Bibliography:

‘neural net’ tag
‘multi-scale Transformers’ tag
‘autoencoder NN’ tag
‘AI scaling’ tag
‘AlphaGo’ tag
InvertOrNot.com Proposal
Convolutional Differentiable Logic Gate Networks
MaskBit: Embedding-free Image Generation via Bit Tokens
Quantum Convolutional Neural Networks are (Effectively) Classically Simulable
Three-Dimension Animation Character Design Based on Probability Genetic Algorithm
Investigating learning-independent abstract reasoning in artificial neural networks
Grokfast: Accelerated Grokking by Amplifying Slow Gradients
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
Neural Networks Learn Statistics of Increasing Complexity
Machine learning reveals the control mechanics of an insect wing hinge
Supplementary Materials for Grounded language acquisition through the eyes and ears of a single child
Grounded language acquisition through the eyes and ears of a single child
Machine Learning as a Tool for Hypothesis Generation
Multi visual feature fusion based fog visibility estimation for expressway surveillance using deep learning network
Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians
Development of Deep Ensembles to Screen for Autism and Symptom Severity Using Retinal Photographs
May the Noise be with you: Adversarial Training without Adversarial Examples
Are Vision Transformers More Data Hungry Than Newborn Visual Systems?
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
The possibility of making $138,000 from shredded banknote pieces using computer vision
ConvNets Match Vision Transformers at Scale
Interpret Vision Transformers as ConvNets with Dynamic Convolutions
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
Hand-drawn anime line drawing colorization of faces with texture details
High-Quality Synthetic Character Image Extraction via Distortion Recognition
Loss of Plasticity in Deep Continual Learning (Continual Backpropagation)
Neural networks trained with SGD learn distributions of increasing complexity
Rosetta Neurons: Mining the Common Units in a Model Zoo
Improving neural network representations using human similarity judgments
U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning
VanillaNet: the Power of Minimalism in Deep Learning
Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification
Hierarchical Multi-Label Attribute Classification With Graph Convolutional Networks on Anime Illustration
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent
Adding Conditional Control to Text-to-Image Diffusion Models
Pruning Compact ConvNets for Efficient Inference
Does progress on ImageNet transfer to real-world datasets?
EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers
Pretraining Without Attention
What do Vision Transformers Learn? A Visual Exploration
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Simulated automated facial recognition systems as decision-aids in forensic face matching tasks
Interpreting Neural Networks through the Polytope Lens
Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes
The Power of Ensembles for Active Learning in Image Classification
GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
Understanding the Covariance Structure of Convolutional Filters
VICRegL: Self-Supervised Learning of Local Visual Features
Omnigrok: Grokking Beyond Algorithmic Data
g.pt: Learning to Learn with Generative Models of Neural Network Checkpoints
FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU
Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease with Brain MRI
Reassessing hierarchical correspondences between brain and deep networks through direct interface
Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series
RHO-LOSS: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
Studying Growth With Neural Cellular Automata
Continual Pre-Training Mitigates Forgetting in Language and Vision
Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (RepLKNet)
Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision
Variational Autoencoders Without the Variation
On the Effectiveness of Dataset Watermarking in Adversarial Settings
General Cyclical Training of Neural Networks
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Variational Neural Cellular Automata
ConvMixer: Patches Are All You Need?
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
ConvNeXt: A ConvNet for the 2020s
An Empirical Investigation of the Role of Pre-training in Lifelong Learning
Noether Networks: Meta-Learning Useful Conserved Quantities
AugMax: Adversarial Composition of Random Augmentations for Robust Training
The Efficiency Misnomer
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators
Evaluating Loss Functions for Illustration Super-Resolution Neural Networks
TWIST: Self-Supervised Learning by Estimating Twin Class Distributions
Deep learning models of cognitive processes constrained by human brain connectomes
Decoupled Contrastive Learning
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm (DeCLIP)
Mining for strong gravitational lenses with self-supervised learning
THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP
Predicting phenotypes from genetic, environment, management, and historical data using CNNs
Do Vision Transformers See Like Convolutional Neural Networks?
