“‘CNN’ Tag”,2019-09-10

Bibliography for tag ai/nn/cnn, most recent first: 4 related tags, 376 annotations, & 29 links (parent).

• See Also
• Gwern
  • “InvertOrNot.com Proposal”, Gwern2021
• Links
  • “Convolutional Differentiable Logic Gate Networks”, Petersen et al 2024
  • “MaskBit: Embedding-Free Image Generation via Bit Tokens”, Weber et al 2024
  • “Quantum Convolutional Neural Networks Are (Effectively) Classically Simulable”, Bermejo et al 2024
  • “Three-Dimension Animation Character Design Based on Probability Genetic Algorithm”, Gao2024
  • “Investigating Learning-Independent Abstract Reasoning in Artificial Neural Networks”, Barak & Loewenstein2024
  • “Grokfast: Accelerated Grokking by Amplifying Slow Gradients”, Lee et al 2024
  • “A Rotation and a Translation Suffice: Fooling CNNs With Simple Transformations”, Engstrom et al 2024
  • “Neural Networks Learn Statistics of Increasing Complexity”, Belrose et al 2024
  • “Machine Learning Reveals the Control Mechanics of an Insect Wing Hinge”, Melis et al 2024
  • “Supplementary Materials for Grounded Language Acquisition through the Eyes and Ears of a Single Child”, Vong2024
  • “Grounded Language Acquisition through the Eyes and Ears of a Single Child”, Vong et al 2024
  • “Machine Learning As a Tool for Hypothesis Generation”, Jens & Sendhil2024
  • “Multi Visual Feature Fusion Based Fog Visibility Estimation for Expressway Surveillance Using Deep Learning Network”, Yang et al 2023
  • “Auditing the Inference Processes of Medical-Image Classifiers by Leveraging Generative AI and the Expertise of Physicians”, DeGrave et al 2023
  • “Development of Deep Ensembles to Screen for Autism and Symptom Severity Using Retinal Photographs”, Kim et al 2023
  • “May the Noise Be With You: Adversarial Training without Adversarial Examples”, Arous et al 2023
  • “Are Vision Transformers More Data Hungry Than Newborn Visual Systems?”, Pandey et al 2023
  • “UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition”, Ding et al 2023
  • “The Possibility of Making $138,000 from Shredded Banknote Pieces Using Computer Vision”, Kong2023
  • “ConvNets Match Vision Transformers at Scale”, Smith et al 2023
  • “Interpret Vision Transformers As ConvNets With Dynamic Convolutions”, Zhou et al 2023
  • “Artificial Intelligence-Supported Screen Reading versus Standard Double Reading in the Mammography Screening With Artificial Intelligence Trial (MASAI): a Clinical Safety Analysis of a Randomised, Controlled, Non-Inferiority, Single-Blinded, Screening Accuracy Study”, Lång et al 2023
  • “Hand-Drawn Anime Line Drawing Colorization of Faces With Texture Details”, Akita et al 2023
  • “High-Quality Synthetic Character Image Extraction via Distortion Recognition”, Sawada et al 2023
  • “Loss of Plasticity in Deep Continual Learning (Continual Backpropagation)”, Dohare et al 2023
  • “Neural Networks Trained With SGD Learn Distributions of Increasing Complexity”, Refinetti et al 2023
  • “Rosetta Neurons: Mining the Common Units in a Model Zoo”, Dravid et al 2023
  • “Improving Neural Network Representations Using Human Similarity Judgments”, Muttenthaler et al 2023
  • “U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning”, Hsu & Serrão2023
  • “VanillaNet: the Power of Minimalism in Deep Learning”, Chen et al 2023
  • “Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships”, Lan et al 2023
  • “ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification”, Taesiri et al 2023
  • “Hierarchical Multi-Label Attribute Classification With Graph Convolutional Networks on Anime Illustration”, Lan et al 2023
  • “Loss Landscapes Are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent”, Chiang et al 2023
  • “Adding Conditional Control to Text-To-Image Diffusion Models”, Zhang et al 2023
  • “Pruning Compact ConvNets for Efficient Inference”, Ghosh et al 2023
  • “Does Progress on ImageNet Transfer to Real-World Datasets?”, Fang et al 2023
  • “EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers”, Mahdad et al 2022
  • “Pretraining Without Attention”, Wang et al 2022
  • “What Do Vision Transformers Learn? A Visual Exploration”, Ghiasi et al 2022
