Felt like I wasn’t reading enough – and what I was reading wasn’t sinking in enough. I also wanted to keep track of my sources in a more controlled manner. As a part of adding everything to my JabRef (maybe…), I figured I would write up my comments on papers.

The goal is to read and comment once a day and this post will be updated day by day according to the reading process.

✏️ A Paper A Day🔁 Generative Models👊 Adversarial Examples/Attacks🎵 Sound & Signal Processing📍 Anomaly Detection / Open Set RecognitionUnsupervised / Domain Adaptation📉 Optimization & Generalization👍 Model Evaluation & Performance & Interpretion & Visualization🎛 Model Configuration〽️ ODE & PDE⚛️ Physics Related📚 Review, Survey & Lecture Notes🖼 Figure Design & Dimension Reduction

**Object Discovery with a Copy-Pasting GAN**. Relja Arandjelovic, Andrew Zisserman [DeepMind] (2019) arXiv:1905.11369

**Image Generation from Small Datasets via Batch Statistics Adaptation**. A Noguchi, T Harada [The University of Tokyo] (2019) arXiv:1904.01774

**A Three-Player GAN: Generating Hard Samples To Improve Classification Networks**. S Vandenhende, B D Brabandere, D Neven, L V Gool [KU Leuven] (2019) arXiv:1903.03496

**O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks**. Jianlin Su [Sun Yat-sen University] (2019) arXiv:1903.01931 Reddit PaperWeekly

**AVP: Physics-informed Data Generation for Small-data Learning**. J Chen, Y Xie, K Wang, C Zhang, M A. Vannan, B Wang, Z Qian [Georgia Institute of Technology] (2019) arXiv:1902.01522

**A Layer-Based Sequential Framework for Scene Generation with GANs**. M O Turkoglu, W Thong, L Spreeuwers, B Kicanaoglu [University of Twente & University of Amsterdam] (2019) arXiv:1902.00671 Github

**Perturbative GAN: GAN with Perturbation Layers**. Y Kishi, T Ikegami, S O'uchi, R Takano, W Nogami, T Kudoh [National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan & The University of Tokyo] (2019) arXiv:1902.01514

**HyperGAN: A Generative Model for Diverse, Performant Neural Networks**. N Ratzlaff, L Fuxin [Oregon State University] (2019) arXiv:1901.11058

**Lie Group Auto-Encoder**. L Gong, Q Cheng [University of Kentucky] (2019) arXiv:1901.09970

**Maximum Entropy Generators for Energy-Based Models**. Rithesh Kumar, Anirudh Goyal, Aaron Courville, Yoshua Bengio [Mila & CIFAR & IVADO] (2019) arXiv:1901.08508 Reddit PaperWeekly PaperWeekly

**MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks**. D Li, D Chen, L Shi, B Jin, J Goh, S Ng [National University of Singapore & UC Berkeley & ST Electronics (Info Security) Pte Ltd] (2019) arXiv:1901.04997

**On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games**. E V. Mazumdar, M I. Jordan, S. S Sastry [UC Berkeley] (2019) arXiv:1901.00838

**Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions**. S O'Brien, M Groh, A Dubey [MIT] (2018) arXiv:1812.10782 Github

**InstaGAN: Instance-aware Image-to-Image Translation**. S Mo, M Cho, J Shin [Korea Advanced Institute of Science and Technology (KAIST) & Pohang University of Science and Technology (POSTECH)] (2018) arXiv:1812.10889 OpenReview.net Github 机器之心

**Improving Generalization and Stability of Generative Adversarial Networks**. H Thanh-Tung, T Tran, S Venkatesh [Deakin University] (ICLR 2018) OpenReview.net Github

**Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN**. S Minaee, A Abdolrashidi [New York University & University of California, Riverside] (2018) arXiv:1812.10482