Dataset Distillation with Infinitely Wide Convolutional Networks
Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria
Graph Jigsaw Learning for Cartoon Face Recognition
Prediction Depth: Deep Learning Through the Lens of Example Difficulty
Revisiting the Calibration of Modern Neural Networks
Partial success in closing the gap between human and machine vision
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Effect of Pre-Training Scale on Intra/Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images
Embracing New Techniques in Deep Learning for Estimating Image Memorability
Predicting sex from retinal fundus photographs using automated deep learning
Rethinking and Improving the Robustness of Image Style Transfer
Rip van Winkle’s Razor, a Simple New Estimate for Adaptive Data Analysis
The surprising impact of mask-head architecture on novel class segmentation
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Learning from videos to understand the world
Fast and Accurate Model Scaling
Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants
Momentum Residual Neural Networks
Hiding Data Hiding
Explaining Neural Scaling Laws
NFNet: High-Performance Large-Scale Image Recognition Without Normalization
Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity
Words as a window: Using word embeddings to explore the learned representations of Convolutional Neural Networks
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Meta Pseudo Labels
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
Taming Transformers for High-Resolution Image Synthesis
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
Scaling down Deep Learning
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
Understanding RL Vision: With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution
Fourier Neural Operator for Parametric Partial Differential Equations
Deep learning-based classification of the polar emotions of ‘moe’-style cartoon pictures
Sharpness-Aware Minimization (SAM) for Efficiently Improving Generalization
Demonstrating that dataset domains are largely linearly separable in the feature space of common CNNs
Optimal Peanut Butter and Banana Sandwiches
Accuracy and Performance Comparison of Video Action Recognition Approaches
A digital biomarker of diabetes from smartphone-based vascular signals
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities
On Robustness and Transferability of Convolutional Neural Networks
NVAE: A Deep Hierarchical Variational Autoencoder
CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
SimCLRv2: Big Self-Supervised Models are Strong Semi-Supervised Learners
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
Danny Hernandez on forecasting and the drivers of AI progress
Measuring the Algorithmic Efficiency of Neural Networks
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
Reinforcement Learning with Augmented Data
YOLOv4: Optimal Speed and Accuracy of Object Detection
Scaling Laws from the Data Manifold Dimension
Shortcut Learning in Deep Neural Networks
Evolving Normalization-Activation Layers
Conditional Convolutions for Instance Segmentation
Train-by-Reconnect: Decoupling Locations of Weights from their Values (LaPerm)
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Do We Need Zero Training Loss After Achieving Zero Training Error?
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
A Simple Framework for Contrastive Learning of Visual Representations
Growing Neural Cellular Automata: Differentiable Model of Morphogenesis
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving
First-in-human evaluation of a hand-held automated venipuncture device for rapid venous blood draws
ImageNet-A: Natural Adversarial Examples
Deep-Eyes: Fully Automatic Anime Character Colorization with Painting of Details on Empty Pupils
CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution
3fe19144f573eaba2ca1b17d9880bd983a0d0d9a.pdf
The Importance of Deconstruction
Big Transfer (BiT): General Visual Representation Learning
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Dynamic Convolution: Attention over Convolution Kernels
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
Fantastic Generalization Measures and Where to Find Them
Anonymous market product classification based on deep learning
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
How Machine Learning Can Help Unlock the World of Ancient Japan
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Self-training with Noisy Student improves ImageNet classification
Taxonomy of Real Faults in Deep Learning Systems
On the Measure of Intelligence
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
Accelerating Deep Learning by Focusing on the Biggest Losers
ANIL: Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
CAR: Learned Image Downscaling for Upscaling using Content Adaptive Resampler
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Intriguing properties of adversarial training at scale
Adversarial Robustness as a Prior for Learned Representations
Human-level performance in 3D multiplayer games with population-based reinforcement learning
ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power
Cold Case: The Lost MNIST Digits
Improved object recognition using neural networks trained to mimic the brain’s statistical properties
Neural System Identification with Neural Information Flow