  • “A 64-Core Mixed-Signal In-Memory Compute Chip Based on Phase-Change Memory for Deep Neural Network Inference”, Gallo et al 2022
  • “Simulated Automated Facial Recognition Systems As Decision-Aids in Forensic Face Matching Tasks”, Carragher & Hancock2022
  • “Interpreting Neural Networks through the Polytope Lens”, Black et al 2022
  • “Predicting Sex, Age, General Cognition and Mental Health With Machine Learning on Brain Structural Connectomes”, Yeung et al 2022
  • “The Power of Ensembles for Active Learning in Image Classification”, Beluch et al 2022
  • “GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features”, Lan et al 2022
  • “The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Kocsis et al 2022
  • “Understanding the Covariance Structure of Convolutional Filters”, Trockman et al 2022
  • “VICRegL: Self-Supervised Learning of Local Visual Features”, Bardes et al 2022
  • “Omnigrok: Grokking Beyond Algorithmic Data”, Liu et al 2022
  • “g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints”, Peebles et al 2022
  • “FastSiam: Resource-Efficient Self-Supervised Learning on a Single GPU”, Pototzky et al 2022
  • “Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease With Brain MRI”, Dhinagar et al 2022
  • “Reassessing Hierarchical Correspondences between Brain and Deep Networks through Direct Interface”, Sexton & Love2022
  • “Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series”, Whitehouse & Schrider2022
  • “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Mindermann et al 2022
  • “BigVGAN: A Universal Neural Vocoder With Large-Scale Training”, Lee et al 2022
  • “Studying Growth With Neural Cellular Automata”, Greydanus2022
  • “Continual Pre-Training Mitigates Forgetting in Language and Vision”, Cossu et al 2022
  • “Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (RepLKNet)”, Ding et al 2022
  • “Democratizing Contrastive Language-Image Pre-Training: A CLIP Benchmark of Data, Model, and Supervision”, Cui et al 2022
  • “Variational Autoencoders Without the Variation”, Daly et al 2022
  • “On the Effectiveness of Dataset Watermarking in Adversarial Settings”, Tekgul & Asokan2022
  • “General Cyclical Training of Neural Networks”, Smith2022
  • “Approximating CNNs With Bag-Of-Local-Features Models Works Surprisingly Well on ImageNet”, Wiel et al 2022
  • “Variational Neural Cellular Automata”, Palm et al 2022
  • “ConvMixer: Patches Are All You Need?”, Trockman & Kolter2022
  • “HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning”, Zhmoginov et al 2022
  • “ConvNeXt: A ConvNet for the 2020s”, Liu et al 2022
  • “An Empirical Investigation of the Role of Pre-Training in Lifelong Learning”, Mehta et al 2021
  • “Noether Networks: Meta-Learning Useful Conserved Quantities”, Alet et al 2021
  • “AugMax: Adversarial Composition of Random Augmentations for Robust Training”, Wang et al 2021
  • “The Efficiency Misnomer”, Dehghani et al 2021
  • “Logical Activation Functions: Logit-Space Equivalents of Probabilistic Boolean Operators”, Lowe et al 2021
  • “Evaluating Loss Functions for Illustration Super-Resolution Neural Networks”, Nepomuceno & Silva2021
  • “TWIST: Self-Supervised Learning by Estimating Twin Class Distributions”, Wang et al 2021
  • “Deep Learning Models of Cognitive Processes Constrained by Human Brain Connectomes”, Zhang et al 2021
  • “Decoupled Contrastive Learning”, Yeh et al 2021
  • “Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-Training Paradigm (DeCLIP)”, Li et al 2021
  • “Mining for Strong Gravitational Lenses With Self-Supervised Learning”, Stein et al 2021
  • “THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks”, Muttenthaler & Hebart2021
  • “A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP”, Zhao et al 2021
  • “Predicting Phenotypes from Genetic, Environment, Management, and Historical Data Using CNNs”, Washburn et al 2021
  • “Do Vision Transformers See Like Convolutional Neural Networks?”, Raghu et al 2021
  • “Dataset Distillation With Infinitely Wide Convolutional Networks”, Nguyen et al 2021
  • “Neuroprosthesis for Decoding Speech in a Paralyzed Person With Anarthria”, Moses et al 2021
