/deep-learning-papers

A curated list of deep learning papers

Deep learning papers

My own curated list of deep learning papers, inspired by Deep Learning Papers Reading Roadmap and Awesome - Most Cited Deep Learning Papers.

Understanding / Generalization / Transfer

  • Distilling the knowledge in a neural network (2015), G. Hinton et al. [pdf]
  • Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al. [pdf]
  • How transferable are features in deep neural networks? (2014), J. Yosinski et al. [pdf]
  • CNN features off-the-Shelf: An astounding baseline for recognition (2014), A. Razavian et al. [pdf]
  • Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [pdf]
  • Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [pdf]
  • Decaf: A deep convolutional activation feature for generic visual recognition (2014), J. Donahue et al. [pdf]
  • Wide & Deep Learning for Recommender Systems (2016), H. Cheng et al. [pdf]
  • Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters (2015), Y. Li et al. [pdf]

Optimization / Training Techniques

  • Training very deep networks (2015), R. Srivastava et al. [pdf]
  • Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015), S. Loffe and C. Szegedy [pdf]
  • Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. [pdf]
  • Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al. [pdf]
  • Adam: A method for stochastic optimization (2014), D. Kingma and J. Ba [pdf]
  • Improving neural networks by preventing co-adaptation of feature detectors (2012), G. Hinton et al. [pdf]
  • Random search for hyper-parameter optimization (2012) J. Bergstra and Y. Bengio [pdf]
  • mixup: BEYOND EMPIRICAL RISK MINIMIZATION (2018) H. Zhang et al. [pdf]
  • A COMPARISON OF FIVE MULTIPLE INSTANCE LEARNING POOLING FUNCTIONS FOR SOUND EVENT DETECTION WITH WEAK LABELING (2019) Y. Wang et al. [pdf]

Unsupervised / Generative Models

  • Pixel recurrent neural networks (2016), A. Oord et al. [pdf]
  • Improved techniques for training GANs (2016), T. Salimans et al. [pdf]
  • Unsupervised representation learning with deep convolutional generative adversarial networks (2015), A. Radford et al. [pdf]
  • DRAW: A recurrent neural network for image generation (2015), K. Gregor et al. [pdf]
  • Generative adversarial nets (2014), I. Goodfellow et al. [pdf]
  • Auto-encoding variational Bayes (2013), D. Kingma and M. Welling [pdf]
  • Building high-level features using large scale unsupervised learning (2013), Q. Le et al. [pdf]
  • A Style-Based Generator Architecture for Generative Adversarial Networks (2019), T. Karras et al. [pdf]
  • Progressive Growing of GANs for Improved Quality, Stability, and Variation (2018), T. Karras et al. [pdf]
  • Training Generative Reversible Networks (2018), R. Schirrmeister et al. [pdf]

Convolutional Neural Network Models

  • Rethinking the inception architecture for computer vision (2016), C. Szegedy et al. [pdf]
  • Inception-v4, inception-resnet and the impact of residual connections on learning (2016), C. Szegedy et al. [pdf]
  • Identity Mappings in Deep Residual Networks (2016), K. He et al. [pdf]
  • Deep residual learning for image recognition (2016), K. He et al. [pdf]
  • Spatial transformer network (2015), M. Jaderberg et al., [pdf]
  • Going deeper with convolutions (2015), C. Szegedy et al. [pdf]
  • Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman [pdf]
  • Return of the devil in the details: delving deep into convolutional nets (2014), K. Chatfield et al. [pdf]
  • OverFeat: Integrated recognition, localization and detection using convolutional networks (2013), P. Sermanet et al. [pdf]
  • Maxout networks (2013), I. Goodfellow et al. [pdf]
  • Network in network (2013), M. Lin et al. [pdf]
  • ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. [pdf]
  • Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids (2018), Z. Zheng et al. [pdf]
  • LEARN TO PAY ATTENTION (2018), S. Jetley et al. [pdf]
  • Densely Connected Convolutional Networks (2018), G. Huang et al. [pdf]

Image: Segmentation / Object Detection

  • You only look once: Unified, real-time object detection (2016), J. Redmon et al. [pdf]
  • Fully convolutional networks for semantic segmentation (2015), J. Long et al. [pdf]
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. [pdf]
  • Fast R-CNN (2015), R. Girshick [pdf]
  • Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. [pdf]
  • Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al. [pdf]
  • Semantic image segmentation with deep convolutional nets and fully connected CRFs, L. Chen et al. [pdf]
  • Learning hierarchical features for scene labeling (2013), C. Farabet et al. [pdf]