**Generative Models from the perspective of Continual Learning**. T Lesort, H Caselles-Dupré, M Garcia-Ortiz, A Stoian, D Filliat [Flowers Team (ENSTA ParisTech & INRIA)] (2018) arXiv:1812.09111 Github OpenReview.net

**A Probe into Understanding GAN and VAE models**. J Zhang, L Mi, M Shen [MIT] (2018) arXiv:1812.05676

**A Style-Based Generator Architecture for Generative Adversarial Networks**. T Karras, S Laine, T Aila [NVIDIA] (2018) arXiv:1812.04948 Code YouTube 机器之心

**Intra-class Variation Isolation in Conditional GANs**. R T. Marriott, S Romdhani, L Chen [Ecole Centrale de Lyon & IDEMIA] (2018) arXiv:1811.11296

**Metropolis-Hastings Generative Adversarial Networks**. R Turner, J Hung, Y Saatci, J Yosinski [Uber AI Labs] (2018) arXiv:1811.11357 Github Blog

**Label-Noise Robust Generative Adversarial Networks**. Takuhiro Kaneko, Yoshitaka Ushiku, Tatsuya Harada [The University of Tokyo & RIKEN] (2018) arXiv:1811.11165

**Do GAN Loss Functions Really Matter?**. Y Qin, N Mitra, P Wonka [KAUST & UCL] (2018) arXiv:1811.09567 Reddit

**Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization**. N Jetchev, U Bergmann, G Yildirim [Zalando Research] (2018) arXiv:1811.09236 GitHub

**Guiding the One-to-one Mapping in CycleGAN via Optimal Transport**. G Lu, Z Zhou, Y Song, K Ren, Y Yu [Shanghai Jiao Tong University] (2018) arXiv:1811.06284

**NEMGAN: Noise Engineered Mode-matching GAN**. D Mishra, P AP, A J, P Pandey, S Chaudhury [Indian Institute of Technology Delhi] (2018) arXiv:1811.03692 GitHub

**Bias and Generalization in Deep Generative Models: An Empirical Study**. S Zhao, H Ren, A Yuan, J Song, N Goodman, S Ermon [Stanford University] (ICML2018) arXiv:1811.03259 GitHub

**Language GANs Falling Short**. Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin [MILA, Université de Montréal & MILA, McGill University & MILA, HEC Montréal & Google Brain & Facebook AI Research] (2018) arXiv:1811.02549

**CariGANs: Unpaired Photo-to-Caricature Translation**. K Cao, J Liao, L Yuan [Tsinghua University & Microsoft Research] (2018) arXiv:1811.00222 Blog App YouTube

**Large Scale GAN Training for High Fidelity Natural Image Synthesis** | OpenReview. (2018) More examples BigGAN Demo(Colab Notebook)

**Generative adversarial networks and adversarial methods in biomedical image analysis**. J M. Wolterink, K Kamnitsas, C Ledig, I Išgum [University Medical Center Utrecht & Imperial College London & Imagen Technologies] (2018) arXiv:1810.10352

**Do Deep Generative Models Know What They Don't Know?**. E Nalisnick, A Matsukawa, Y W Teh, D Gorur, B Lakshminarayanan [DeepMind] (2018) arXiv:1810.09136

**Discriminator Rejection Sampling**. Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian Goodfellow, Augustus Odena [UC Berkeley & Google Brain]. arXiv:1810.06758

**Refacing: reconstructing anonymized facial features using GANs**. D Abramian, A Eklund [Linkoping University] (2018) arXiv:1810.06455

**ClusterGAN : Latent Space Clustering in Generative Adversarial Networks**. S Mukherjee, H Asnani, E Lin, S Kannan [University of Washington] (2018) arXiv:1809.03627

**Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks**. Santiago Pascual, Antonio Bonafonte, Joan Serrà, Jose A. Gonzalez [Universitat Polite`cnica de Catalunya & Telefo ́nica Research & Universidad de Ma ́laga, Spain] (2018) arXiv:1808.10687