  • “Graph Jigsaw Learning for Cartoon Face Recognition”, Li et al 2021
  • “Prediction Depth: Deep Learning Through the Lens of Example Difficulty”, Baldock et al 2021
  • “Revisiting the Calibration of Modern Neural Networks”, Minderer et al 2021
  • “Partial Success in Closing the Gap between Human and Machine Vision”, Geirhos et al 2021
  • “CoAtNet: Marrying Convolution and Attention for All Data Sizes”, Dai et al 2021
  • “Effect of Pre-Training Scale on Intra/Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images”, Cherti & Jitsev2021
  • “Embracing New Techniques in Deep Learning for Estimating Image Memorability”, Needell & Bainbridge2021
  • “Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning”, Korot et al 2021
  • “Rethinking and Improving the Robustness of Image Style Transfer”, Wang et al 2021
  • “Rip Van Winkle’s Razor, a Simple New Estimate for Adaptive Data Analysis”, Arora & Zhang2021
  • “The Surprising Impact of Mask-Head Architecture on Novel Class Segmentation”, Birodkar et al 2021
  • “Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, Northcutt et al 2021
  • “ConViT: Improving Vision Transformers With Soft Convolutional Inductive Biases”, d’Ascoli et al 2021
  • “Learning from Videos to Understand the World”, Zweig et al 2021
  • “Fast and Accurate Model Scaling”, Dollár et al 2021
  • “Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants”, Soemers et al 2021
  • “Momentum Residual Neural Networks”, Sander et al 2021
  • “Hiding Data Hiding”, Wu et al 2021
  • “Explaining Neural Scaling Laws”, Bahri et al 2021
  • “NFNet: High-Performance Large-Scale Image Recognition Without Normalization”, Brock et al 2021
  • “Brain2Pix: Fully Convolutional Naturalistic Video Reconstruction from Brain Activity”, Le et al 2021
  • “Words As a Window: Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks”, Dharmaretnam et al 2021
  • “E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials”, Batzner et al 2021
  • “Meta Pseudo Labels”, Pham et al 2021
  • “Is MLP-Mixer a CNN in Disguise? As Part of This Blog Post, We Look at the MLP Mixer Architecture in Detail and Also Understand Why It Is Not Considered Convolution Free.”
  • “Converting Tabular Data into Images for Deep Learning With Convolutional Neural Networks”, Zhu et al 2021
  • “Taming Transformers for High-Resolution Image Synthesis”, Esser et al 2020
  • “Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge”, Couvy-Duchesne et al 2020
  • “Scaling down Deep Learning”, Greydanus2020
  • “Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, Child2020
  • “Understanding RL Vision: With Diverse Environments, We Can Analyze, Diagnose and Edit Deep Reinforcement Learning Models Using Attribution”, Hilton et al 2020
  • “Fourier Neural Operator for Parametric Partial Differential Equations”, Li et al 2020
  • “Deep Learning-Based Classification of the Polar Emotions of ‘Moe’-Style Cartoon Pictures”, Cao et al 2020b
  • “Sharpness-Aware Minimization (SAM) for Efficiently Improving Generalization”, Foret et al 2020
  • “Demonstrating That Dataset Domains Are Largely Linearly Separable in the Feature Space of Common CNNs”, Dragan2020
  • “Optimal Peanut Butter and Banana Sandwiches”, Rosenthal2020
  • “Accuracy and Performance Comparison of Video Action Recognition Approaches”, Hutchinson et al 2020
  • “A Digital Biomarker of Diabetes from Smartphone-Based Vascular Signals”, Avram et al 2020
  • “SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities”, Morera et al 2020
  • “On Robustness and Transferability of Convolutional Neural Networks”, Djolonga et al 2020
  • “NVAE: A Deep Hierarchical Variational Autoencoder”, Vahdat & Kautz2020
  • “CoCoNuT: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair”, Lutellier et al 2020
  • “The Many Faces of Robustness: A Critical Analysis of Out-Of-Distribution Generalization”, Hendrycks et al 2020
  • “SimCLRv2: Big Self-Supervised Models Are Strong Semi-Supervised Learners”, Chen et al 2020
  • “FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining”, Dai et al 2020