Image / Video / Etc

  • Image Super-Resolution Using Deep Convolutional Networks (2016), C. Dong et al. [pdf]
  • A neural algorithm of artistic style (2015), L. Gatys et al. [pdf]
  • Deep visual-semantic alignments for generating image descriptions (2015), A. Karpathy and L. Fei-Fei [pdf]
  • Show, attend and tell: Neural image caption generation with visual attention (2015), K. Xu et al. [pdf]
  • Show and tell: A neural image caption generator (2015), O. Vinyals et al. [pdf]
  • Long-term recurrent convolutional networks for visual recognition and description (2015), J. Donahue et al. [pdf]
  • VQA: Visual question answering (2015), S. Antol et al. [pdf]
  • DeepFace: Closing the gap to human-level performance in face verification (2014), Y. Taigman et al. [pdf]:
  • Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. [pdf]
  • Two-stream convolutional networks for action recognition in videos (2014), K. Simonyan et al. [pdf]
  • 3D convolutional neural networks for human action recognition (2013), S. Ji et al. [pdf]
  • Quadruplet Network with One-Shot Learning for Fast Visual Object Tracking (2019), X. Dong et al. [pdf]

Natural Language Processing / RNNs

  • Neural Architectures for Named Entity Recognition (2016), G. Lample et al. [pdf]
  • Exploring the limits of language modeling (2016), R. Jozefowicz et al. [pdf]
  • Teaching machines to read and comprehend (2015), K. Hermann et al. [pdf]
  • Effective approaches to attention-based neural machine translation (2015), M. Luong et al. [pdf]
  • Conditional random fields as recurrent neural networks (2015), S. Zheng and S. Jayasumana. [pdf]
  • Memory networks (2014), J. Weston et al. [pdf]
  • Neural turing machines (2014), A. Graves et al. [pdf]
  • Neural machine translation by jointly learning to align and translate (2014), D. Bahdanau et al. [pdf]
  • Sequence to sequence learning with neural networks (2014), I. Sutskever et al. [pdf]
  • Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014), K. Cho et al. [pdf]
  • A convolutional neural network for modeling sentences (2014), N. Kalchbrenner et al. [pdf]
  • Convolutional neural networks for sentence classification (2014), Y. Kim [pdf]
  • Glove: Global vectors for word representation (2014), J. Pennington et al. [pdf]
  • Distributed representations of sentences and documents (2014), Q. Le and T. Mikolov [pdf]
  • Distributed representations of words and phrases and their compositionality (2013), T. Mikolov et al. [pdf]
  • Efficient estimation of word representations in vector space (2013), T. Mikolov et al. [pdf]
  • Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher et al. [pdf]
  • Generating sequences with recurrent neural networks (2013), A. Graves. [pdf]
  • WaveGlow: a Flow-based Generative Network for Speech Synthesis (2013), R. Prenger et al. [pdf]
  • ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs (2018), W. Yin et al. [pdf]

Speech / Other Domain

  • End-to-end attention-based large vocabulary speech recognition (2016), D. Bahdanau et al. [pdf]
  • Deep speech 2: End-to-end speech recognition in English and Mandarin (2015), D. Amodei et al. [pdf]
  • Speech recognition with deep recurrent neural networks (2013), A. Graves [pdf]
  • Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups (2012), G. Hinton et al. [pdf]
  • Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012) G. Dahl et al. [pdf]
  • Acoustic modeling using deep belief networks (2012), A. Mohamed et al. [pdf]
  • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (2019), Y. Jia et al. [pdf]
  • Deep features-based speech emotion recognition for smart affective services (2016), A. Badshah et al. [pdf]
  • Speech Emotion Recognition using Convolutional and Recurrent Neural Networks (2016), W. Lim et al. [pdf]
  • Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features (2019), A. Tursunov et al. [pdf]
  • Deep Learning based Emotion Recognition System Using Speech Features and Transcriptions (2019), S. Tripathi et al. [pdf]
  • Speech Emotion Recognition using Deep Learning (2018), N. Roopa et al. [pdf]
  • Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019), R. Khalil et al. [pdf]

Reinforcement Learning / Robotics

  • End-to-end training of deep visuomotor policies (2016), S. Levine et al. [pdf]
  • Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection (2016), S. Levine et al. [pdf]
  • Asynchronous methods for deep reinforcement learning (2016), V. Mnih et al. [pdf]
  • Deep Reinforcement Learning with Double Q-Learning (2016), H. Hasselt et al. [pdf]
  • Mastering the game of Go with deep neural networks and tree search (2016), D. Silver et al. [pdf]
  • Continuous control with deep reinforcement learning (2015), T. Lillicrap et al. [pdf]
  • Human-level control through deep reinforcement learning (2015), V. Mnih et al. [pdf]
  • Deep learning for detecting robotic grasps (2015), I. Lenz et al. [pdf]
  • Playing atari with deep reinforcement learning (2013), V. Mnih et al. [pdf])