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Adversarial Examples Are Not Bugs, They Are Features
Searching for MobileNetV3
Billion-scale semi-supervised learning for image classification
A Recipe for Training Neural Networks
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
COCO-GAN: Generation by Parts via Conditional Coordinating
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Semantic Image Synthesis with Spatially-Adaptive Normalization
The Bitter Lesson
Learning To Follow Directions in Street View
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
Real-time Continuous Transcription with Live Transcribe
Do We Train on Test Data? Purging CIFAR of Near-Duplicates
Pay Less Attention with Lightweight and Dynamic Convolutions
Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition
Detecting advertising on building façades with computer vision
On Lazy Training in Differentiable Programming
Quantifying Generalization in Reinforcement Learning
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Evolving Space-Time Neural Architectures for Videos
ADNet: A Deep Network for Detecting Adverts
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
FloWaveNet: A Generative Flow for Raw Audio
StreetNet: Preference Learning with Convolutional Neural Network on Urban Crime Perception
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
Understanding and correcting pathologies in the training of learned optimizers
Graph Convolutional Reinforcement Learning
Cellular automata as convolutional neural networks
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Human-Like Playtesting with Deep Learning
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
MnasNet: Platform-Aware Neural Architecture Search for Mobile
LEO: Meta-Learning with Latent Embedding Optimization
Glow: Generative Flow with Invertible 1×1 Convolutions
The Goldilocks zone: Towards better understanding of neural network loss landscapes
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
Confounding variables can degrade generalization performance of radiological deep learning models
Faster SGD training by minibatch persistency
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Resource-Efficient Neural Architect
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
Deep learning generalizes because the parameter-function map is biased towards simple functions
Bidirectional Learning for Robust Neural Networks
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Self-distillation: Born Again Neural Networks
Tile2Vec: Unsupervised representation learning for spatially distributed data
Exploring the Limits of Weakly Supervised Pretraining
YOLOv3: An Incremental Improvement
Reptile: On First-Order Meta-Learning Algorithms
Essentially No Barriers in Neural Network Energy Landscape
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Guess, check and fix: a phenomenology of improvisation in ‘neural’ painting
Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play
Evolved Policy Gradients
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Man against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists
DeepGS: Predicting phenotypes from genotypes using Deep Learning
Deep image reconstruction from human brain activity
Visualizing the Loss Landscape of Neural Nets
SPP-Net: Deep Absolute Pose Regression with Synthetic Views
China’s AI Advances Help Its Tech Industry, and State Security
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
BlockDrop: Dynamic Inference Paths in Residual Networks
Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks
Learning to Generalize: Meta-Learning for Domain Generalization
High-Precision Automated Reconstruction of Neurons with Flood-filling Networks
Efficient K-shot Learning with Regularized Deep Networks
NIMA: Neural Image Assessment
Squeeze-and-Excitation Networks
What does a convolutional neural network recognize in the moon?
SMASH: One-Shot Model Architecture Search through HyperNetworks
BitNet: Bit-Regularized Deep Neural Networks
A deep architecture for unified esthetic prediction
Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback
WebVision Database: Visual Learning and Understanding from Web Data
Focal Loss for Dense Object Detection
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Learning to Infer Graphics Programs from Hand-Drawn Images
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
Learning Transferable Architectures for Scalable Image Recognition
Efficient Architecture Search by Network Transformation
A Simple Neural Attentive Meta-Learner
Towards Deep Learning Models Resistant to Adversarial Attacks
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning
Device Placement Optimization with Reinforcement Learning
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Submanifold Sparse Convolutional Networks
A simple neural network module for relational reasoning
Deep Learning is Robust to Massive Label Noise
What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography
Adversarial Neural Machine Translation
Multi-Scale Dense Networks for Resource Efficient Image Classification
Scaling the Scattering Transform: Deep Hybrid Networks
Mask R-CNN
Using Human Brain Activity to Guide Machine Learning
Learned Optimizers that Scale and Generalize
Prediction and Control with Temporal Segment Models
Parallel Multiscale Autoregressive Density Estimation
Convolution Aware Initialization
Gender-From-Iris or Gender-From-Mascara?
