Noob-can-Compile / Moms-Spaghetti

Stash of some of the most potent research papers, blogs and videos on AI which I liked.

Moms-Spaghetti

1. For the love of Self Driving Cars & Autonomous Vehicles :

• Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age [ http://rpg.ifi.uzh.ch/docs/TRO16_cadena.pdf ]

• GSLAM: A General SLAM Framework and Benchmark [ https://arxiv.org/pdf/1902.07995.pdf ]

• A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors [ https://arxiv.org/pdf/1901.03642.pdf ]

• Unsupervised Learning of Depth and Ego-Motion from Video/ Andrej Karpathy & Tesla's Main Focus [ https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner/cvpr17_sfm_final.pdf ]

• Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras/ Andrej Karpathy & Tesla's Main Focus [ https://arxiv.org/pdf/1904.04998.pdf ]

• Types of Visual SLAM [https://www.kudan.io/post/different-types-of-visual-slam-systems]

• Introduction to Oriented FAST & Rotated BRIEF [https://medium.com/analytics-vidhya/introduction-to-orb-oriented-fast-and-rotated-brief-4220e8ec40cf]

• William Hoff's Computer Vision lecture Playlist [ https://www.youtube.com/watch?v=skaQfPQFSyY&list=PL4B3F8D4A5CAD8DA3&index=1 ]

• Fundamental Matrix Implementation [ https://scikit-image.org/docs/dev/auto_examples/transform/plot_fundamental_matrix.html ]

• Graph Based SLAM Research Paper [http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf]

• Predicting vehicle speed from dashcam video [https://medium.com/weightsandbiases/predicting-vehicle-speed-from-dashcam-video-f6158054f6fd]

• AI progress measurements, 2017, Electronic Frontier Foundation, Best Link's stash of papers [ https://www.eff.org/ai/metrics#Vision ]

• Speed Challenge from video [https://github.com/JonathanCMitchell/speedChallenge]

• End to End Learning for Self-Driving Cars, NVIDIA 2016 [ https://arxiv.org/pdf/1604.07316v1.pdf ]

• Vehicle Speed Prediction [ https://github.com/kevinzakka/vehicle-speed-prediction ]

• Raquel Urtasun's autonomous driving car's perception lectures [ http://www.cs.toronto.edu/~urtasun/courses/CSC2541/CSC2541_Winter16.html ]

• MIT deep learning self driving cars [ https://deeplearning.mit.edu/ ]

• comma.ai/research [ https://github.com/commaai/research ]

• All about self driving cars [ https://github.com/handong1587/handong1587.github.io/blob/master/_posts/deep_learning/2015-10-09-autonomous-driving.md ]

• All about AI [ https://github.com/handong1587/handong1587.github.io/tree/master/_posts ]

• Awesome autonomous driving [ https://github.com/manfreddiaz/awesome-autonomous-vehicles ]

• Awesome autonomous driving clone [ https://zhuanlan.zhihu.com/p/27686577 ]

• 3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection [ https://arxiv.org/pdf/1708.09839.pdf ]

• Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning [ http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w9/Milz_Visual_SLAM_for_CVPR_2018_paper.pdf ]

• Awesome Autonomous Vehicles [ https://github.com/DeepTecher/awesome-autonomous-vehicle/blob/master/README.md#%E7%A0%94%E7%A9%B6%E5%AE%9E%E9%AA%8C%E5%AE%A4 ]

• Deeper Direct Perception in Autonomous Driving [ http://cs231n.stanford.edu/reports/2016/pdfs/123_Report.pdf ]

• Oxford University Chris Linegar Thesis supervised by prof. Paul Newman [ http://www.robots.ox.ac.uk/~mobile/Theses/linegar_thesis_2016.pdf ]

• Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information [ https://www.mrt.kit.edu/z/publ/download/2019/Koenigshof2019Objects.pdf ]

• LEARNING TO DRIVE: PERCEPTION FOR AUTONOMOUS CARS-David Stavens Phd Thesis [ https://www.cs.stanford.edu/people/dstavens/thesis/David_Stavens_PhD_Dissertation.pdf ]

• Local Feature Matching visual object recognition [ http://www.micc.unifi.it/delbimbo/wp-content/uploads/2011/03/slide_corso/A53%20Geometric%20alignment%20and%20outlier%20rejection%20RANSAC.pdf ]

• David Dye's MSE101 course for fitting the gaussian and Non-linear Least Square minimization Fitting [ https://dyedavid.com/mse101/ ]

• David Dye's MSE101 course playlist for fitting the gaussian and Non-linear Least Square minimization Fitting [ https://www.youtube.com/watch?v=dgafxlyrFF8&list=PLRl6YIfL5k2WB5Hf4TjEK6oI7iGh1-CLH ]

• Large-Scale Object Mining for Object Discovery from Unlabeled Video [ https://arxiv.org/pdf/1903.00362.pdf ]

• MIT 6.S094: Convolutional Neural Networks for End-to-End Learning of the Driving Task [ https://youtu.be/U1toUkZw6VI ]

• Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving/ Andrej Karpathy & Tesla's Main Focus [ https://arxiv.org/pdf/1812.07179.pdf ]

• End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection/ Andrej Karpathy & Tesla's Main Focus [ https://arxiv.org/pdf/2004.03080v1.pdf ]