  • “Danny Hernandez on Forecasting and the Drivers of AI Progress”, Koehler et al 2020
  • “Measuring the Algorithmic Efficiency of Neural Networks”, Hernandez & Brown2020
  • “AI and Efficiency: We’re Releasing an Analysis Showing That Since 2012 the Amount of Compute Needed to Train a Neural Net to the Same Performance on ImageNet Classification Has Been Decreasing by a Factor of 2 Every 16 Months”, Hernandez & Brown2020
  • “Reinforcement Learning With Augmented Data”, Laskin et al 2020
  • “YOLOv4: Optimal Speed and Accuracy of Object Detection”, Bochkovskiy et al 2020
  • “Scaling Laws from the Data Manifold Dimension”, Sharma & Kaplan2020
  • “Shortcut Learning in Deep Neural Networks”, Geirhos et al 2020
  • “Evolving Normalization-Activation Layers”, Liu et al 2020
  • “Conditional Convolutions for Instance Segmentation”, Tian et al 2020
  • “Train-By-Reconnect: Decoupling Locations of Weights from Their Values (LaPerm)”, Qiu & Suda2020
  • “Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited”, Maddox et al 2020
  • “Do We Need Zero Training Loss After Achieving Zero Training Error?”, Ishida et al 2020
  • “Bayesian Deep Learning and a Probabilistic Perspective of Generalization”, Wilson & Izmailov2020
  • “A Simple Framework for Contrastive Learning of Visual Representations”, Chen et al 2020
  • “Growing Neural Cellular Automata: Differentiable Model of Morphogenesis”, Mordvintsev et al 2020
  • “Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving”, Song et al 2020
  • “First-In-Human Evaluation of a Hand-Held Automated Venipuncture Device for Rapid Venous Blood Draws”, Leipheimer et al 2020
  • “ImageNet-A: Natural Adversarial Examples”, Hendrycks et al 2020
  • “Deep-Eyes: Fully Automatic Anime Character Colorization With Painting of Details on Empty Pupils”, Akita et al 2020
  • “CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution”, Li2020
  • “The Importance of Deconstruction”, Weinberger2020
  • “Big Transfer (BiT): General Visual Representation Learning”, Kolesnikov et al 2019
  • “Linear Mode Connectivity and the Lottery Ticket Hypothesis”, Frankle et al 2019
  • “Dynamic Convolution: Attention over Convolution Kernels”, Chen et al 2019
  • “Deep Double Descent: We Show That the Double Descent Phenomenon Occurs in CNNs, ResNets, and Transformers: Performance First Improves, Then Gets Worse, and Then Improves Again With Increasing Model Size, Data Size, or Training Time”, Nakkiran et al 2019
  • “Fantastic Generalization Measures and Where to Find Them”, Jiang et al 2019
  • “Anonymous Market Product Classification Based on Deep Learning”, Yang et al 2019b
  • “The Origins and Prevalence of Texture Bias in Convolutional Neural Networks”, Hermann et al 2019
  • “How Machine Learning Can Help Unlock the World of Ancient Japan”, Lamb2019
  • “SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning”, Wang et al 2019
  • “Self-Training With Noisy Student Improves ImageNet Classification”, Xie et al 2019
  • “Taxonomy of Real Faults in Deep Learning Systems”, Humbatova et al 2019
  • “On the Measure of Intelligence”, Chollet2019
  • “DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames”, Wijmans et al 2019
  • “Accelerating Deep Learning by Focusing on the Biggest Losers”, Jiang et al 2019
  • “ANIL: Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML”, Raghu et al 2019
  • “ObjectNet: A Large-Scale Bias-Controlled Dataset for Pushing the Limits of Object Recognition Models”, Barbu et al 2019
  • “CAR: Learned Image Downscaling for Upscaling Using Content Adaptive Resampler”, Sun & Chen2019
  • “Finding the Needle in the Haystack With Convolutions: on the Benefits of Architectural Bias”, d’Ascoli et al 2019
  • “Intriguing Properties of Adversarial Training at Scale”, Xie & Yuille2019
  • “Adversarial Robustness As a Prior for Learned Representations”, Engstrom et al 2019
  • “Human-Level Performance in 3D Multiplayer Games With Population-Based Reinforcement Learning”, Jaderberg et al 2019
  • “ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power”, Wang et al 2019
  • “Cold Case: The Lost MNIST Digits”, Yadav & Bottou2019