**The relativistic discriminator: a key element missing from standard GAN**. Alexia Jolicoeur-Martineau [Lady Davis Institute Montreal, Canada] (2018) arXiv:1807.00734

**Exploring Disentangled Feature Representation Beyond Face Identification**. Yu Liu, Fangyin Wei, Jing Shao, Lu Sheng, Junjie Yan, Xiaogang Wang [The Chinese University of Hong Kong, SenseTime Group Limited, Peking University] (2018) arXiv:1804.03487

**Evolutionary Generative Adversarial Networks**. Chaoyue Wang, Chang Xu, Xin Yao, Dacheng Tao [2018] arXiv:1803.00657 PaperWeekly

**Do GANs learn the distribution? Some Theory and Empirics**. Sanjeev Arora, Andrej Risteski, Yi Zhang [Princeton University & MIT] (ICLR 2018) OpenReview.net

**Do GANs actually learn the distribution? An empirical study**. Sanjeev Arora, Yi Zhang [] (2017) arXiv:1706.08224 Reddit Blog

**Generalization and Equilibrium in Generative Adversarial Nets (GANs)**. S Arora, R Ge, Y Liang, T Ma, Y Zhang [Princeton University & Duke University] (2017) arXiv:1703.00573 Blog Reddit

**InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets**. X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel [UC Berkeley & OpenAI] (2016) arXiv:1606.03657 Reddit

**Improved Techniques for Training GANs**. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen [OpenAI] (2016) arXiv:1606.03498 Github Reddit Github PyTorch

**GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier**. G Liu, I Khalil, A Khreishah [New Jersey Institute of Technology & Qatar Computing Research Institute] (2019) https://arxiv.org/abs/1903.02585

**Adversarial Attacks on Time Series**. Fazle Karim, Somshubra Majumdar, Houshang Darabi […] (2019) arXiv:1902.10755

**On Evaluating Adversarial Robustness**. N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, I Goodfellow, A Madry [Google Brain & MIT & University of Tübingen] (2019) arXiv:1902.06705 Github Reddit

**Adversarial Examples Are a Natural Consequence of Test Error in Noise**. Nic Ford, Justin Gilmer, Nicolas Carlini, Dogus Cubuk [Google AI Residency] (2019) arXiv:1901.10513 Reddit

**Towards a Deeper Understanding of Adversarial Losses**. H Dong, Y Yang [Academia Sinica] (2019) arXiv:1901.08753 Github Blog

**Image Transformation can make Neural Networks more robust against Adversarial Examples**. D D Thang, T Matsui [Institute of Information Security] (2019) arXiv:1901.03037

**Multi-Label Adversarial Perturbations**. Q Song, H Jin, X Huang, X Hu [Texas A&M University] (2019) arXiv:1901.00546

**Adversarial Transfer Learning**. G Wilson, D J. Cook [Washington State University] (2018) arXiv:1812.02849

**Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks**. S Wang, X Wang, P Zhao, W Wen, D Kaeli, P Chin, X Lin [Northeastern University & Boston university & Florida International University] (2018) arXiv:1809.05165

**Are adversarial examples inevitable?**. A Shafahi, W. R Huang, C Studer, S Feizi, T Goldstein (2018) arXiv:1809.02104

**Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples**. Anish Athalye, Nicholas Carlini, David Wagner [MIT & Berkeley] (2018) arXiv:1802.00420 Github

**Generating Natural Adversarial Examples**. Z Zhao, D Dua, S Singh [University of California, Irvine] (2017) arXiv:1710.11342 Github [comment]

**MelNet: A Generative Model for Audio in the Frequency Domain**. Sean Vasquez, Mike Lewis [Facebook AI Research] (2019) arXiv:1906.01083 Blog Audio Samples Facebook频谱图模型生成比尔·盖茨声音，性能完胜WaveNet、MAESTRO

**Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning**. T Wen, R Keyes [Arundo Analytics] (2019) arXiv:1905.13628

**VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking**. Q Wang, H Muckenhirn, K Wilson, P Sridhar, Z Wu, J Hershey, R A. Saurous, R J. Weiss, Y Jia, I L Moreno [Google & Idiap Research Institute] (2018) arXiv:2018:04826 Home VentureBeat Tproger 机器之心 新智元

**Phase-aware Speech Enhancement with Deep Complex U-Net**. H Choi, J Kim, J Huh, A Kim, J Ha, K Lee [Seoul National University & NAVER Corp] (2019) arXiv:1903.03107 Home OpenReview.net

**A Deep Generative Model of Speech Complex Spectrograms**. A A Nugraha, K Sekiguchi, K Yoshii [RIKEN Center for Advanced Intelligence Project (AIP) & Kyoto University] (2019) arXiv:1903.03269

**Utterance-level Aggregation For Speaker Recognition In The Wild**. W Xie, A Nagrani, J S Chung, A Zisserman [University of Oxford] (2019) arXiv:1902.10107 Blog

**catch22: CAnonical Time-series CHaracteristics**. C H Lubba, S S Sethi, P Knaute, S R Schultz, B D Fulcher, N S Jones [Imperial College London] (2019) arXiv:1901.10200 Github

**End-to-End Probabilistic Inference for Nonstationary Audio Analysis**. W J. Wilkinson, M R Andersen, J D. Reiss, D Stowell, A Solin [Queen Mary University of London & Aalto University] (2019) arXiv:1901.11436

**Unsupervised speech representation learning using WaveNet autoencoders**. J Chorowski, R J. Weiss, S Bengio, A v d Oord [University of Wrocław & Google Research & DeepMind] (2019) arXiv:1901.08810

**Kymatio: Scattering Transforms in Python**. M Andreux, T Angles, G Exarchakis... [PSL Research University & Universite de Montreal & New York University] (2018) arXiv:1812.11214 Home

**Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness**. Konstantinos Patlatzoglou, etc. [etc.] (2018) PDF

**Using Convolutional Neural Networks to Classify Audio Signal in Noisy Sound Scenes**. M.V. Gubin [South Ural State University] (2018 GloSIC) PDF Github

**Interpretable Convolutional Filters with SincNet**. M Ravanelli, Y Bengio [Université de Montréal] (2018) arXiv:1811.09725 GitHub

**A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data**. C Zhang, D Song, Y Chen, X Feng, C Lumezanu, W Cheng, J Ni, B Zong, H Chen, N V. Chawla [University of Notre Dame & NEC Laboratories America & Columbia University] (2018) arXiv:1811.08055

**Stochastic Adaptive Neural Architecture Search for Keyword Spotting**. T Véniat, O Schwander, L Denoyer [Sorbonne Université & Facebook AI Research] (2018) arXiv:1811.06753 GitHub

**Unifying Probabilistic Models for Time-Frequency Analysis**. W J. Wilkinson, M R Andersen, J D. Reiss, D Stowell, A Solin [Queen Mary University of London & Aalto University] (2018) arXiv:1811.02489 GitHub

**WaveGlow: A Flow-based Generative Network for Speech Synthesis**. Ryan Prenger, Rafael Valle, Bryan Catanzaro [NVIDIA Corporation] (2018) arXiv:1811.00002 Github

**Training neural audio classifiers with few data**. J Pons, J Serrà, X Serra [Telefonica Research & Universitat Pompeu Fabra] (2018) arXiv:1810.10274 Github

**End-to-end music source separation: is it possible in the waveform domain?**. F Lluís, J Pons, X Serra [Universitat Pompeu Fabra] (2018) arXiv:1810.12187

**Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis**. Jingyuan Wang, Ze Wang, Jianfeng Li, Junjie Wu.[Beihang University] (2018) arXiv:1806.08946

**Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition**. Jian Bo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, Shonali Krishnaswamy (2015) IJCAI2015

