AI Geeks

Papers

Natural Language Processing

Distributed Word Representations

Distributed Sentence Representations

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

Language Model

Machine Translation

Question Answering

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

Sentences Matching

  • 2017-12
  • 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

Text Classification

  • 2017-09
  • 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
  • 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

  • [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

Source