• Computer Vision for Visual Effects (ECSE-6969) Lectures Spring 2014 Rich Radke's Playlist [ https://www.youtube.com/watch?v=rE-hVtytT-I&list=PLuh62Q4Sv7BUJlKlt84HFqSWfW36MDd5a ]

• Intro to Digital Image Processing (ECSE-4540) Lectures, Spring 2015 Rich Radke's Playlist [ https://www.youtube.com/watch?v=UhDlL-tLT2U&list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX ]

• A2D2: Audi Autonomous Driving Dataset [ https://arxiv.org/pdf/2004.06320.pdf ]/ 2TB Dataset: [ https://www.a2d2.audi/a2d2/en/download.html ]

• RANSAC [ https://people.cs.umass.edu/~elm/Teaching/ppt/370/370_10_RANSAC.pptx.pdf ] [ http://www.cse.psu.edu/~rtc12/CSE486/lecture15.pdf ] [ https://www.cc.gatech.edu/~afb/classes/CS4495-Fall2014/slides/CS4495-Ransac.pdf ]

• Epipolar Geometry [ https://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/EPSRC_SSAZ/node18.html ]

• Andrej Karpathy - AI for Full-Self Driving @ScaledML conference (Basically Screw LiDAR) [ https://www.youtube.com/watch?v=hx7BXih7zx8&feature=youtu.be ]

• Medium Post by David Silver on Andrej Karpathy - AI for Full-Self Driving @ScaledML conference (Basically Screw LiDAR) [ https://medium.com/self-driving-cars/annotated-karpathys-autopilot-talk-3e6270a21f8d ]

• LEARNING TO EXPLORE USING ACTIVE NEURAL SLAM [ https://arxiv.org/pdf/2004.05155v1.pdf ]

• SEMANTICALLY-GUIDED REPRESENTATION LEARNING FOR SELF-SUPERVISED MONOCULAR DEPTH [ https://arxiv.org/pdf/2002.12319v1.pdf ]

• David Silver blog for autonomous vehicles dataset [ https://medium.com/self-driving-cars/autonomous-vehicle-datasets-2ec5cab58dc9 ]

- Pose Estimation:

• CAMERA POSE ESTIMATION FROM A STEREO SETUP Thesis by sebastien Gilbert [ https://pdfs.semanticscholar.org/675a/75494f55b0ac6092f6beef6ac413c296faf4.pdf ]

• Real-time 3D Pose Estimation with a Monocular Camera Using Deep Learning and Object Priors Thesis by Ankit Dhall [ https://arxiv.org/pdf/1809.10548.pdf ]

• RECOVERING THE 3D POSE AND SHAPE OF VEHICLES FROM STEREO IMAGES [ https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2/73/2018/isprs-annals-IV-2-73-2018.pdf ]

• STEREO VISION-BASED 3D CAMERA POSE AND OBJECT STRUCTURE ESTIMATION [ http://rovislab.com/papers/Grigorescu_visapp.pdf ]

• Visual-Inertial Odometry of Aerial Robots [ https://arxiv.org/pdf/1906.03289.pdf ]

• Research on pose estimation for stereo vision measurement system by an improved method: uncertainty weighted stereopsis pose solution method based on projection vector [ https://www.osapublishing.org/DirectPDFAccess/4024C1D5-0CEF-AED2-0D013F1162DA9053_427466/oe-28-4-5470.pdf?da=1&id=427466&seq=0&mobile=no ]

2. Computer Vision:

• Dense Associative Memory for Pattern Recognition. [ https://arxiv.org/pdf/1606.01164.pdf ]

• Self-Supervised Learning of Pretext-Invariant Representations. [ https://arxiv.org/abs/1912.01991 ].

• Momentum Contrast for Unsupervised Visual Representation Learning. [ https://arxiv.org/abs/1911.05722 ].

• On the Relationship between Self-Attention and Convolutional Layers. [ https://openreview.net/attachment?id=HJlnC1rKPB&name=original_pdf ]

• William Hoff's Image processing and signal processing lectures [ https://www.youtube.com/watch?v=rbY-JRQEDUU&list=PLyED3W677ALNv8Htn0f9Xh-AHe1aZPftv ]

• An Efficient UAV(Unmanned Aerial Vehicle)-based Artificial Intelligence Framework for Real-Time Visual Tasks [ https://arxiv.org/pdf/2004.06154.pdf ]

• SpaceNet 6: Multi-Sensor All Weather Mapping Dataset(Automate Satellite surveillance)/Synthetic Aperture Radar [ https://arxiv.org/pdf/2004.06500.pdf ]/ Dataset: [ https://spacenet.ai/sn6-challenge/ ]

• YOLOv4: Optimal Speed and Accuracy of Object Detection [ https://arxiv.org/pdf/2004.10934.pdf ]

• YOLOv4 (v3/v2) - Windows and Linux version of Darknet Neural Networks for object detection [ https://github.com/AlexeyAB/darknet ]

• Two-Stream Convolutional Networks for Action Recognition in Videos(SpatioTemporal learning) [ https://papers.nips.cc/paper/5353-two-stream-convolutional-networks-for-action-recognition-in-videos.pdf ]

• Spatio-temporal Texture Modelling for Real-time Crowd Anomaly Detection [ http://shura.shu.ac.uk/18876/3/Wang%20Spatio-temporal%20Texture%20Modelling%20for%20Real-time%20Crowd%20Anomaly%20Detection.pdf ]

• TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation [ https://arxiv.org/pdf/1801.05746.pdf ]

• V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [ https://arxiv.org/pdf/1606.04797.pdf ]