**Towards a universal neural network encoder for time series**. J Serrà, S Pascual, A Karatzoglou [Telefonica Research & Universitat Politecnica de Catalunya] (2018)

**Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network**. Sharath Adavanne, Pasi Pertilä, Tuomas Virtanen [Tampere University of Technology] (DCASE 2017) arXiv:1706.02291 Github

**Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks**. Yi Zheng, Qi Liu, Enhong Chen, Yong Ge, and J. Leon Zhao [USTC, et al.] (2014) WAIM2014

**Deep CNN-based Multi-task Learning for Open-Set Recognition**. P Oza, V M. Patel [Johns Hopkins University] (2019) arXiv:1903.03161 Github

**Learning Confidence Sets using Support Vector Machines**. W Wang, X Qiao [Binghamton University] (2018) arXiv:1809.10818

**Novelty Detection with GAN**, Mark Kliger, Shachar Fleishman [Amazon] arXiv:1802.10560

**Learning Confidence for Out-of-Distribution Detection in Neural Networks**. T DeVries, G W. Taylor [University of Guelph & Vector Institute] (2018) arXiv:1802.04865

**Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples**, Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin [Korea Advanced Institute of Science and Technology, University of Michigan, Google Brain] (ICLR 2018) arXiv:1711.09325

**Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation**. C Lee, T Batra, M H Baig, D Ulbricht [Apple Inc] (2019) arXiv:1903.04064

**Transfusion: Understanding Transfer Learning with Applications to Medical Imaging**. M Raghu, C Zhang, J Kleinberg, S Bengio [Cornell University & Google Brain] (2019) arXiv:1902.07208

**Decoupled Greedy Learning of CNNs**. Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon [University of Montreal , University of California Berkeley, CentraleSupelec and INRIA] (2019) arXiv:1901.08164 Github

**Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit**. J Sohl-Dickstein, K Kawaguchi [Google & MIT] (2019) arXiv:1901.03909

**The Benefits of Over-parameterization at Initialization in Deep ReLU Networks**. D Arpit, Y Bengio [Montreal Institute for Learning Algorithms] (2019) arXiv:1901.03611

**Generalization in Deep Networks: The Role of Distance from Initialization**. Vaishnavh Nagarajan, J. Zico Kolter [Carnegie-Mellon University] (NeurlPS 2017, 2019) arXiv:1901.01672

**Elimination of All Bad Local Minima in Deep Learning**. K Kawaguchi, L P Kaelbling [MIT] (2019) arXiv:1901.00279

**Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks**. B Neyshabur, Z Li... [Princeton & Toyota Technological Institute at Chicago & Facebook AI Research] (2018) arXiv:1805.12076 Github Reddit

**Visualizing the Loss Landscape of Neural Nets**. H Li, Z Xu, G Taylor, T Goldstein [University of Maryland & United States Naval Academy] (NIPS 2018) arXiv:1712.09913 Github Reddit

**Gradient Descent Happens in a Tiny Subspace**. G Gur-Ari, D A. Roberts, E Dyer [Institute for Advanced Study & Facebook AI Research & Johns Hopkins University] (2018) arXiv:1812.04754

**A Sufficient Condition for Convergences of Adam and RMSProp**. F Zou, L Shen, Z Jie, W Zhang, W Liu [Stony Brook University & Tencent AI Lab] (2018) arXiv:1811.09358

**Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks**. D Zou, Y Cao, D Zhou, Q Gu [University of California, Los Angeles] (2018) arXiv:1811.08888

**Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers**. Z Allen-Zhu, Y Li, Y Liang [Microsoft Research AI & Stanford University & University of Wisconsin-Madison] (2018) arXiv:1811.04918

**A Convergence Theory for Deep Learning via Over-Parameterization**. Z Allen-Zhu, Y Li, Z Song [Microsoft Research AI & Stanford University & UT-Austin] (2018) arXiv:1811.03962

