – Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features (sent2vec) [pdf] [pdf (annotated)] [note]
  • Logeswaran et al. – 2018 – An efficient framework for learning sentence representations (Quick-Thought Vectors) [pdf] [pdf (annotated)] [note]
• 2019-01
  • Wieting et al. – 2015 – Towards universal paraphrastic sentence embeddings [pdf] [pdf (annotated)] [note]
  • Adi et al. – 2016 – Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks [pdf] [pdf (annotated)] [note]
  • Conneau et al. – 2017 – Supervised Learning of Universal Sentence Representations from Natural Language Inference Data (InferSent) [pdf] [pdf (annotated)] [note]
  • Cer et al. – 2018 – Universal Sentence Encoder [pdf] [pdf (annotated)] [note]
• references
  • awesome-sentence-embedding: A curated list of pretrained sentence(and word) embedding models
  • SentEval: evaluation toolkit for sentence embeddings
  • doc2vec(gensim)
  • Skip-Thought Vectors
  • SIF(sentence embedding by Smooth Inverse Frequency weighting scheme)
  • Quick-Thought Vectors
  • sent2vec
  • InferSent

Entity Recognition

• 2018-10
  • Lample et al. – 2016 – Neural Architectures for Named Entity Recognition [pdf]
  • Ma and Hovy – 2016 – End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF [pdf]
  • Yang et al. – 2017 – Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks [pdf]
  • Peters et al. – 2017 – Semi-supervised sequence tagging with bidirectional language models [pdf]
  • Shang et al. – 2018 – Learning Named Entity Tagger using Domain-Specific Dictionary [pdf]
• references
  • ChineseNER (TensorFlow)
  • flair (PyTorch)

Language Model

• 2017-11
  • Bengio et al. – 2003 – A neural probabilistic language model [pdf]
  • Press and Wolf – 2016 – Using the output embedding to improve language model [pdf]
• 2019-02
  • Peters et al. – 2018- Deep contextualized word representations(ELMo) [pdf] [note]
  • Howard and Ruder – 2018 – Universal language model fine-tuning for text classification(ULMFit) [pdf]
  • Radford et al. – 2018 – Improving language understanding by generative pre-training [pdf]
  • Devlin et al. – 2018 – Bert: Pre-training of deep bidirectional transformers for language understanding [pdf]
• references
  • Blog:The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
  • ELMo
    • ELMo(AllenNLP)
    • Pre-trained ELMo Representations for Many Languages
  • Quick Start: Training an IMDb sentiment model with ULMFiT
  • finetune-transformer-lm: Code and model for the paper “Improving Language Understanding by Generative Pre-Training”
  • awesome-bert: bert nlp papers, applications and github resources ， BERT 相关论文和 github 项目

Machine Translation

• 2017-12
  • Oda et al. – 2017 – Neural Machine Translation via Binary Code Predict [pdf] [note]
  • Kalchbrenner et al. – 2016 – Neural machine translation in linear time [pdf] [pdf (annotated)] [note]
• 2018-05
  • Sutskever et al. – 2014 – Sequence to Sequence Learning with Neural Networks [pdf]
  • Cho et al. – 2014 – Learning Phrase Representations using RNN Encoder-Decoder for NMT [pdf]
  • Bahdanau et al. – 2014 – NMT by Jointly Learning to Align and Translate [pdf]
  • Luong et al. – 2015 – Effective Approaches to Attention-based NMT [pdf]
• 2018-06
  • Gehring et al. – 2017 – Convolutional sequence to sequence learning [pdf]
  • Vaswani et al. – 2017 – Attention is all you need [pdf] [note1:The Illustrated Transformer] [note2:The Annotated Transformer]
• references
  • OpenNMT-py (in PyTorch)
  • nmt (in TensorFlow)
  • MT-Reading-List