  • “Improved Object Recognition Using Neural Networks Trained to Mimic the Brain’s Statistical Properties”, Federer et al 2019
  • “Neural System Identification With Neural Information Flow”, Seeliger et al 2019
  • “Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning”, Din et al 2019
  • “CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features”, Yun et al 2019
  • “Adversarial Examples Are Not Bugs, They Are Features”, Ilyas et al 2019
  • “Searching for MobileNetV3”, Howard et al 2019
  • “Billion-Scale Semi-Supervised Learning for Image Classification”, Yalniz et al 2019
  • “A Recipe for Training Neural Networks”, Karpathy2019
  • “NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection”, Ghiasi et al 2019
  • “COCO-GAN: Generation by Parts via Conditional Coordinating”, Lin et al 2019
  • “Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, Hendrycks & Dietterich2019
  • “Semantic Image Synthesis With Spatially-Adaptive Normalization”, Park et al 2019
  • “The Bitter Lesson”, Sutton2019
  • “Learning To Follow Directions in Street View”, Hermann et al 2019
  • “SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data”, Sun et al 2019
  • “Real-Time Continuous Transcription With Live Transcribe”, Savla2019
  • “Do We Train on Test Data? Purging CIFAR of Near-Duplicates”, Barz & Denzler2019
  • “Pay Less Attention With Lightweight and Dynamic Convolutions”, Wu et al 2019
  • “Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition”, Winkler et al 2019
  • “Detecting Advertising on Building Façades With Computer Vision”, Bochkarev & Smirnov2019
  • “On Lazy Training in Differentiable Programming”, Chizat et al 2018
  • “Quantifying Generalization in Reinforcement Learning”, Cobbe et al 2018
  • “ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware”, Cai et al 2018
  • “ImageNet-Trained CNNs Are Biased towards Texture; Increasing Shape Bias Improves Accuracy and Robustness”, Geirhos et al 2018
  • “Evolving Space-Time Neural Architectures for Videos”, Piergiovanni et al 2018
  • “ADNet: A Deep Network for Detecting Adverts”, Hossari et al 2018
  • “AdVersarial: Perceptual Ad Blocking Meets Adversarial Machine Learning”, Tramèr et al 2018
  • “FloWaveNet: A Generative Flow for Raw Audio”, Kim et al 2018
  • “StreetNet: Preference Learning With Convolutional Neural Network on Urban Crime Perception”, Fu et al 2018
  • “Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation”, Wang et al 2018
  • “Understanding and Correcting Pathologies in the Training of Learned Optimizers”, Metz et al 2018
  • “Graph Convolutional Reinforcement Learning”, Jiang et al 2018
  • “Cellular Automata As Convolutional Neural Networks”, Gilpin2018
  • “Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization”, Narayan et al 2018
  • “Human-Like Playtesting With Deep Learning”, Gudmundsson et al 2018
  • “CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Guo et al 2018
  • “MnasNet: Platform-Aware Neural Architecture Search for Mobile”, Tan et al 2018
  • “LEO: Meta-Learning With Latent Embedding Optimization”, Rusu et al 2018
  • “Glow: Generative Flow With Invertible 1×1 Convolutions”, Kingma & Dhariwal2018
  • “The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes”, Fort & Scherlis2018
  • “Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, Hendrycks & Dietterich2018
  • “Confounding Variables Can Degrade Generalization Performance of Radiological Deep Learning Models”, Zech et al 2018
  • “Faster SGD Training by Minibatch Persistency”, Fischetti et al 2018
  • “Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks”, Xiao et al 2018
  • “Resource-Efficient Neural Architect”, Zhou et al 2018
  • “More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Calandra et al 2018
  • “Deep Learning Generalizes Because the Parameter-Function Map Is Biased towards Simple Functions”, Valle-Pérez et al 2018
  • “Bidirectional Learning for Robust Neural Networks”, Pontes-Filho & Liwicki2018
  • “BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning”, Yu et al 2018
  • “Self-Distillation: Born Again Neural Networks”, Furlanello et al 2018
  • “Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data”, Jean et al 2018