**Gradient Descent Finds Global Minima of Deep Neural Networks**. S S. Du, J D. Lee, H Li, L Wang, X Zhai [CMU & University of Southern California & Peking University & MIT] (2018) arXiv:1811.03804

**Identifying Generalization Properties in Neural Networks**. H Wang, N S Keskar, C Xiong, R Socher [Salesforce Research] (2018) arXiv:1809.07402

**Accelerating Natural Gradient with Higher-Order Invariance**. by Yang Song Post paper

**Training decision trees as replacement for convolution layers**. Wolfgang Fuhl, Gjergji Kasneci, Wolfgang Rosenstiel, Enkelejda Kasneci [Eberhard Karls University Tübingen] arXiv:1905.10073 Reddit

**Unmasking Clever Hans Predictors and Assessing What Machines Really Learn**. S Lapuschkin, S Wäldchen, A Binder, G Montavon, W Samek, K Müller [Fraunhofer Heinrich Hertz Institute & Technische Universitat Berlin & Singapore University of Technology and Design] (2019) arXiv:1902.10178

**Seven Myths in Machine Learning Research**. O Chang, H Lipson [Columbia University] (2019) arXiv:1902.06789 Blog

**Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent**. J Lee, L Xiao, S S. Schoenholz, Y Bahri, J Sohl-Dickstein, J Pennington [Google Brain] (2019) arXiv:1902.06720 Reddit

**Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification**. S H S Basha, S R Dubey, V Pulabaigari, S Mukherjee [Indian Institute of Information Technology Sri City] arXiv:1902.02771 Github

**CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks**. X Cui, D Wang, Z. J Wang [University of British Columbia] (2019) arXiv:1902.02497

**Are All Layers Created Equal?**. C Zhang, S Bengio, Y Singer [Google] (2019) arXiv:1902.01996 新智元

**Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet**. W Brendel, M Bethge [Eberhard Karls University of Tübingen] (2019) OpenReview.net Reddit Blogcomment Blogcomment 过往Net，皆为调参？一篇BagNet论文引发学界震动

**See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification**. T Hu, H Qi [University of Chinese Academy of Sciences] (2019) arXiv:1901.09891

**Deep Learning on Small Datasets without Pre-Training using Cosine Loss**. B Barz, J Denzler [Friedrich Schiller University Jena] (2019) arXiv:1901.09054 Github

**Using Pre-Training Can Improve Model Robustness and Uncertainty**. D Hendrycks, K Lee, M Mazeika (2019) arXiv:1901.09960

**Understanding Geometry of Encoder-Decoder CNNs**. J C Ye, W K Sung [KAIST] (2019) arXiv:1901.07647

**Attribute-Aware Attention Model for Fine-grained Representation Learning**. K Han, J Guo, C Zhang, M Zhu [Peking University] (2019) arXiv:1901.00392

**Multi-class Classification without Multi-class Labels**. Y Hsu, Z Lv, J Schlosser, P Odom, Z Kira [Georgia Institute of Technology & Georgia Tech Research Institute] (ICLR 2019) arXiv:1901.00544

**Are All Training Examples Created Equal? An Empirical Study**. K Vodrahalli, K Li, J Malik [UC Berkeley] (2018) arXiv:1811.12569 知乎

**Rethinking ImageNet Pre-training**. K He, R Girshick, P Dollár [Facebook AI Research (FAIR)] (2018) arXiv:1811.08883

**Efficient Identification of Approximate Best Configuration of Training in Large Datasets**. S Huang, C Wang, B Ding, S Chaudhuri [University of Illinois & Microsoft Research & Alibaba Group] (2018) arXiv:1811.03250

**Explaining Deep Learning Models - A Bayesian Non-parametric Approach**. W Guo, S Huang, Y Tao, X Xing, L Lin [The Pennsylvania State University & Netflix Inc & Columbia University] (2018) arXiv:1811.03422