Question Answering

• 2018-03
  • Wang and Jiang. – 2016 – Machine Comprehension Using Match-LSTM and Answer Pointer [pdf]
  • Seo et al. – 2016 – Bidirectional Attention Flow for Machine Comprehension [pdf]
  • Cui et al. – 2016 – Attention-over-Attention Neural Networks for Reading Comprehension [pdf]
• 2018-04
  • Clark and Gardner. – 2017 – Simple and Effective Multi-Paragraph Reading Comprehension [pdf]
  • Wang et al. – 2017 – Gated Self-Matching Networks for Reading Comprehension and Question Answering [pdf]
  • Yu et al. – 2018 – QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension [pdf]
• references
  • DuReader
  • SQuAD
  • MS MARCO
  • 深度学习解决机器阅读理解任务的研究进展
  • RCPapers: Must-read papers on Machine Reading Comprehension

Recommendation Systems

• 2019-05
  • Rendle S. – 2010 – Factorization machines [pdf] [note]
  • Cheng et al. – 2016 – Wide & Deep Learning for Recommender Systems [pdf]
  • Guo et al. – 2017 – DeepFM: A Factorization-Machine based Neural Network for CTR Prediction [pdf]
  • He and Chua. – 2017 – Neural Factorization Machines for Sparse Predictive Analytics [pdf]

Relation Extraction

• 2018-08
  • Mintz et al. – 2009 – Distant supervision for relation extraction without labeled data [pdf]
  • Zeng et al. – 2015 – Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks [pdf]
  • Zhou et al. – 2016 – Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification [pdf]
  • Lin et al. – 2016 – Neural Relation Extraction with Selective Attention over Instances [pdf]
• 2018-09
  • Ji et al. – 2017 – Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions [pdf]
  • Levy et al. – 2017 – Zero-Shot Relation Extraction via Reading Comprehension [pdf]
• references
  • OpenNRE
  • NREPapers: Must-read papers on neural relation extraction (NRE)
  • awesome-relation-extraction

Sentences Matching

• 2017-12
  • Hu et al. – 2014 – Convolutional neural network architectures for Matching Natural Language Sentences [pdf] [pdf (annotated)] [note]
• 2018-07
  • Nie and Bansal – 2017 – Shortcut-Stacked Sentence Encoders for Multi-Domain Inference [pdf] [note]
  • Wang et al. – 2017 – Bilateral Multi-Perspective Matching for Natural Language Sentences [pdf] [note]
  • Tay et al. – 2017 – A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference [pdf]
  • Chen et al. – 2017 – Enhanced LSTM for Natural Language Inference [pdf] [note]
  • Ghaeini et al. – 2018 – DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference [pdf]
• references
  • The Stanford Natural Language Inference (SNLI) Corpus

Text Classification

• 2017-09
  • Joulin et al. – 2016 – Bag of tricks for efficient text classification [pdf] [pdf (annotated)] [note]
• 2017-10
  • Kim – 2014 – Convolutional neural networks for sentence classification [pdf] [pdf (annotated)] [note]
  • Zhang and Wallace – 2015 – A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification [pdf] [pdf (annotated)] [note]
  • Zhang et al. – 2015 – Character-level convolutional networks for text classification [pdf] [pdf (annotated)] [note]
  • Lai et al. – 2015 – Recurrent Convolutional Neural Networks for Text Classification [pdf] [pdf (annotated)] [note]
  • Yang et al. – 2016 – Hierarchical attention networks for document classification [pdf]
• 2017-11
  • Iyyer et al. – 2015 – Deep unordered composition rivals syntactic methods for Text Classification [pdf] [pdf (annotated)] [note]
• 2019-04 (Aspect level sentiment classification)
  • Wang et al. – 2016 – Attention-based LSTM for aspect-level sentiment classification [pdf]
  • Tang et al. – 2016 – Aspect level sentiment classification with deep memory network [pdf]
  • Chen et al. – 2017 – Recurrent Attention Network on Memory for Aspect Sentiment Analysis [pdf]
  • Xue and Li – 2018 – Aspect Based Sentiment Analysis with Gated Convolutional Networks [pdf]