• U-Net: Convolutional Networks for Biomedical Image Segmentation [ https://arxiv.org/pdf/1505.04597.pdf ]

• Mask R-CNN [ https://arxiv.org/pdf/1703.06870.pdf ]

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [ https://arxiv.org/pdf/1506.01497.pdf ]

• Faster Fourier Transformation by JVDP [ https://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/ ]

• BUNET: Blind Medical Image Segmentation Based on Secure UNET [ https://arxiv.org/pdf/2007.06855v1.pdf ]

• A Biologically Inspired Spiking Model of Visual Processing for Image Feature Detection [ http://irep.ntu.ac.uk/id/eprint/15302/1/2409_McGinnity.pdf ]

• Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction [ https://arxiv.org/pdf/2003.12346v1.pdf ]

• On the Use of AI for Satellite Communications [ https://arxiv.org/ftp/arxiv/papers/2007/2007.10110.pdf ]

• Learning to See Through Obstructions [ https://alex04072000.github.io/ObstructionRemoval/ / https://www.youtube.com/watch?v=ICr6xi9wA94&t=71s ]

• 3D Photography using Context-aware Layered Depth Inpainting [ https://shihmengli.github.io/3D-Photo-Inpainting/ / https://youtu.be/MrIbQ0pIFOg ]

• TecoGAN: Super Resolution Extraordinaire! [ https://www.youtube.com/watch?v=MwCgvYtOLS0 ] / [ https://arxiv.org/pdf/1811.09393.pdf ] / [ https://ge.in.tum.de/publications/2019-tecogan-chu/ ]

• ETH Zurich CV and Geometry Group [ http://cvg.ethz.ch/research/ ]

- Brain | Visual Perception | BCI |:

• Affordances Provide a Fundamental Categorization Principle for Visual Scenes [ https://arxiv.org/ftp/arxiv/papers/1411/1411.5340.pdf ]

• Neural mechanisms of rapid natural scene categorization in human visual cortex [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752739/ ]

• To err is human: correlating fMRI decoding and behavioral errors to probe the neural representation of natural scene categories [ https://bwlab.utoronto.ca/wp-content/uploads/2014/10/walther_etal_visualpopulationcodes2012_withfigures.pdf ]

• Visual Noise from Natural Scene Statistics Reveals Human Scene Category Representations [ https://arxiv.org/ftp/arxiv/papers/1411/1411.5331.pdf ]

• VISUAL SCENE PERCEPTION IN THE HUMAN BRAIN: CONNECTIONS TO MEMORY, CATEGORIZATION, AND SOCIAL COGNITION [ http://vision.stanford.edu/documents/Baldassano_PhD_thesis_2015.pdf ]

• How (and why) the visual control of action differs from visual perception [ https://royalsocietypublishing.org/doi/10.1098/rspb.2014.0337 ]

• How does the brain solve visual object recognition? [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3306444/pdf/nihms352068.pdf ]

• SENSE AND THE SINGLE NEURON: Probing the Physiology of Perception [ https://www.annualreviews.org/doi/10.1146/annurev.neuro.21.1.227 ]

• Backpropagation and the brain by Geoffrey Hinton [ https://sci-hub.tw/10.1038/s41583-020-0277-3 / https://www.youtube.com/watch?v=a0f07M2uj_A ]

• Backpropagation and the brain: Reddit Comments section [ https://www.reddit.com/r/MachineLearning/comments/g3gvfm/r_backpropagation_and_the_brain/ ]

• Brain Scans Show Why Our Mind's Eye Sees The World So Differently to Everyday Vision [ https://www.sciencealert.com/here-s-why-our-mind-s-eye-sees-the-world-so-differently-to-everyday-vision ]

• Studies of brain activity aren't as useful as scientists thought [ https://medicalxpress.com/news/2020-06-brain-scientists-thought.html ]

• Scientists Now Question Brain Imaging Methods [ https://learningenglish.voanews.com/a/scientists-now-question-brain-imaging-methods/5694850.html ]

• Why the Brain Never Processes the Same Input in the Same Way [ https://neurosciencenews.com/brain-input-processing-16699/ ]

• The rise and fall of MRI studies in major depressive disorder [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6901449/pdf/41398_2019_Article_680.pdf ]

• All that glitters is not BOLD: inconsistencies in functional MRI [ https://www.nature.com/articles/srep03920.pdf ]

• Scene Perception in the Human Brain [ https://www.sas.upenn.edu/psych/epsteinlab/pdfs/Epstein%20and%20Baker%202019.pdf ]

• Anterior hippocampus: the anatomy of perception, imagination and episodic memory [ https://sci-hub.tw/https://www.nature.com/articles/nrn.2015.24 ]

• Using a model of human visual perception to improve deep learning [ https://sci-hub.tw/https://www.sciencedirect.com/science/article/abs/pii/S0893608018301254 ]

• Deep Neural Networks for Modeling Visual Perceptual Learning [ https://www.jneurosci.org/content/jneuro/38/27/6028.full.pdf ]

• Adapting deep neural networks as models of human visual perception Phd Thesis [ https://pdfs.semanticscholar.org/8dee/494d0ee3f2d123583929069f68c47e1ea3b5.pdf ]

• Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future [ https://arxiv.org/ftp/arxiv/papers/2001/2001.07092.pdf ]

• Visual Perceptual Learning and Models [ https://sci-hub.tw/https://www.annualreviews.org/doi/pdf/10.1146/annurev-vision-102016-061249 ]