**How deep is deep enough? - Optimizing deep neural network architecture**. A Schilling, J Rietsch, R Gerum, H Schulze, C Metzner, P Krauss [University Hospital Erlangen & Friedrich-Alexander University Erlangen-N¨urnberg (FAU)] (2018) arXiv:1811.01753

**Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks**. Z Liao, T Drummond, I Reid, G Carneiro [University of Adelaide & Monash University] (2018) arXiv:1810.06767 GitHub

**A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing**. Chaitanya Nagpal, Shiv Ram Dubey (2018) arXiv:1805.04176

**An Information-Theoretic View for Deep Learning**. J Zhang, T Liu, D Tao [UBTECH Sydney AI Centre] (2018) arXiv:1804.09060

**Understanding Individual Neuron Importance Using Information Theory**. K Liu, R A Amjad, B C. Geiger [Technical University of Munich & Graz University of Technology] (2018) arXiv:1804.06679

**Understanding Convolutional Neural Network Training with Information Theory**. S Yu, R Jenssen, J C. Principe [University of Florida & University of Tromsø] (2018) arXiv:1804.06537

**A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay**. Leslie N. Smith. arXiv:1803.09820

**Focal Loss for Dense Object Detection**. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár [Facebook AI Research] (2017) arXiv:1708.02002 Github

**How transferable are features in deep neural networks?**. J Yosinski, J Clune, Y Bengio, H Lipson [Cornell University, University of Wyoming, University of Montreal] (2014) (NIPS 2014)

Samples & Datasets

**Do we train on test data? Purging CIFAR of near-duplicates**. B Barz, J Denzler [Friedrich Schiller University Jena] (2019) arXiv:1902.00423 Blog**Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need**. V Birodkar, H Mobahi, S Bengio [Google Research] (2019) arXiv:1901.11409**Image Score: How to Select Useful Samples**. Simiao Zuo, Jialin Wu [University of Texas at Austin] (2018) arXiv:1812.00334 Reddit**Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift**. S Rabanser, S Günnemann, Z C. Lipton [CMU & Technical University of Munich] (2018) arXiv:1810.11953**How Many Samples are Needed to Learn a Convolutional Neural Network?**. S S. Du, Y Wang, X Zhai, S Balakrishnan, R Salakhutdinov, A Singh [CMU & University of Cambridge] (2018) arXiv:1805.07883Batch-size

**Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise**. Y Wen, K Luk, M Gazeau, G Zhang, H Chan, J Ba [Borealis AI & University of Toronto] (2019) arXiv:1902.08234**An Empirical Model of Large-Batch Training**. Sam McCandlish, Jared Kaplan, Dario Amodei [OpenAI] (DECEMBER 14, 2018) PDF BLOG**Don't Use Large Mini-Batches, Use Local SGD**. T Lin, S U. Stich, M Jaggi [EPFL] (2018) arXiv:1808.07217**Revisiting Small Batch Training for Deep Neural Networks**. Dominic Masters, Carlo Luschi. (2018) arXiv:1804.07612Saliency

**Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps**. B Kim, J Seo, S Jeon, J Koo, J Choe, T Jeon [Korea Advanced Institute of Science and Technology & Daejeon] (2019) arXiv:1902.04893 Github**Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation**. S Singla, E Wallace, S Feng, S Feizi [University of Maryland] (2019) arXiv:1902.00407 Reddit**Visualizing Deep Similarity Networks**. A Stylianou, R Souvenir, R Pless [George Washington University & Temple University] (2019) arXiv:1901.00536 Github**Understanding Individual Decisions of CNNs via Contrastive Backpropagation**. J Gu, Y Yang, V Tresp [the University of Munich & Siemens AG] (2018) arXiv:1812.02100**Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values**. J Adebayo, J Gilmer, I Goodfellow, B Kim [Google Brain] (2018) arXiv:1810.03307 OpenReview**Sanity Checks for Saliency Maps**. J Adebayo, J Gilmer, M Muelly, I Goodfellow, M Hardt, B Kim [Google Brain] (2018) arXiv:1810.03292Explanatory Graphs