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment
Universal representations: The missing link between faces, text, planktons, and cat breeds
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
YOLO9000: Better, Faster, Stronger
Language Modeling with Gated Convolutional Networks
Learning from Simulated and Unsupervised Images through Adversarial Training
LipNet: End-to-End Sentence-level Lipreading
Feature Pyramid Networks for Object Detection
Self-critical Sequence Training for Image Captioning
ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
Understanding deep learning requires rethinking generalization
Designing Neural Network Architectures using Reinforcement Learning
VPN: Video Pixel Networks
HyperNetworks
Neural Photo Editing with Introspective Adversarial Networks
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
WaveNet: A Generative Model for Raw Audio
Direct Feedback Alignment Provides Learning in Deep Neural Networks
Deep Learning Human Mind for Automated Visual Classification
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
DenseNet: Densely Connected Convolutional Networks
Clockwork Convnets for Video Semantic Segmentation
Deep Learning the City: Quantifying Urban Perception At A Global Scale
Convolutional Neural Fabrics
Deep neural networks are robust to weight binarization and other non-linear distortions
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution (ASPP), and Fully Connected CRFs
FractalNet: Ultra-Deep Neural Networks without Residuals
Wide Residual Networks
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Neural Autoregressive Distribution Estimation
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
OHEM: Training Region-based Object Detectors with Online Hard Example Mining
Deep Networks with Stochastic Depth
Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Network Morphism
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
PlaNet—Photo Geolocation with Convolutional Neural Networks
Value Iteration Networks
PixelRNN: Pixel Recurrent Neural Networks
Image Synthesis from Yahoo’s open_nsfw
Deep Residual Learning for Image Recognition
Microsoft researchers win ImageNet computer vision challenge
Adding Gradient Noise Improves Learning for Very Deep Networks
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
Learning Visual Features from Large Weakly Supervised Data
Illustration2Vec: a semantic vector representation of illustrations
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Predicting and Understanding Urban Perception with Convolutional Neural Networks
A Neural Attention Model for Abstractive Sentence Summarization
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
You Only Look Once: Unified, Real-Time Object Detection
Clothing-1M: Learning from Massive Noisy Labeled Data for Image Classification
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
STN: Spatial Transformer Networks
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Cyclical Learning Rates for Training Neural Networks
Deep Learning
Fast R-CNN
End-to-End Training of Deep Visuomotor Policies
FaceNet: A Unified Embedding for Face Recognition and Clustering
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
DeepID3: Face Recognition with Very Deep Neural Networks
Explaining and Harnessing Adversarial Examples
Understanding image representations by measuring their equivariance and equivalence
Going Deeper with Convolutions
Very Deep Convolutional Networks for Large-Scale Image Recognition
ImageNet Large Scale Visual Recognition Challenge
Deep Learning Face Representation by Joint Identification-Verification
One weird trick for parallelizing convolutional neural networks
Network In Network
R-CNN: Rich feature hierarchies for accurate object detection and semantic segmentation
Maxout Networks
ImageNet Classification with Deep Convolutional Neural Networks
Multi-column deep neural network for traffic sign classification
Multi-column Deep Neural Networks for Image Classification
Building high-level features using large scale unsupervised learning
DanNet: Flexible, High Performance Convolutional Neural Networks for Image Classification
Hypernetworks [Blog]
Deconvolution and Checkerboard Artifacts
Hierarchical Object Detection With Deep Reinforcement Learning
f106bf397cea4b3c184a40c91893ee695f7646df.html
Creating a 17 KB Style Transfer Model With Layer Pruning and Quantization
Now Anyone Can Train Imagenet in 18 Minutes
Cats, Rats, A.I., Oh My!