  • “Exploring the Limits of Weakly Supervised Pretraining”, Mahajan et al 2018
  • “YOLOv3: An Incremental Improvement”, Redmon & Farhadi2018
  • “Reptile: On First-Order Meta-Learning Algorithms”, Nichol et al 2018
  • “Essentially No Barriers in Neural Network Energy Landscape”, Draxler et al 2018
  • “Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs”, Garipov et al 2018
  • “Guess, Check and Fix: a Phenomenology of Improvisation in ‘Neural’ Painting”, Choi2018
  • “Sim-To-Real Optimization of Complex Real World Mobile Network With Imperfect Information via Deep Reinforcement Learning from Self-Play”, Tan et al 2018
  • “Evolved Policy Gradients”, Houthooft et al 2018
  • “Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-Of-The-Art Deep Artificial Neural Networks”, Rajalingham et al 2018
  • “IMPALA: Scalable Distributed Deep-RL With Importance Weighted Actor-Learner Architectures”, Espeholt et al 2018
  • “Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, Zhou et al 2018
  • “ArcFace: Additive Angular Margin Loss for Deep Face Recognition”, Deng et al 2018
  • “Man against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists”, Haenssle et al 2018
  • “DeepGS: Predicting Phenotypes from Genotypes Using Deep Learning”, Ma et al 2017
  • “Deep Image Reconstruction from Human Brain Activity”, Shen et al 2017
  • “Visualizing the Loss Landscape of Neural Nets”, Li et al 2017
  • “SPP-Net: Deep Absolute Pose Regression With Synthetic Views”, Purkait et al 2017
  • “China’s AI Advances Help Its Tech Industry, and State Security”, Mozur & Bradsher2017
  • “Measuring the Tendency of CNNs to Learn Surface Statistical Regularities”, Jo & Bengio2017
  • “3D Semantic Segmentation With Submanifold Sparse Convolutional Networks”, Graham et al 2017
  • “BlockDrop: Dynamic Inference Paths in Residual Networks”, Wu et al 2017
  • “Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN”, Gao et al 2017
  • “The Signature of Robot Action Success in EEG Signals of a Human Observer: Decoding and Visualization Using Deep Convolutional Neural Networks”, Behncke et al 2017
  • “11K Hands: Gender Recognition and Biometric Identification Using a Large Dataset of Hand Images”, Afifi2017
  • “Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, Sabatelli2017 (page 3)
  • “Learning to Generalize: Meta-Learning for Domain Generalization”, Li et al 2017
  • “High-Precision Automated Reconstruction of Neurons With Flood-Filling Networks”, Januszewski et al 2017
  • “Efficient K-Shot Learning With Regularized Deep Networks”, Yoo et al 2017
  • “NIMA: Neural Image Assessment”, Talebi & Milanfar2017
  • “Squeeze-And-Excitation Networks”, Hu et al 2017
  • “What Does a Convolutional Neural Network Recognize in the Moon?”, Shoji2017
  • “SMASH: One-Shot Model Architecture Search through HyperNetworks”, Brock et al 2017
  • “BitNet: Bit-Regularized Deep Neural Networks”, Raghavan et al 2017
  • “A Deep Architecture for Unified Esthetic Prediction”, Murray & Gordo2017
  • “Learning With Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback”, Li et al 2017
  • “WebVision Database: Visual Learning and Understanding from Web Data”, Li et al 2017
  • “Focal Loss for Dense Object Detection”, Lin et al 2017
  • “Active Learning for Convolutional Neural Networks: A Core-Set Approach”, Sener & Savarese2017
  • “Learning to Infer Graphics Programs from Hand-Drawn Images”, Ellis et al 2017
  • “A Downsampled Variant of ImageNet As an Alternative to the CIFAR Datasets”, Chrabaszcz et al 2017
  • “Learning Transferable Architectures for Scalable Image Recognition”, Zoph et al 2017
  • “Efficient Architecture Search by Network Transformation”, Cai et al 2017
  • “A Simple Neural Attentive Meta-Learner”, Mishra et al 2017
  • “Towards Deep Learning Models Resistant to Adversarial Attacks”, Madry et al 2017
  • “Gradient Diversity: a Key Ingredient for Scalable Distributed Learning”, Yin et al 2017
  • “Device Placement Optimization With Reinforcement Learning”, Mirhoseini et al 2017
  • “Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour”, Goyal et al 2017
  • “Submanifold Sparse Convolutional Networks”, Graham & Maaten2017
  • “A Simple Neural Network Module for Relational Reasoning”, Santoro et al 2017
  • “Deep Learning Is Robust to Massive Label Noise”, Rolnick et al 2017
  • “What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features”, Zhang et al 2017
  • “Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers”, Henderson & Rothe2017
  • “BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography”, Wilber et al 2017
  • “Adversarial Neural Machine Translation”, Wu et al 2017
  • “Multi-Scale Dense Networks for Resource Efficient Image Classification”, Huang et al 2017
  • “Scaling the Scattering Transform: Deep Hybrid Networks”, Oyallon et al 2017
  • “Mask R-CNN”, He et al 2017
  • “Using Human Brain Activity to Guide Machine Learning”, Fong et al 2017
  • “Learned Optimizers That Scale and Generalize”, Wichrowska et al 2017
  • “Prediction and Control With Temporal Segment Models”, Mishra et al 2017
  • “Parallel Multiscale Autoregressive Density Estimation”, Reed et al 2017
  • “Convolution Aware Initialization”, Aghajanyan2017
  • “Gender-From-Iris or Gender-From-Mascara?”, Kuehlkamp et al 2017
  • “BrainNetCNN: Convolutional Neural Networks for Brain Networks; towards Predicting Neurodevelopment”, Kawahara et al 2017
  • “Universal Representations: The Missing Link between Faces, Text, Planktons, and Cat Breeds”, Bilen & Vedaldi2017
  • “PixelCNN++: Improving the PixelCNN With Discretized Logistic Mixture Likelihood and Other Modifications”, Salimans et al 2017
  • “YOLO9000: Better, Faster, Stronger”, Redmon & Farhadi2016
  • “Language Modeling With Gated Convolutional Networks”, Dauphin et al 2016
  • “Learning from Simulated and Unsupervised Images through Adversarial Training”, Shrivastava et al 2016
  • “LipNet: End-To-End Sentence-Level Lipreading”, Assael et al 2016
  • “Feature Pyramid Networks for Object Detection”, Lin et al 2016
  • “Self-Critical Sequence Training for Image Captioning”, Rennie et al 2016
  • “ResNeXt: Aggregated Residual Transformations for Deep Neural Networks”, Xie et al 2016
  • “Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of ArXiv:1611_413ya.04135)”, Wu & Zhang2016
  • “Understanding Deep Learning Requires Rethinking Generalization”, Zhang et al 2016
  • “Designing Neural Network Architectures Using Reinforcement Learning”, Baker et al 2016
  • “VPN: Video Pixel Networks”, Kalchbrenner et al 2016
  • “HyperNetworks”, Ha et al 2016
  • “Neural Photo Editing With Introspective Adversarial Networks”, Brock et al 2016
  • “On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima”, Keskar et al 2016
  • “WaveNet: A Generative Model for Raw Audio”, Oord et al 2016
  • “Direct Feedback Alignment Provides Learning in Deep Neural Networks”, Nøkland2016
  • “Deep Learning Human Mind for Automated Visual Classification”, Spampinato et al 2016
  • “Temporal Convolutional Networks: A Unified Approach to Action Segmentation”, Lea et al 2016
  • “DenseNet: Densely Connected Convolutional Networks”, Huang et al 2016
  • “Clockwork Convnets for Video Semantic Segmentation”, Shelhamer et al 2016
  • “Deep Learning the City: Quantifying Urban Perception At A Global Scale”, Dubey et al 2016
  • “Convolutional Neural Fabrics”, Saxena & Verbeek2016
  • “Deep Neural Networks Are Robust to Weight Binarization and Other Non-Linear Distortions”, Merolla et al 2016
  • “DeepLab: Semantic Image Segmentation With Deep Convolutional Nets, Atrous Convolution (ASPP), and Fully Connected CRFs”, Chen et al 2016
  • “FractalNet: Ultra-Deep Neural Networks without Residuals”, Larsson et al 2016
  • “Wide Residual Networks”, Zagoruyko & Komodakis2016
  • “Residual Networks Behave Like Ensembles of Relatively Shallow Networks”, Veit et al 2016
  • “Neural Autoregressive Distribution Estimation”, Uria et al 2016
  • “ViZDoom: A Doom-Based AI Research Platform for Visual Reinforcement Learning”, Kempka et al 2016
  • “OHEM: Training Region-Based Object Detectors With Online Hard Example Mining”, Shrivastava et al 2016
  • “Deep Networks With Stochastic Depth”, Huang et al 2016