• Visual Perception with Deep Learning by Yann LeCun [ https://cs.nyu.edu/~yann/talks/lecun-20080409-google.pdf ]

• Perceptual Dominance in Brief Presentations of Mixed Images: Human Perception vs. Deep Neural Networks [ https://www.frontiersin.org/articles/10.3389/fncom.2018.00057/full ]

• Coding of navigational affordances in the human visual system [ https://sci-hub.tw/https://www.pnas.org/content/114/18/4793#:~:text=A%20central%20component%20of%20spatial,affordances%20of%20the%20local%20scene. ]

• From spatial navigation via visual construction to episodic memory and imagination [ https://link.springer.com/content/pdf/10.1007/s00422-020-00829-7.pdf ]

• O'Reilly Chapter 4. Perception, Cognition, and Affordance [ https://www.oreilly.com/library/view/understanding-context/9781449326531/ch04.html ]

• Affordances in psychology, neuroscience and robotics: a survey [ https://www.cmpe.boun.edu.tr/~emre/papers/TCDS2016-Affordances.pdf ]

• Affordances and neuroscience: Steps towards a successful marriage [ https://sci-hub.tw/https://doi.org/10.1016/j.neubiorev.2017.07.008 ]

• Affordance - ScholarlyCommons - University of Pennsylvania [ https://repository.upenn.edu/cgi/viewcontent.cgi?article=1682&context=ese_papers ]

• Models of visual cortex [ http://www.scholarpedia.org/article/Models_of_visual_cortex ]

• NYU library [ https://www.cns.nyu.edu/heegerlab/?page=research ]

• Interpretable Visual Models for Human Perception-Based Object Retrieval [ https://sci-hub.tw/https://doi.org/10.1145/1991996.1992017 ]

• Decision-theoretic models of visual perception and action [ https://sci-hub.tw/https://doi.org/10.1016/j.visres.2010.09.031 ]

• 2019 Conference on Cognitive Computational Neuroscience [ https://ccneuro.org/2019/Papers/AcceptedPapers.asp ]

• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense [ https://arxiv.org/pdf/2004.09044.pdf ]

• Towards an integration of deep learning and neuroscience [ https://arxiv.org/pdf/1606.03813.pdf ]

• An overview of deep learning in medical imaging focusing on MRI [ https://arxiv.org/pdf/1811.10052.pdf ]

• Interpreting encoding and decoding models [ https://arxiv.org/ftp/arxiv/papers/1812/1812.00278.pdf ]

• 42nd European Conference on Visual Perception (ECVP) [ https://journals.sagepub.com/doi/pdf/10.1177/0301006619863862 ]

• Neural Representations for Object Perception: Structure, Category, and Adaptive Coding [ https://www.abg.psychol.cam.ac.uk/system/files/documents/kourtziconnor2011.pdf ]

• General Transformations of Object Representations in Human Visual Cortex [ https://www.jneurosci.org/content/jneuro/38/40/8526.full.pdf ]

• The relative contributions of visual and semantic information in the neural representation of object categories [ https://onlinelibrary.wiley.com/doi/epdf/10.1002/brb3.1373 ]

• Selective Neural Representation of Objects Relevant for Navigation [ http://graphics.cs.cmu.edu/courses/16-899A/2014_spring/thevisualworld/19.pdf ]

• Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5281549/pdf/fncom-11-00004.pdf ]

• Dynamic representation of partially occluded objects in primate prefrontal and visual cortex [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5605274/pdf/elife-25784.pdf ]

• The Neural Representation of Multiple Objects in the Primate Visual System [ https://www.jneurosci.org/content/jneuro/35/37/12612.full.pdf ]

• Neural representation of visual concepts in people born blind [ https://www.nature.com/articles/s41467-018-07574-3.pdf ]

• Recent advances in understanding object recognition in the human brain: deep neural networks, temporal dynamics, and context [ https://f1000researchdata.s3.amazonaws.com/manuscripts/24595/89bfaabc-f2da-415b-819a-439654871e0e_22296_-_susan_wardle.pdf?doi=10.12688/f1000research.22296.1&numberOfBrowsableCollections=24&numberOfBrowsableInstitutionalCollections=5&numberOfBrowsableGateways=24 ]

• Interaction between Scene and Object Processing Revealed by Human fMRI and MEG Decoding [ https://www.jneurosci.org/content/jneuro/37/32/7700.full.pdf ]

• Visual Shape and Object Perception [ http://depts.washington.edu/shapelab/research/journals/Review2018.pdf ]

• Computation of Object Size in Visual Cortical Area V4 as a Neural Basis for Size Constancy [ https://www.jneurosci.org/content/jneuro/35/34/12033.full.pdf ]

• The Neural Basis of Individual Face and Object Perception [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4771946/pdf/fnhum-10-00066.pdf ]

• The neural basis of object perception [ https://sci-hub.tw/https://doi.org/10.1016/S0959-4388(03)00040-0 ]

• The Representation of Object Concepts in the Brain [ http://ling.umd.edu/~ellenlau/courses/ling646/Martin_2007.pdf ]

• Invariant Recognition Shapes Neural Representations of Visual Input [ http://cbmm.mit.edu/sites/default/files/publications/annurev-vision-091517-034103.pdf ]

• Dynamic updating of hippocampal object representations reflects new conceptual knowledge [ https://www.pnas.org/content/pnas/113/46/13203.full.pdf ]