Source

Computer Vision

• [R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | [CVPR’ 14] |[pdf] [official code - caffe]
• [OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | [ICLR’ 14] |[pdf] [official code - torch]
• [MultiBox] Scalable Object Detection using Deep Neural Networks | [CVPR’ 14] |[pdf]
• [SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | [ECCV’ 14] |[pdf] [official code - caffe] [unofficial code - keras] [unofficial code - tensorflow]

• Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | [CVPR’ 15] |[pdf] [official code - matlab]
• [MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | [ICCV’ 15] |[pdf] [official code - caffe]
• [DeepBox] DeepBox: Learning Objectness with Convolutional Networks | [ICCV’ 15] |[pdf] [official code - caffe]
• [AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | [ICCV’ 15] |[pdf]
• [Fast R-CNN] Fast R-CNN | [ICCV’ 15] |[pdf] [official code - caffe]
• [DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | [ICCV’ 15] |[pdf] [official code - matconvnet]
• [Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | [NIPS’ 15] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch]

• [YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | [CVPR’ 16] |[pdf] [official code - c]
• [G-CNN] G-CNN: an Iterative Grid Based Object Detector | [CVPR’ 16] |[pdf]
• [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | [CVPR’ 16] |[pdf]
• [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | [CVPR’ 16] |[pdf]
• [HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | [CVPR’ 16] |[pdf]
• [OHEM] Training Region-based Object Detectors with Online Hard Example Mining | [CVPR’ 16] |[pdf] [official code - caffe]
• [CRAPF] CRAFT Objects from Images | [CVPR’ 16] |[pdf] [official code - caffe]
• [MPN] A MultiPath Network for Object Detection | [BMVC’ 16] |[pdf] [official code - torch]
• [SSD] SSD: Single Shot MultiBox Detector | [ECCV’ 16] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch]
• [GBDNet] Crafting GBD-Net for Object Detection | [ECCV’ 16] |[pdf] [official code - caffe]
• [CPF] Contextual Priming and Feedback for Faster R-CNN | [ECCV’ 16] |[pdf]
• [MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | [ECCV’ 16] |[pdf] [official code - caffe]
• [R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | [NIPS’ 16] |[pdf] [official code - caffe] [unofficial code - caffe]
• [PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | [NIPSW’ 16] |[pdf] [official code - caffe]
• [DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | [PAMI’ 16] |[pdf]
• [NoC] Object Detection Networks on Convolutional Feature Maps | [TPAMI’ 16] |[pdf]

• [DSSD] DSSD : Deconvolutional Single Shot Detector | [arXiv’ 17] |[pdf] [official code - caffe]
• [TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | [CVPR’ 17] |[pdf]
• [FPN] Feature Pyramid Networks for Object Detection | [CVPR’ 17] |[pdf] [unofficial code - caffe]
• [YOLO v2] YOLO9000: Better, Faster, Stronger | [CVPR’ 17] |[pdf] [official code - c] [unofficial code - caffe] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch]
• [RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | [CVPR’ 17] |[pdf] [official code - caffe] [unofficial code - tensorflow]
• [RSA] Recurrent Scale Approximation for Object Detection in CNN | | [ICCV’ 17] |[pdf] [official code - caffe]
• [DCN] Deformable Convolutional Networks | [ICCV’ 17] |[pdf] [official code - mxnet] [unofficial code - tensorflow] [unofficial code - pytorch]
• [DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | [ICCV’ 17] |[pdf] [official code - theano]
• [CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | [ICCV’ 17] |[pdf] [official code - caffe]
• [RetinaNet] Focal Loss for Dense Object Detection | [ICCV’ 17] |[pdf] [official code - keras] [unofficial code - pytorch] [unofficial code - mxnet] [unofficial code - tensorflow]
• [Mask R-CNN] Mask R-CNN | [ICCV’ 17] |[pdf] [official code - caffe2] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch]
• [DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | [ICCV’ 17] |[pdf] [official code - caffe] [unofficial code - pytorch]
• [SMN] Spatial Memory for Context Reasoning in Object Detection | [ICCV’ 17] |[pdf]
• [Light-Head R-CNN] Light-Head R-CNN: In Defense of Two-Stage Object Detector | [arXiv’ 17] |[pdf] [official code - tensorflow]
• [Soft-NMS] Improving Object Detection With One Line of Code | [ICCV’ 17] |[pdf] [official code - caffe]

• [YOLO v3] YOLOv3: An Incremental Improvement | [arXiv’ 18] |[pdf] [official code - c] [unofficial code - pytorch] [unofficial code - pytorch] [unofficial code - keras] [unofficial code - tensorflow]
• [ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | [IJCV’ 18] |[pdf] [official code - caffe]
• [SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | [CVPR’ 18] |[pdf] [official code - tensorflow]
• [STDN] Scale-Transferrable Object Detection | [CVPR’ 18] |[pdf]
• [RefineDet] Single-Shot Refinement Neural Network for Object Detection | [CVPR’ 18] |[pdf] [official code - caffe] [unofficial code - chainer] [unofficial code - pytorch]
• [MegDet] MegDet: A Large Mini-Batch Object Detector | [CVPR’ 18] |[pdf]
• [DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | [CVPR’ 18] |[pdf] [official code - caffe]
• [SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | [CVPR’ 18] |[pdf]
• [Relation-Network] Relation Networks for Object Detection | [CVPR’ 18] |[pdf] [official code - mxnet]
• [Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | [CVPR’ 18] |[pdf] [official code - caffe]
• Finding Tiny Faces in the Wild with Generative Adversarial Network | [CVPR’ 18] |[pdf]
• [MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | [CVPR’ 18] |[pdf] [official code - caffe]
• Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | [CVPR’ 18] |[pdf] [official code - chainer]
• [Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | [CVPR’ 18] |[pdf]
• [STDnet] STDnet: A ConvNet for Small Target Detection | [BMVC’ 18] |[pdf]
• [RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV’ 18] |[pdf] [official code - pytorch]
• Zero-Annotation Object Detection with Web Knowledge Transfer | [ECCV’ 18] |[pdf]
• [CornerNet] CornerNet: Detecting Objects as Paired Keypoints | [ECCV’ 18] |[pdf] [official code - pytorch]
• [PFPNet] Parallel Feature Pyramid Network for Object Detection | [ECCV’ 18] |[pdf]
• [Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | [arXiv’ 18] |[pdf]
• [ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | [ECML-PKDD’ 18] |[pdf] [official code - tensorflow]
• [Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | [NIPS’ 18] |[pdf] [official code - caffe]
• [HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | [NIPS’ 18] |[pdf]
• [MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | [NIPS’ 18] |[pdf]
• [SNIPER] SNIPER: Efficient Multi-Scale Training | [NIPS’ 18] |[pdf]

• [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | [AAAI’ 19] |[pdf] [official code - pytorch]
• [R-DAD] Object Detection based on Region Decomposition and Assembly | [AAAI’ 19] |[pdf]
• [CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | [ICLR’ 19] |[pdf]
• Feature Intertwiner for Object Detection | [ICLR’ 19] |[pdf]
• [GIoU] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | [CVPR’ 19] |[pdf]
• Automatic adaptation of object detectors to new domains using self-training | [CVPR’ 19] |[pdf]
• [Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR’ 19] |[pdf]
• Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR’ 19] |[pdf]
• [ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR’ 19] |[pdf] | [official code - pytorch]
• [C-MIL] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection | [CVPR’ 19] |[pdf] | [official code - torch]
• [ScratchDet] ScratchDet: Training Single-Shot Object Detectors from Scratch | [CVPR’ 19] |[pdf]
• Bounding Box Regression with Uncertainty for Accurate Object Detection | [CVPR’ 19] |[pdf] | [official code - caffe2]
• Activity Driven Weakly Supervised Object Detection | [CVPR’ 19] |[pdf]
• Towards Accurate One-Stage Object Detection with AP-Loss | [CVPR’ 19] |[pdf]
• Strong-Weak Distribution Alignment for Adaptive Object Detection | [CVPR’ 19] |[pdf] | [official code - pytorch]
• [NAS-FPN] NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | [CVPR’ 19] |[pdf]
• [Adaptive NMS] Adaptive NMS: Refining Pedestrian Detection in a Crowd | [CVPR’ 19] |[pdf]
• Point in, Box out: Beyond Counting Persons in Crowds | [CVPR’ 19] |[pdf]
• Locating Objects Without Bounding Boxes | [CVPR’ 19] |[pdf]
• Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | [CVPR’ 19] |[pdf]
• Towards Universal Object Detection by Domain Attention | [CVPR’ 19] |[pdf]
• Exploring the Bounds of the Utility of Context for Object Detection | [CVPR’ 19] |[pdf]
• What Object Should I Use? – Task Driven Object Detection | [CVPR’ 19] |[pdf]
• Dissimilarity Coefficient based Weakly Supervised Object Detection | [CVPR’ 19] |[pdf]
• Adapting Object Detectors via Selective Cross-Domain Alignment | [CVPR’ 19] |[pdf]
• Fully Quantized Network for Object Detection | [CVPR’ 19] |[pdf]
• Distilling Object Detectors with Fine-grained Feature Imitation | [CVPR’ 19] |[pdf]
• Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations | [CVPR’ 19] |[pdf]
• [Reasoning-RCNN] Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | [CVPR’ 19] |[pdf]
• Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | [CVPR’ 19] |[pdf]
• Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors | [CVPR’ 19] |[pdf]
• Spatial-aware Graph Relation Network for Large-scale Object Detection | [CVPR’ 19] |[pdf]
• [MaxpoolNMS] MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors | [CVPR’ 19] |[pdf]
• You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection | [CVPR’ 19] |[pdf]
• Object detection with location-aware deformable convolution and backward attention filtering | [CVPR’ 19] |[pdf]
• Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | [CVPR’ 19] |[pdf]
• [GFR] Improving Object Detection from Scratch via Gated Feature Reuse | [BMVC’ 19] |[pdf] | [official code - pytorch]
• [Cascade RetinaNet] Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection | [BMVC’ 19] |[pdf]
• Soft Sampling for Robust Object Detection | [BMVC’ 19] |[pdf]
• Multi-adversarial Faster-RCNN for Unrestricted Object Detection | [ICCV’ 19] |[pdf]
• Towards Adversarially Robust Object Detection | [ICCV’ 19] |[pdf]
• [Cap2Det] Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection | [ICCV’ 19] |[pdf]
• [Gaussian YOLOv3] Gaussian YOLOv3: An Accurate and Fast Object Detector using Localization Uncertainty for Autonomous Driving | [ICCV’ 19] |[pdf]

Source

Reinforcement Learning

• Human Level Control through Deep Reinforcement Learning
• Asynchronous Methods for Deep Reinforcement Learning
• Deep Reinforcement Learning with Double Q-learning
• Dueling Network Architectures for Deep Reinforcement Learning
• Playing Atari with Deep Reinforcement Learning
• HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
• Deterministic Policy Gradient Algorithms
• Continuous control with deep reinforcement learning
• High-Dimensional Continuous Control Using Generalized Advantage Estimation
• Hybrid Reward Architecture for Reinforcement Learning
• Trust Region Policy Optimization
• Proximal Policy Optimization Algorithms
• Emergence of Locomotion Behaviours in Rich Environments
• Action-Conditional Video Prediction using Deep Networks in Atari Games
• A Distributional Perspective on Reinforcement Learning
• Distributional Reinforcement Learning with Quantile Regression
• The Option-Critic Architecture
• Some hyper-parameters are from DeepMind Control Suite, OpenAI Baselines and Ilya Kostrikov

Source

Pages: 1 2 3