  • “Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing”, Esser et al 2016
  • “Do Deep Convolutional Nets Really Need to Be Deep and Convolutional?”, Urban et al 2016
  • “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”, Rastegari et al 2016
  • “Learning Hand-Eye Coordination for Robotic Grasping With Deep Learning and Large-Scale Data Collection”, Levine et al 2016
  • “Network Morphism”, Wei et al 2016
  • “Inception-V4, Inception-ResNet and the Impact of Residual Connections on Learning”, Szegedy et al 2016
  • “PlaNet—Photo Geolocation With Convolutional Neural Networks”, Weyand et al 2016
  • “Value Iteration Networks”, Tamar et al 2016
  • “PixelRNN: Pixel Recurrent Neural Networks”, Oord et al 2016
  • “Image Synthesis from Yahoo’s open_nsfw”, Goh2016
  • “Deep Residual Learning for Image Recognition”, He et al 2015
  • “Microsoft Researchers Win ImageNet Computer Vision Challenge”, Linn2015
  • “Adding Gradient Noise Improves Learning for Very Deep Networks”, Neelakantan et al 2015
  • “The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition”, Krause et al 2015
  • “Learning Visual Features from Large Weakly Supervised Data”, Joulin et al 2015
  • “Illustration2Vec: a Semantic Vector Representation of Illustrations”, Masaki & Matsui2015
  • “BinaryConnect: Training Deep Neural Networks With Binary Weights during Propagations”, Courbariaux et al 2015
  • “Predicting and Understanding Urban Perception With Convolutional Neural Networks”, Porzi et al 2015
  • “A Neural Attention Model for Abstractive Sentence Summarization”, Rush et al 2015
  • “LSUN: Construction of a Large-Scale Image Dataset Using Deep Learning With Humans in the Loop”, Yu et al 2015
  • “You Only Look Once: Unified, Real-Time Object Detection”, Redmon et al 2015
  • “Clothing-1M: Learning from Massive Noisy Labeled Data for Image Classification”, Xiao et al 2015
  • “Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, Gal & Ghahramani2015
  • “STN: Spatial Transformer Networks”, Jaderberg et al 2015
  • “Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks”, Ren et al 2015
  • “Cyclical Learning Rates for Training Neural Networks”, Smith2015
  • “Deep Learning”, LeCun et al 2015
  • “Fast R-CNN”, Girshick2015
  • “End-To-End Training of Deep Visuomotor Policies”, Levine et al 2015
  • “FaceNet: A Unified Embedding for Face Recognition and Clustering”, Schroff et al 2015
  • “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”, Ioffe & Szegedy2015
  • “DeepID3: Face Recognition With Very Deep Neural Networks”, Sun et al 2015
  • “Explaining and Harnessing Adversarial Examples”, Goodfellow et al 2014
  • “Understanding Image Representations by Measuring Their Equivariance and Equivalence”, Lenc & Vedaldi2014
  • “Going Deeper With Convolutions”, Szegedy et al 2014
  • “Very Deep Convolutional Networks for Large-Scale Image Recognition”, Simonyan & Zisserman2014
  • “ImageNet Large Scale Visual Recognition Challenge”, Russakovsky et al 2014
  • “Deep Learning Face Representation by Joint Identification-Verification”, Sun et al 2014
  • “One Weird Trick for Parallelizing Convolutional Neural Networks”, Krizhevsky2014
  • “Network In Network”, Lin et al 2013
  • “R-CNN: Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”, Girshick et al 2013
  • “Maxout Networks”, Goodfellow et al 2013
  • “ImageNet Classification With Deep Convolutional Neural Networks”, Krizhevsky et al 2012
  • “Multi-Column Deep Neural Network for Traffic Sign Classification”, Cireşan et al 2012b
  • “Multi-Column Deep Neural Networks for Image Classification”, Cireşan et al 2012
  • “Building High-Level Features Using Large Scale Unsupervised Learning”, Le et al 2011
  • “DanNet: Flexible, High Performance Convolutional Neural Networks for Image Classification”, Ciresan et al 2011
  • “Hypernetworks [Blog]”, Ha2024
  • “Deconvolution and Checkerboard Artifacts”
  • “Hierarchical Object Detection With Deep Reinforcement Learning”
  • “Creating a 17 KB Style Transfer Model With Layer Pruning and Quantization”, Toole2024
  • “Now Anyone Can Train Imagenet in 18 Minutes”
  • “Cats, Rats, A.I., Oh My!”
  • Wikipedia
• Miscellaneous
• Bibliography