• Deep image reconstruction from human brain activity [ https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006633&type=printable ]

• End-to-end deep image reconstruction from human brain activity [ https://www.biorxiv.org/content/10.1101/272518v1.full.pdf ]

• From the human visual system to the computational models of visual attention: a survey [ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.3859&rep=rep1&type=pdf ]

• Computational Models of Object Recognition in Cortex: A Review [ https://dspace.mit.edu/bitstream/handle/1721.1/7231/AIM-1695.pdf?sequence=2 ]

• A quantitative theory of immediate visual recognition [ http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07_wfig.pdf ]

• Decoding Images in the Mind’s Eye: The Temporal Dynamics of Visual Imagery [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969936/pdf/vision-03-00053.pdf ]

• Spatial and temporal dynamics of presaccadic attentional facilitation before pro- and antisaccades [ https://sci-hub.tw/10.1167/18.11.2 ]

• Differential temporal dynamics during visual imagery and perception [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5973830/pdf/elife-33904.pdf ]

• Temporal Dynamics of Neural Adaptation Effect in the Human Visual Ventral Stream [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6729535/pdf/0246283.pdf ]

• Spatio-temporal dynamics of face perception [ https://sci-hub.tw/https://doi.org/10.1016/j.neuroimage.2020.116531 ]

• Temporal dynamics of binocular integration in primary visual cortex [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797477/pdf/i1534-7362-19-12-13.pdf ]

• Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition [ https://people.csail.mit.edu/khosla/papers/arxiv_Cichy.pdf ]

• Visual Field Maps in Human Cortex [ https://www.cell.com/action/showPdf?pii=S0896-6273%2807%2900774-X ]

• Eccentricity mapping of the human visual cortex to evaluate temporal dynamics of functional T1ρ mapping [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4640285/pdf/jcbfm201594a.pdf ]

• Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation [ https://arxiv.org/pdf/1807.10936.pdf / https://github.com/tudelft/cuSNN / https://www.youtube.com/watch?v=FJrba02kZII ]

• Stochastic Resonance Based Visual Perception Using Spiking Neural Networks [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242793/pdf/fncom-14-00024.pdf ]

• A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems [ https://www.nature.com/articles/srep40703.pdf ]

• A Spiking Neural Network Model of Depth from Defocus for Eventbased Neuromorphic Vision [ https://www.nature.com/articles/s41598-019-40064-0.pdf ]

• Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture [ https://www.nature.com/articles/s41598-018-27169-8.pdf / https://www.youtube.com/watch?v=_tWsmhCP7hs ]

• SLAYER: Spike Layer Error Reassignment in Time (Spiking NN) [ https://papers.nips.cc/paper/7415-slayer-spike-layer-error-reassignment-in-time.pdf / https://www.youtube.com/watch?v=wCs2lv3g4A4 / https://www.garrickorchard.com/ https://github.com/bamsumit/slayerpytorch ]

• Low‑level image statistics in natural scenes infuence perceptual decision‑making [ https://www.nature.com/articles/s41598-020-67661-8.pdf ]

• Temporal signatures of criticality in human cortical excitability as probed by early somatosensory responses [ https://sci-hub.tw/10.1523/JNEUROSCI.0241-20.2020 ]

• Brain Areas Active during Visual Perception of Biological Motion [ https://www.cell.com/neuron/pdf/S0896-6273(02)00897-8.pdf ]

• Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5595153/pdf/fnhum-11-00445.pdf ]

• Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings [ https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0001394&type=printable ]

• Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG [ https://sci-hub.tw/https://doi.org/10.1016/j.neuroimage.2016.02.019 ]

• Instance-level contrastive learning yields human brain-like representation without category-supervision [ https://www.biorxiv.org/content/10.1101/2020.06.15.153247v1.full.pdf ]

• Learning Perceptual Inference by Contrasting [ https://arxiv.org/pdf/1912.00086.pdf ]

• Making Sense of Real-World Scenes [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5125545/pdf/nihms822642.pdf ]

• Visual perception affected by motivation and alertness controlled by a noninvasive braincomputer interface [ https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0188700&type=printable ]

• Improving decision-making based on visual perception via a collaborative brain-computer interface [ https://sci-hub.st/https://doi.org/10.1109/CogSIMA.2013.6523816 ]

• Brain Machine Interfaces for Vision Restoration: The Current State of Cortical Visual Prosthetics [ https://link.springer.com/content/pdf/10.1007/s13311-018-0660-1.pdf ]

• Effects of Visual Attention on Tactile P300 BCI [ http://downloads.hindawi.com/journals/cin/2020/6549189.pdf ]

• Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks [ https://arxiv.org/ftp/arxiv/papers/2005/2005.08842.pdf ]

• Computational models of visual attention [ http://www.scholarpedia.org/article/Computational_models_of_visual_attention ]

• Maps in the head [ https://philosophy.ucla.edu/wp-content/uploads/2016/08/Maps-in-the-Head.pdf ]

• Maps in the head article [ https://aeon.co/essays/how-cognitive-maps-help-animals-navigate-the-world ]

• The “Map in the Head” Metaphor [ https://sci-hub.st/https://doi.org/10.1177/001391658414200 ]

• The cognitive map in humans: Spatial navigation and beyond [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6028313/pdf/nihms975780.pdf ]

• CVPR 2021: An Overview [ https://yassouali.github.io/ml-blog/cvpr2021/ ]