2023-hsu-appendix-acompleteunetnnimplementationinapl.png
2022-mindermann-figure1-18xspeedupfromactivelearningofclothing1mdataset.jpg
2022-smith-table1-cyclicalweightdecaycifarimagenet.png
2020-07-24-gwern-meme-moneyprinter-bitterlesson-gpt3.png
2019-boazbarak-deepdoubledescent-expandedversion-degree1000spline-goodoverfitting.png
2019-humbatova-figure1-taxonomyofrealfaultsindeeplearningsystems.png
2018-mahajan-figure2-imagenetcub2011transferlearningfrominstagramhashtagsscalingcurves.jpg
2017-rawat.pdf
2015-joulin-figure2-flickrpascalvoc2007precisionscalingwithflickr100mnscaling.jpg
https://aclanthology.org/D13-1176.pdf
8ecd1e30c66400dbf395e3f5b7852c8bb70a02d2.pdf
https://animatedai.github.io/
https://frankzliu.com/blog/vision-transformers-are-overrated
ce5cab2244b861ebaefca3f9b0a377b832d07921.html
https://github.com/lukas-blecher/LaTeX-OCR
https://github.com/ultralytics/yolov5/issues/6998#issue-1170533269
https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-023-01094-0
a61009d7a51e4cad361ec5fa9ea1b9298e69b6a0.html
https://invertornot.com/
https://karpathy.github.io/2022/03/14/lecun1989/
https://myrtle.ai/learn/how-to-train-your-resnet/
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4588941
https://schollz.com/tinker/watercolor/
fb6541535fe7e5e0059b867451c7e3e8ed692b17.html
https://wandb.ai/wandb_fc/articles/reports/Image-to-LaTeX--Vmlldzo1NDQ0MTAx
https://www.jerpint.io/blog/snowflake-neural-ca/
7da551ce8c6fb8cdab7d63eec915ec35b6cd26c5.html
https://www.nature.com/articles/s41467-023-41693-w
https://www.nature.com/articles/s42003-023-05098-1
https://www.quantamagazine.org/sparse-neural-networks-point-physicists-to-useful-data-20230608/
6f5ad29694d194976fd23467d00a4bbabcbca622.html
https://www.reddit.com/r/MachineLearning/comments/jthxui/p_chasing_intruding_cats_from_your_home_with/
https://www.reddit.com/r/mlscaling/comments/1ggr0j4/neural_network_recognizer_for_handwritten_zip/
https://www.wired.com/story/beauty-is-in-the-eye-of-the-beholder-but-memorability-may-be-universal/
https://x.com/chriswolfvision/status/1313059518574718977
MaskBit: Embedding-free Image Generation via Bit Tokens
https%253A%252F%252Farxiv.org%252Fabs%252F2409.16211%2523bytedance.html
Grokfast: Accelerated Grokking by Amplifying Slow Gradients
https%253A%252F%252Farxiv.org%252Fabs%252F2405.20233.html
Grounded language acquisition through the eyes and ears of a single child
%252Fdoc%252Fai%252Fnn%252Fcnn%252F2024-vong.pdf.html
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
https%253A%252F%252Farxiv.org%252Fabs%252F2311.15599%2523tencent.html
ConvNets Match Vision Transformers at Scale
https%253A%252F%252Farxiv.org%252Fabs%252F2310.16764%2523deepmind.html
Interpret Vision Transformers as ConvNets with Dynamic Convolutions
https%253A%252F%252Farxiv.org%252Fabs%252F2309.10713.html
Rosetta Neurons: Mining the Common Units in a Model Zoo
https%253A%252F%252Farxiv.org%252Fabs%252F2306.09346.html
U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning
https%253A%252F%252Fdl.acm.org%252Fdoi%252Fpdf%252F10.1145%252F3589246.3595371.html
VanillaNet: the Power of Minimalism in Deep Learning
https%253A%252F%252Farxiv.org%252Fabs%252F2305.12972.html
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification
https%253A%252F%252Farxiv.org%252Fabs%252F2304.05538.html
Hierarchical Multi-Label Attribute Classification With Graph Convolutional Networks on Anime Illustration
https%253A%252F%252Fieeexplore.ieee.org%252Fabstract%252Fdocument%252F10097719.html
Pretraining Without Attention
Junxiong Wang
Albert Gu
https://rush-nlp.com/
https%253A%252F%252Farxiv.org%252Fabs%252F2212.10544.html
What do Vision Transformers Learn? A Visual Exploration
https%253A%252F%252Farxiv.org%252Fabs%252F2212.06727.html
Simulated automated facial recognition systems as decision-aids in forensic face matching tasks
%252Fdoc%252Fai%252Fnn%252Fcnn%252F2022-carragher.pdf.html
GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features
%252Fdoc%252Fai%252Fanime%252Fdanbooru%252F2022-lan.pdf.html
Understanding the Covariance Structure of Convolutional Filters
https%253A%252F%252Farxiv.org%252Fabs%252F2210.03651.html
Omnigrok: Grokking Beyond Algorithmic Data
https%253A%252F%252Farxiv.org%252Fabs%252F2210.01117.html
g.pt: Learning to Learn with Generative Models of Neural Network Checkpoints
https%253A%252F%252Farxiv.org%252Fabs%252F2209.12892.html
FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU
%252Fdoc%252Fai%252Fnn%252Fcnn%252F2022-pototzky.pdf.html
Reassessing hierarchical correspondences between brain and deep networks through direct interface
https%253A%252F%252Fwww.science.org%252Fdoi%252F10.1126%252Fsciadv.abm2219.html
RHO-LOSS: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt
https%253A%252F%252Farxiv.org%252Fabs%252F2206.07137.html
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
https%253A%252F%252Farxiv.org%252Fabs%252F2206.04658%2523nvidia.html
Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (RepLKNet)
https%253A%252F%252Farxiv.org%252Fabs%252F2203.06717.html
General Cyclical Training of Neural Networks
https%253A%252F%252Farxiv.org%252Fabs%252F2202.08835.html
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Matthias Bethge
https%253A%252F%252Fopenreview.net%252Fforum%253Fid%253DSkfMWhAqYQ.html
ConvMixer: Patches Are All You Need?