Music

  • A tutorial on deep learning for music information retrieval (2018), K. Choi et al. [pdf]
  • Singing style investigation by residual siamese convolutional neural networks (2018), C. Wang et al. [pdf]
  • Deep autotuner: a data-driven approach to natural-sounding pitch correction for singing voice in karaoke performances (2019), S. Wager et al. [pdf]
  • Exploting synchronized lyrics and vocal features for music emotion detection (2019), L. Parisi et al. [pdf]
  • Timbre analysis of music audio signals with convolutional neural networks (2017), P. Jordi et al. [pdf]
  • Designing efficient architectures for modeling temporal features with convolutional neural networks (2017), P. Jordi et al. [pdf]
  • Automatic tagging using deep convolutional neural networks (2016), K. Choi et al. [pdf]
  • EXPLORING DATA AUGMENTATION FOR IMPROVED SINGING VOICE DETECTION WITH NEURAL NETWORKSs (2015), J. Schlüter et al. [pdf]
  • GANSynth: Adversarial Neural Audio Synthesis (2019), J. Engel et al. [pdf]
  • WAVENET: A GENERATIVE MODEL FOR RAW AUDIO (2016), A. Oord et al. [pdf]
  • HIT SONG PREDICTION:LEVERAGING LOW- AND HIGH-LEVEL AUDIO FEATURES (2019), E. Zangerle et al. [pdf]
  • SINGING EXPRESSION TRANSFER FROM ONE VOICE TO ANOTHER FOR A GIVEN SONG (2018), S. Yong et al. [pdf]
  • Modeling of the Latent Embedding of Music using Deep Neural Network (2017), Z. Xing et al. [pdf]
  • A COMPARISON OF FIVE MULTIPLE INSTANCE LEARNING POOLING FUNCTIONS FOR SOUND EVENT DETECTION WITH WEAK LABELING (2019), Y. Wang et al. [pdf]
  • AUTOMATIC SINGING EVALUATION WITHOUT REFERENCE MELODY USING BI-DENSE NEURAL NETWORK (2019), N. Zhang et al. [pdf]
  • Automatic Evaluation of Singing Quality without a Reference (2018), C. Gupta et al. [pdf]
  • GENERATIVE TIMBRE SPACES: REGULARIZING VARIATIONAL AUTO-ENCODERS WITH PERCEPTUAL METRICS (2018), P. Esling et al. [pdf]
  • MODULATED VARIATIONAL AUTO-ENCODERS FOR MANY-TO-MANY MUSICAL TIMBRE TRANSFER (2018), A. Bitton et al. [pdf]
  • LEARNING DISENTANGLED REPRESENTATIONS OF TIMBRE AND PITCH FOR MUSICAL INSTRUMENT SOUNDS USING GAUSSIAN MIXTURE VARIATIONAL AUTOENCODERS (2019), Y. Luo et al. [pdf]
  • DISENTANGLING TIMBRE AND SINGING STYLE WITH MULTI-SINGER SINGING SYNTHESIS SYSTEM (2019), J. Lee et al. [pdf]
  • Adversarially Trained End-to-end Korean Singing Voice Synthesis System (2019), J. Lee et al. [pdf]
  • WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN (2019), P. Chandna et al. [pdf]
  • Efficient karaoke song recommendation via multiple kernel learning approximation (2016), C. Guan et al. [pdf]
  • Vocal Competence Based Karaoke Recommendation: A Maximum-Margin Joint Model (2016), C. Guan et al. [pdf]
  • Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network (2015), A. Simpson et al. [pdf]
  • EMPIRICALLY WEIGHING THE IMPORTANCE OF DECISION FACTORS WHEN SELECTING MUSIC TO SING (2018), K. Ibrahim et al. [pdf]
  • Perceptual Evaluation of Singing Quality (2017), C. Gupta et al. [pdf]
  • Heterogeneous Collaborative Filtering (2019), Y. Liu et al. [pdf]
  • LEARNING A JOINT EMBEDDING SPACE OF MONOPHONIC AND MIXED MUSIC SIGNALS FOR SINGING VOICE (2019), K. Lee et al. [pdf]
  • VOCAL TIMBRE ANALYSIS USING LATENT DIRICHLET ALLOCATION AND CROSS-GENDER VOCAL TIMBRE SIMILARITY (2014), T. Nakano et al. [pdf]
  • REPRESENTATION LEARNING OF MUSIC USING ARTIST LABELS (2018), J. Park et al. [pdf]
  • REVISITING SINGING VOICE DETECTION: A QUANTITATIVE REVIEW AND THE FUTURE OUTLOOK (2018), K. Lee et al. [pdf]
  • Deep Content-User Embedding Model for Music Recommendation (2018), J. Lee et al. [pdf]