- Geometric and Probabilistic Modeling & Processing | Variational Autoencoder (VAE) | Partial Differential Equations (PDE) | Equivariance | Invertibility | Causal Inference | Causality | Probabilistic Methods | Robustness | Uncertainty Quantification | Trustworthy AI

• Monte Carlo Geometry Processing: A Grid-Free Approach to PDE-Based Methods on Volumetric Domains [ https://www.cs.cmu.edu/~kmcrane/Projects/MonteCarloGeometryProcessing/index.html ] / [ https://www.youtube.com/watch?v=oHLR287rDRA ] / [ https://www.cs.cmu.edu/~kmcrane/Projects/MonteCarloGeometryProcessing/paper.pdf ]

• Unifying Points, Beams, and Paths in Volumetric Light Transport Simulation [ https://cs.dartmouth.edu/~wjarosz/publications/krivanek14upbp.pdf / https://www.youtube.com/watch?v=TbWQ4lMnLNw ]

• An Explanatory Analysis of the Geometry of Latent Variables Learned by Variational Auto-Encoders [ http://bayesiandeeplearning.org/2017/papers/76.pdf ]

• MATHEMATICAL METHODS IN MEDICAL IMAGE PROCESSING [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640423/pdf/nihms-462128.pdf ]

• Codimensional Surface Tension Flow using Moving-Least-Squares Particles [ https://www.youtube.com/watch?v=SxIkQt04WCo ] / [ https://web.stanford.edu/~yxjin/pdf/codim.pdf ]

• AnisoMPM: Animating Anisotropic Damage Mechanics [ https://www.youtube.com/watch?v=fE9BqmJrrW0 ] / [ https://joshuahwolper.com/anisompm ]

• N-Dimensional Rigid Body Dynamics [ https://www.youtube.com/watch?v=nkHL1GNU18M ] / https://marctenbosch.com/ndphysics/

• All Things Deep Geometry Learning [ http://geometrylearning.com/ ]

• A Geometric View of Neural Networks Using homotopy [ https://sci-hub.st/10.1109/NNSP.1993.471877 ]

• Neural Networks, Manifolds, and Topology [ http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ ]

• Literature on the differential geometry of neural networks [ https://www.reddit.com/r/MachineLearning/comments/40r676/literature_on_the_differential_geometry_of_neural/ ]

• On advances in differential-geometric approaches for 2D and 3D shape analyses and activity recognition by Anuj Srivastava [ https://sci-hub.st/https://doi.org/10.1016/j.imavis.2012.03.006 ]

• A Literature Review: Geometric Methods and Their Applications in Human-Related Analysis [ https://www.mdpi.com/1424-8220/19/12/2809/pdf-vor ]

• All Things Geometric publication of Anuj Srivastava [ https://ani.stat.fsu.edu/~anuj/publications.php ]

• Anuj Srivastava Google Scholar [ https://scholar.google.com/citations?user=Kj-lB0MAAAAJ&hl=en ]

• Erik J. Bekkers [ https://scholar.google.com/citations?user=yeWrfR4AAAAJ&hl=en ]

• Taco Cohen [ https://tacocohen.wordpress.com/ ] / [ https://scholar.google.com/citations?user=a3q4YxEAAAAJ&hl=en ]

• Anand Joshi [https://scholar.google.com/citations?user=rgLGzAQAAAAJ&hl=en]

• Math Basics Probability and Geometric Distribution [ https://www.youtube.com/channel/UC_STD9JXaOIqIs29uKvrcYQ/videos ]

• Applications of Differential Geometry in Artificial Intelligence [ https://math.stackexchange.com/questions/584551/applications-of-differential-geometry-in-artificial-intelligence ]

• How useful is differential geometry and topology to deep learning? [ https://mathoverflow.net/questions/350228/how-useful-is-differential-geometry-and-topology-to-deep-learning ]

• GEOMETRIC DEEP LEARNING [ http://geometricdeeplearning.com/ ]

• DiffCVML 2020 [ https://diffcvml.org/2020/ ]

• Differential Geometry meets Deep Learning NeurIPS 2020 Workshop [ https://sites.google.com/view/diffgeo4dl/ ]

• A Differential Geometric Approach to Automated Segmentation of Human Airway Tree [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3271357/pdf/nihms351945.pdf ]

• Effects of Differential Geometry Parameters on Grid Generation and Segmentation of MRI Brain Image [ https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8719974 ]

• Lie Groups for 2D and 3D Transformations [ http://ethaneade.com/lie.pdf ]

• Roto-Translation Covariant Convolutional Networks for Medical Image Analysis [ https://arxiv.org/pdf/1804.03393.pdf ]

• Oxford Visual Geometry Group [ https://www.robots.ox.ac.uk/~vgg/research/ ]

• Deep Scale-spaces: Equivariance Over Scale by Max Welling [ https://openreview.net/pdf/162130ec5fbc269e7eacc9c52d86efe6d38396a5.pdf ]

• Rotation Equivariant CNNs for Digital Pathology by Max Welling [ http://basveeling.nl/pdf/gcnn_pcam.pdf ]

• Exploiting Cyclic Symmetry in Convolutional Neural Networks [ https://arxiv.org/pdf/1602.02660.pdf ]

• Variational autoencoders [ https://www.jeremyjordan.me/variational-autoencoders/ ] / [ https://www.youtube.com/watch?v=9zKuYvjFFS8&vl=en ]

• Geometric Foundations of Deep Learning Research [ https://www.reddit.com/r/MachineLearning/comments/m8ewph/geometric_foundations_of_deep_learning_research/ ]

• Fast end-to-end learning on protein surfaces [ https://www.biorxiv.org/content/10.1101/2020.12.28.424589v1.full.pdf ]

• DeepMind’s AlphaFold2 Predicts Protein Structures with Atomic-Level Accuracy [ https://syncedreview.com/2021/07/20/deepmind-podracer-tpu-based-rl-frameworks-deliver-exceptional-performance-at-low-cost-65/amp/ ]

• Alphafold2 code [ https://github.com/deepmind/alphafold ] / paper [https://www.nature.com/articles/s41586-021-03819-2_reference.pdf]

• Google colab notebooks are already running Deepmind’s AlphaFold v.2 [ https://towardsdatascience.com/google-colab-notebooks-are-already-running-deepminds-alphafold-v-2-92b4531ec127 ]

• Verbitsky [ http://verbit.ru/ ] / [ https://arxiv.org/find/math/1/au:+Verbitsky_Misha/0/1/0/all,past/0/1?skip=0 ]

• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [ https://arxiv.org/abs/2011.06225 ]

• Probabilistic Causation [ https://plato.stanford.edu/entries/causation-probabilistic/ ]

3. Federated Learning | Differential Privacy | Privacy Preserving | Fairness | Bias | Ethics | Transparency | Interpretability | Explainability | Gossip Learning

• GENERATIVE MODELS FOR EFFECTIVE ML ON PRIVATE, DECENTRALIZED DATASETS [ https://arxiv.org/pdf/1911.06679v2.pdf ]

• A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference [ https://arxiv.org/pdf/2003.12154v1.pdf ]

• SHREDDER: Learning Noise Distributions to Protect Inference Privacy [ https://arxiv.org/pdf/1905.11814.pdf ]

• All Things Data, AI & Ethics [ https://docs.google.com/document/d/e/2PACX-1vS5uiipUvpthiMUp6x09VJ93QiBN1tQYKWZUxwXLCsbYVp8yNIgCBnJd9vbP91KzrIuXStONkb0a429/pub ]

• Approaches for Explainability of AIEnabled Systems in Medical Imaging [ https://ncats.nih.gov/files/2_Sahiner_Berkman_Explainability_v1.pdf ]

• “Why Should I Trust You?” Explaining the Predictions of Any Classifier [ https://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf ]

• Explainable deep learning models in medical image analysis [ https://arxiv.org/pdf/2005.13799.pdf ]

• Interpretable and Differentially Private Predictions [ https://arxiv.org/pdf/1906.02004.pdf ]

• Explainability Methods for Graph Convolutional Neural Networks [ https://openaccess.thecvf.com/content_CVPR_2019/papers/Pope_Explainability_Methods_for_Graph_Convolutional_Neural_Networks_CVPR_2019_paper.pdf ]

• The Building Blocks of Interpretability [ https://distill.pub/2018/building-blocks/ ]

• GNNExplainer: Generating Explanations for Graph Neural Networks [ https://arxiv.org/pdf/1903.03894.pdf ]

• BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis∗ [ https://www.biorxiv.org/content/10.1101/2020.05.16.100057v1.full.pdf ]

• CVPR 2020 Tutorial on Interpretable Machine Learning for Computer Vision [ https://interpretablevision.github.io/ ]

• Breaking the interpretability barrier - a method for interpreting deep graph convolutional models [ http://www.di.uniba.it/~loglisci/NFMCP2019/NFMCP/nfMCP2019_paper_17.pdf ]

• GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks [ https://arxiv.org/pdf/2001.06216.pdf ]

• ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition [ https://arxiv.org/pdf/2003.05328v1.pdf ]

• Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks [ https://arxiv.org/pdf/1902.01147v2.pdf ]

• Secure Byzantine-Robust Machine Learning [ https://arxiv.org/pdf/2006.04747v1.pdf ]

• BYZANTINE-RESILIENT SECURE FEDERATED LEARNING [ https://arxiv.org/pdf/2007.11115v1.pdf ]

• Secure and Differentially Private Bayesian Learning on Distributed Data [ https://arxiv.org/pdf/2005.11007v1.pdf ]

• Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models [ http://people.cs.uchicago.edu/~ravenben/publications/pdf/fawkes-usenix20.pdf ]

• Privacy in Deep Learning: A Survey [ https://arxiv.org/pdf/2004.12254v4.pdf ]

• Decentralized Federated Learning: A Segmented Gossip Approach [ https://arxiv.org/pdf/1908.07782.pdf ]

• Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication [ http://proceedings.mlr.press/v97/koloskova19a/koloskova19a.pdf ]

• Gossip Learning: Off the Beaten Path [ https://people.kth.se/~sarunasg/Papers/Giaretta2019GossipLearning.pdf ]

• Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints [ http://iphome.hhi.de/samek/pdf/SatArXiv19.pdf ]

• Gossip training for deep learning [ https://arxiv.org/pdf/1611.09726.pdf ]

• Edge Cloud as an Enabler for Distributed AI in Industrial IoT Applications: the Experience of the IoTwins Project* [ http://ceur-ws.org/Vol-2502/invited1.pdf ]

• GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent [ https://arxiv.org/pdf/1803.05880.pdf ]

• Gossip Learning as a Decentralized Alternative to Federated Learning⋆ [ http://publicatio.bibl.u-szeged.hu/15824/1/dais19a.pdf ]

• Secure and Robust Machine Learning for Healthcare: A Survey [ https://arxiv.org/pdf/2001.08103.pdf ]

• AI models need to be ‘interpretable’ rather than just ‘explainable’ [ https://thenextweb.com/neural/2020/08/06/ai-models-need-to-be-interpretable-rather-than-just-explainable/ / https://www.youtube.com/watch?v=I0yrJz8uc5Q ]

• SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation [ https://arxiv.org/pdf/2001.07645v1.pdf ]

• A concise primer on Differential Privacy [ https://github.com/Kritikalcoder/DP-primer/blob/master/DP_Primer.ipynb ]

• Statistical Inference is Not a Privacy Violation [ https://differentialprivacy.org/inference-is-not-a-privacy-violation/ ]

• DifferentialPrivacy.org [ https://differentialprivacy.org/categories/ ]

• The Limits of Differential Privacy (and Its Misuse in Data Release and Machine Learning) [ https://cacm.acm.org/magazines/2021/7/253460-the-limits-of-differential-privacy-and-its-misuse-in-data-release-and-machine-learning/fulltext ]

• Reading Race: AI Recognizes Patient's Racial Identity In Medical Images [ https://arxiv.org/ftp/arxiv/papers/2107/2107.10356.pdf ]
  [ Big trouble in big medical data: This is a huge potential issue. As the authors write: "our findings that AI can trivially predict self-reported race -- even from corrupted, cropped, and noised medical images -- in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to."
  "Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities." They tested out a bunch of models on datasets including chest x-rays, breast mammograms, CT scans (computed tomography), and more and found that models were able to tell different races apart even under degraded image settings. Probably the most inherently challenging finding is that "models trained on high-pass filtered images maintained performance well beyond the point that the degraded images contained no recognisable structures; to the human co-authors and radiologists it was not even clear that the image was an x-ray at all," they write. In other words - these ML models are making decisions about racial classification (and doing it accurately) using features that humans can't even observe, let alone analyze.]

4. End to End ML

• brohrer's github [ https://e2eml.school/blog.html ]

• brohrer's how cnn works [ http://brohrer.github.io/how_convolutional_neural_networks_work.html ]

• Implementing Graph Neural Networks with JAX [ http://gcucurull.github.io/deep-learning/2020/04/20/jax-graph-neural-networks/ ]

• GRAPH CONVOLUTIONAL NETWORKS [ https://tkipf.github.io/graph-convolutional-networks/ ]

• You don't know Jax [ https://colinraffel.com/blog/you-don-t-know-jax.html ]

• The fastest 2D convolution in the world [ https://laurentperrinet.github.io/sciblog/posts/2017-09-20-the-fastest-2d-convolution-in-the-world.html# ]

• A guide to convolution arithmetic for deep learning [ https://arxiv.org/pdf/1603.07285.pdf ]

• Jax Incoming!! [ https://g-k.ai/post/convoluted_stuff_1/ ]

• JAX strikes back, more puppies and a deeper look into Winograd [ https://g-k.ai/post/convoluted_stuff_2/ ]

• Brandon Rohrer's E2EML 321. Convolution in One Dimension for Neural Networks [ https://gkaissis.github.io/blog/computer%20science/machine%20learning/python/2020/06/05/ConvolutedStuffVol2.html ]

• A Comprehensive Survey on Graph Neural Networks [ https://arxiv.org/pdf/1901.00596.pdf ]

• Fusing Structural and Functional MRIs using Graph Convolutional Networks for Autism Classification [ https://openreview.net/pdf?id=EKu4FU5s4 ]

• An overview of gradient descent optimization algorithms(Which optimizer shall I use?) [ https://ruder.io/optimizing-gradient-descent/ ]

• Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs [ https://arxiv.org/pdf/1812.00279.pdf ]

• Graph Capsule Convolutional Neural Networks [ https://arxiv.org/pdf/1805.08090.pdf ]

• S4NN: temporal backpropagation for spiking neural networks with one spike per neuron [ https://arxiv.org/pdf/1910.09495.pdf ]

• Demystification of AI-driven medical image interpretation: past, present and future [ https://canvas.stanford.edu/files/3473819/download?download_frd=1 / Generalized AI & Interpretability Table at 27:46 min [ https://youtu.be/Yk_7hm5FCf0 ] Slides [ https://learning.acm.org/binaries/content/assets/leaning-center/webinar-slides/2020/animaanandkumar_techtalk_slides1.pdf ] ]

• The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence by Gary Marcus [ https://arxiv.org/ftp/arxiv/papers/2002/2002.06177.pdf ]

• Lectures and slides for the UvA Master AI course Machine Learning 1 [ https://uvaml1.github.io/ ] / [ https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n ]

• What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? [ https://www.reddit.com/r/MachineLearning/comments/mgzvt2/d_whats_the_simplest_most_lightweight_but/ ]

• How to Use Google Colab for Deep Learning – Complete Tutorial [ https://neptune.ai/blog/how-to-use-google-colab-for-deep-learning-complete-tutorial?utm_source=reddit&utm_medium=post&utm_campaign=blog-how-to-use-google-colab-for-deep-learning-complete-tutorial&utm_content=googlecolab ]

• Deep Learning for AI [ https://cacm.acm.org/magazines/2021/7/253464-deep-learning-for-ai/fulltext ]

Footnote: Having problem accessing papers?

• Visit https://sci-hub.now.sh/ and follow the instructions.