**Explanatory Graphs for CNNs**. Q Zhang, X Wang, R Cao, Y N Wu, F Shi, S Zhu [Shanghai Jiao Tong University & University of California, Los Angeles] (2018) arXiv:1812.07997

**RandomOut: Using a convolutional gradient norm to rescue convolutional filters**. [Joseph Paul Cohen, Henry Z. Lo, Wei Ding, MILA] (2019) arXiv:1602.05931 Reddit

**Kervolutional Neural Networks**. Chen Wang, Jianfei Yang, Lihua Xie, Junsong Yuan [Nanyang Technological University, State University of New York at Buffalo] (2019) arXiv:1904.03955 Reddit

**Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks**. H Salehinejad, S Valaee [University of Toronto] (2019) arXiv:1902.08673

**LocalNorm: Robust Image Classification through Dynamically Regularized Normalization**. B Yin, S Schaafsma, H Corporaal, H. S Scholte, S M. Bohte [Centrum Wiskunde & Informatica (CWI) & Holst Centre / IMEC] (2019) arXiv:1902.06550

**On the Impact of the Activation Function on Deep Neural Networks Training**. S Hayou, A Doucet, J Rousseau [Universiy of Oxford] (2019) arXiv:1902.06853 机器之心

**TUNet: Incorporating segmentation maps to improve classification**. Y Tian [New York University] (2019) arXiv:1901.11379

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Batch Normalization / Standardization

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Fenchel-Young Losses

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**Neural Ordinary Differential Equations**. Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud [University of Toronto, Canada] (NeurIPS 2018 | best paper) arXiv:1806.07366 Github Blog 机器之心(【硬核NeruIPS 2018最佳论文，一个神经了的常微分方程】这是一篇神奇的论文，以前一层一层叠加的神经网络似乎突然变得连续了，反向传播也似乎不再需要一点一点往前传、一层一层更新参数了。 )

【《Neural Ordinary Differential Equations》论文解读】《Paper Summary: Neural Ordinary Differential Equations》by Branislav Holländer http://t.cn/EGPEh8y

summary by Adrian Colyer http://t.cn/EqANCZ0

【神经常微分方程的PyTorch实现与分析(Jupyter Notebooks)】’Neural ODEs - Jupyter notebook with Pytorch implementation and investigation of Neural Ordinary Differential Equations' by Mikhail Surtsukov GitHub: http://t.cn/Ef3Qkw4

【神经常微分方程与对抗攻击】《Neural Ordinary Differential Equations and Adversarial Attacks》by Rajat Vadiraj Dwaraknath http://t.cn/EqW9Anb

【神经微分方程】《Neural Differential Equations - YouTube》by Siraj Raval http://t.cn/EqWCSBN

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**Using Machine Learning to Predict the Evolution of Physics Research**. W Liu, S Saganowski, P Kazienko, S A Cheong [Nanyang Technological University & Wrocław University of Science and Technology] (2018) arXiv:1810.12116

**hep-th**. Y He, V Jejjala, B D. Nelson [University of London & Nankai University & Northeastern University] (2018) arXiv:1807.00735 [comment]

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**Curriculum learning**. Y Bengio, J Louradour, R Collobert, J Weston [Montreal, Fabert, Princeton] (2009) PDF

Graph Neural Networks

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**Deep Learning Multidimensional Projections**. Mateus Espadoto, Nina S. T. Hirata and Alexandru C. Telea (2019) arXiv:1902.07958

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**Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations**. X Steenbrugge, S Leroux, T Verbelen, B Dhoedt [Ghent University] (2018) arXiv:1811.04784

**UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction**. Leland McInnes and John Healy [Tutte Institute for Mathematics and Computing] (2018) arXiv:1802.03426 Github

Need to be reviewed.....

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