https%253A%252F%252Farxiv.org%252Fabs%252F2201.09792.html
ConvNeXt: A ConvNet for the 2020s
Zhuang Liu’s Homepage
https%253A%252F%252Farxiv.org%252Fabs%252F2201.03545%2523facebook.html
AugMax: Adversarial Composition of Random Augmentations for Robust Training
https%253A%252F%252Farxiv.org%252Fabs%252F2110.13771%2523nvidia.html
TWIST: Self-Supervised Learning by Estimating Twin Class Distributions
https%253A%252F%252Farxiv.org%252Fabs%252F2110.07402%2523bytedance.html
Decoupled Contrastive Learning
https%253A%252F%252Farxiv.org%252Fabs%252F2110.06848.html
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm (DeCLIP)
https%253A%252F%252Farxiv.org%252Fabs%252F2110.05208.html
THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks
https%253A%252F%252Fwww.frontiersin.org%252Farticles%252F10.3389%252Ffninf.2021.679838%252Ffull.html
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP
https%253A%252F%252Farxiv.org%252Fabs%252F2108.13002%2523microsoft.html
Do Vision Transformers See Like Convolutional Neural Networks?
https%253A%252F%252Farxiv.org%252Fabs%252F2108.08810%2523google.html
Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria
%252Fdoc%252Fai%252Fnn%252Fcnn%252F2021-moses.pdf.html
Partial success in closing the gap between human and machine vision
Robert Geirhos
Matthias Bethge
https%253A%252F%252Farxiv.org%252Fabs%252F2106.07411.html
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Zihang Dai
https%253A%252F%252Farxiv.org%252Fabs%252F2106.04803%2523google.html
Effect of Pre-Training Scale on Intra/Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images
https%253A%252F%252Farxiv.org%252Fabs%252F2106.00116.html
Rethinking and Improving the Robustness of Image Style Transfer
https%253A%252F%252Farxiv.org%252Fabs%252F2104.05623.html
Rip van Winkle’s Razor, a Simple New Estimate for Adaptive Data Analysis
https%253A%252F%252Fwww.offconvex.org%252F2021%252F04%252F07%252Fripvanwinkle%252F.html
The surprising impact of mask-head architecture on novel class segmentation
https%253A%252F%252Farxiv.org%252Fabs%252F2104.00613.html
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
https%253A%252F%252Farxiv.org%252Fabs%252F2103.14749.html
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
https%253A%252F%252Farxiv.org%252Fabs%252F2103.10697%2523facebook.html
Learning from videos to understand the world
Polina Kuznetsova
https%253A%252F%252Fai.facebook.com%252Fblog%252Flearning-from-videos-to-understand-the-world%252F.html
NFNet: High-Performance Large-Scale Image Recognition Without Normalization
Karen Simonyan
https%253A%252F%252Farxiv.org%252Fabs%252F2102.06171%2523deepmind.html
Meta Pseudo Labels
Zihang Dai
https%253A%252F%252Farxiv.org%252Fabs%252F2003.10580%2523google.html
Scaling down Deep Learning
About Sam Greydanus
https%253A%252F%252Fgreydanus.github.io%252F2020%252F12%252F01%252Fscaling-down%252F.html
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
https%253A%252F%252Farxiv.org%252Fabs%252F2011.10650%2523openai.html
Sharpness-Aware Minimization (SAM) for Efficiently Improving Generalization
Behnam Neyshabur
https%253A%252F%252Farxiv.org%252Fabs%252F2010.01412%2523google.html
Optimal Peanut Butter and Banana Sandwiches
https%253A%252F%252Fwww.ethanrosenthal.com%252F2020%252F08%252F25%252Foptimal-peanut-butter-and-banana-sandwiches%252F.html
Accuracy and Performance Comparison of Video Action Recognition Approaches
https%253A%252F%252Farxiv.org%252Fabs%252F2008.09037.html
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities
https%253A%252F%252Fwww.mdpi.com%252F1424-8220%252F20%252F16%252F4587.html
NVAE: A Deep Hierarchical Variational Autoencoder
https%253A%252F%252Farxiv.org%252Fabs%252F2007.03898%2523nvidia.html
YOLOv4: Optimal Speed and Accuracy of Object Detection
https%253A%252F%252Farxiv.org%252Fabs%252F2004.10934.html
Scaling Laws from the Data Manifold Dimension
Jared Kaplan
https%253A%252F%252Farxiv.org%252Fabs%252F2004.10802.html
A Simple Framework for Contrastive Learning of Visual Representations
https%253A%252F%252Farxiv.org%252Fabs%252F2002.05709%2523google.html
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving
Stefano Ermon
https%253A%252F%252Farxiv.org%252Fabs%252F2002.03629.html
The Importance of Deconstruction
Welcome
https%253A%252F%252Fwww.youtube.com%252Fwatch%253Fv%253DkY2NHSKBi10.html
Dynamic Convolution: Attention over Convolution Kernels
https%253A%252F%252Farxiv.org%252Fabs%252F1912.03458%2523microsoft.html
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
Preetum Nakkiran
Yamini Bansal
https%253A%252F%252Fopenai.com%252Fresearch%252Fdeep-double-descent.html
Fantastic Generalization Measures and Where to Find Them
Behnam Neyshabur
https%253A%252F%252Farxiv.org%252Fabs%252F1912.02178.html
Self-training with Noisy Student improves ImageNet classification
https%253A%252F%252Farxiv.org%252Fabs%252F1911.04252%2523google.html
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https%253A%252F%252Farxiv.org%252Fabs%252F1911.00357%2523facebook.html
Human-level performance in 3D multiplayer games with population-based reinforcement learning
Guy Lever
Koray Kavukcuoglu
%252Fdoc%252Freinforcement-learning%252Fexploration%252F2019-jaderberg.pdf%2523deepmind.html
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https%253A%252F%252Farxiv.org%252Fabs%252F1905.04899.html
Billion-scale semi-supervised learning for image classification
https%253A%252F%252Farxiv.org%252Fabs%252F1905.00546%2523facebook.html
A Recipe for Training Neural Networks
https%253A%252F%252Fkarpathy.github.io%252F2019%252F04%252F25%252Frecipe%252F.html
Detecting advertising on building façades with computer vision
https%253A%252F%252Fwww.sciencedirect.com%252Fscience%252Farticle%252Fpii%252FS1877050919311299.html
FloWaveNet: A Generative Flow for Raw Audio
https%253A%252F%252Farxiv.org%252Fabs%252F1811.02155.html
StreetNet: Preference Learning with Convolutional Neural Network on Urban Crime Perception
%252Fdoc%252Fai%252Fnn%252Fcnn%252F2018-fu.pdf.html
Human-Like Playtesting with Deep Learning
%252Fdoc%252Freinforcement-learning%252Fimitation-learning%252F2018-gudmundsson.pdf.html
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
https%253A%252F%252Farxiv.org%252Fabs%252F1808.01097.html
Exploring the Limits of Weakly Supervised Pretraining
Ross Girshick
Laurens Van Der Maaten
https%253A%252F%252Farxiv.org%252Fabs%252F1805.00932%2523facebook.html
YOLOv3: An Incremental Improvement
https%253A%252F%252Farxiv.org%252Fabs%252F1804.02767.html
Reptile: On First-Order Meta-Learning Algorithms
John Schulman’s Homepage
https%253A%252F%252Farxiv.org%252Fabs%252F1803.02999%2523openai.html
Guess, check and fix: a phenomenology of improvisation in ‘neural’ painting
%252Fdoc%252Fai%252Fnn%252Fcnn%252F2018-choi.pdf.html
China’s AI Advances Help Its Tech Industry, and State Security
https%253A%252F%252Fwww.nytimes.com%252F2017%252F12%252F03%252Fbusiness%252Fchina-artificial-intelligence.html.html
Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks
%252Fdoc%252Freinforcement-learning%252Fchess%252F2017-sabatelli.pdf%2523page%253D3.html
SMASH: One-Shot Model Architecture Search through HyperNetworks
https%253A%252F%252Farxiv.org%252Fabs%252F1708.05344.html
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
Ilya Loshchilov
Profile – Machine Learning Lab
https%253A%252F%252Farxiv.org%252Fabs%252F1707.08819.html
Towards Deep Learning Models Resistant to Adversarial Attacks
Homepage: Aleksander Mądry
https%253A%252F%252Farxiv.org%252Fabs%252F1706.06083.html
A simple neural network module for relational reasoning
https://sites.google.com/view/razp/home
https%253A%252F%252Farxiv.org%252Fabs%252F1706.01427%2523deepmind.html
What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features
Kannan Srinivasan—Analysis Group
https%253A%252F%252Fpapers.ssrn.com%252Fsol3%252Fpapers.cfm%253Fabstract_id%253D2976021.html
Scaling the Scattering Transform: Deep Hybrid Networks
https%253A%252F%252Farxiv.org%252Fabs%252F1703.08961.html
Mask R-CNN
Ross Girshick
https%253A%252F%252Farxiv.org%252Fabs%252F1703.06870%2523facebook.html
Convolution Aware Initialization
https%253A%252F%252Farxiv.org%252Fabs%252F1702.06295.html
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment
%252Fdoc%252Fpsychology%252Fneuroscience%252F2017-kawahara.pdf.html
YOLO9000: Better, Faster, Stronger
https%253A%252F%252Farxiv.org%252Fabs%252F1612.08242.html
Self-critical Sequence Training for Image Captioning
https%253A%252F%252Farxiv.org%252Fabs%252F1612.00563.html
ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
Ross Girshick
https%253A%252F%252Farxiv.org%252Fabs%252F1611.05431%2523facebook.html
Clockwork Convnets for Video Semantic Segmentation
https%253A%252F%252Farxiv.org%252Fabs%252F1608.03609.html
Wide Residual Networks
https%253A%252F%252Farxiv.org%252Fabs%252F1605.07146.html
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
https%253A%252F%252Farxiv.org%252Fabs%252F1603.05279.html
Microsoft researchers win ImageNet computer vision challenge
https%253A%252F%252Fblogs.microsoft.com%252Fai%252Fmicrosoft-researchers-win-imagenet-computer-vision-challenge%252F.html
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
Jonathan Krause
https%253A%252F%252Farxiv.org%252Fabs%252F1511.06789%2523google.html
Learning Visual Features from Large Weakly Supervised Data
Laurens Van Der Maaten
https%253A%252F%252Farxiv.org%252Fabs%252F1511.02251%2523facebook.html
Predicting and Understanding Urban Perception with Convolutional Neural Networks
%252Fdoc%252Fai%252Fnn%252Fcnn%252F2015-porzi.pdf.html
You Only Look Once: Unified, Real-Time Object Detection
Ross Girshick
https%253A%252F%252Farxiv.org%252Fabs%252F1506.02640.html
Clothing-1M: Learning from Massive Noisy Labeled Data for Image Classification
https%253A%252F%252Fopenaccess.thecvf.com%252Fcontent_cvpr_2015%252Fpapers%252FXiao_Learning_From_Massive_2015_CVPR_paper.pdf%2523baidu.html
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ross Girshick
https%253A%252F%252Farxiv.org%252Fabs%252F1506.01497%2523microsoft.html
Cyclical Learning Rates for Training Neural Networks
https%253A%252F%252Farxiv.org%252Fabs%252F1506.01186.html
Fast R-CNN
Ross Girshick
https%253A%252F%252Farxiv.org%252Fabs%252F1504.08083%2523microsoft.html
R-CNN: Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick
https%253A%252F%252Farxiv.org%252Fabs%252F1311.2524.html
DanNet: Flexible, High Performance Convolutional Neural Networks for Image Classification
https%253A%252F%252Farxiv.org%252Fabs%252F1102.0183%2523schmidhuber.html
Wikipedia Bibliography: