Computer Vision: Algorithms and Applications, 2nd ed, Richard Szeliski, 2022
Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016
Understanding Deep Learning, Simon J. Prince, 2023
(book site URL: https://udlbook.github.io/udlbook/)
Highly Accurate Dichotomous Image Segmentation, Xuebin Qin et al, 2022
Anomalib v1.0.1: Unveiling Anomaly Detection on Plastic Surfaces, Vincent Liu, Medium, 2024
related repo: https://github.com/openvinotoolkit/anomalib
Feature Visualization, Chris Olah et al, OpenAI, 2017
Zoom In: An Introduction to Circuits, Chris Olah et al, OpenAI, 2020
as pdf here
An Overview of Early Vision in InceptionV1, Chris Olah et al, OpenAI, 2020
as pdf here
Curve Detectors, Nick Cammarata et al, OpenAI, 2020
as pdf here
Naturally Occurring Equivariance in Neural Networks, Chris Olah et al, OpenAI, 2020
High-Low Frequency Detectors, Ludwig Schubert et al, OpenAI, 2021
Curve Circuits, Nick Cammarata et al, OpenAI, 2021
Visualizing Weights, Chelsea Voss et al, OpenAI, 2021
Branch Specialization, Chelsea Voss et al, OpenAI, 2021
Weight Banding, Michael Petrov et al, OpenAI, 2021
Going deeper with convolutions, Christian Szegedy et al, Google, 2014
Distilling Singular Learning Theory, Liam Carroll, June 2023
The RLCT Measures the Effective Dimension of Neural Networks, Liam Carroll, June 2023
Why Neural Networks Obey Occam's Razor, Liam Carroll, June 2023
Neural Networks Are Singular, Liam Carroll, June 2023
Phase Transitions in Neural Networks, Liam Carroll, June 2023
Neural Networks: Unleashing the Power of Latent Space Compression by Julien Pascal, May 2023, Medium
Autoencoders, Unsupervised Learning, and Deep Architectures, Pierre Baldi, 2012
Neural Networks: Unleashing the Power of Latent Space Compression, Julien Pascal, Medium, 2023
The Sparse Autoencoder, Andrew Ng, Lecture Notes CS294A
Tutorial On Principal Component Analysis, Jonathon Shlens, Google Research, 2014
Long Short-Term Memory, Sepp Hochreiter et al., 1997
LSTM Can Solve Hard Long Time Lag Problems, Sepp Hochreiter, Juergen Schmidthhuber, NIPS, 1996
Recurent Models of Visual Attention, Volodimir Mnih et al, 2014
Sequence to Sequence Learning with Neural Networks, Sutskever et al, Google Research, 2014
Generating Sequences with Recurrent Neural Networks, Alex Graves, UofToronto, 2014
The Unreasonable Effectiveness of Recurrent Neural Networks, Andrej Karpathy's blog, May 2015
Understanding LSTM: a tutorial into Long Short-Term Memory, R. Staudemeyer et al., 2019
Understanding LSTM: Colah's Blog, 2015
as pdf here
A Mathematical Framework for Transformer Circuits, Nelson Elhage et al, Anthropic, 2021
An Image is Worth 16X16 Wwords: Transformers for Image Recognition at Scale, A. Dosovitskiy, 2021
Attention Is All You Need, Vaswani et al, Google Brain, 2017
Do Vision Transformers See Like Convolutional Neural Networks? M. Raghu, Google Brain, 2022
How Do Vision Transformers Work? N. Park et al, 2022
The Annotated Transformer - delving into Vaswani's paper "Attention Is All You Need", 2018
as pdf here
The Illustrated Transformer, Jay Alamar's blog, 2021
as pdf here
The Transformer - Attention Is All You Need - Michal Chromiak's blog, 2017
Transformers for Image Recognition at Scale, Nel Houlsby and Dirk Weissenborn, Dec 2020, blog
as pdf here
An Introduction to Transformers: an NLP Perspective, T. Xiao et al, 2023
Transforming Auto-encoders, G. Hinton, A. Krizhevsky, et al., 2011
Understanding Transformer Reasoning Capabilities via Graph Algorithms, Clayton Sanford et al, 2024
Lecture 2 on Transformers from CMU CS 10-423 (GenAI) given in Jan 2024
MLP-Mixer: An all-MLP Architecture for Vision, I. Tolstikhin et al, Google, 2021
Introduction to Diffusion Models for Deep Learning, Ryan O'Connor, 2022 (online blog)
What are Diffusion Models? Lilian Weng, OpenAI, 2021 (online blog)
Diffusion Models for Video Generation, Lilian Weng, OpenAI, 2024 (online blog)
Step-By-Step Diffusion: An Elementary Tutorial, P. Nakkiran et al, 2024
Introduction to Flow Matching, Tor Fjelde, Emile Mathieu, Vincent Dutordoir, 2024 (online blog)
Building Diffusion Model's theory from ground up, Ayan Das, ICRL blogposts, 2024
as a pdf: here
Perspectives on Diffusion, Sander Dieleman, 2023
as a pdf: here
Lightweight Diffusion Models: A Survey, W. Song et al, 2024
Flow Matching For Generative Modeling, Y. Lipman et al, Meta AI, 2023
Tutorial on Diffusion Models for Imaging and Vision, Stanley Chan, 2024
On Error Propagation of Diffusion Models, Y. Li, Michaela van der Schaar, U of Cambridge, 2024
Consistency Models, Y. Song et al, 2023
Understanding Diffusion Models: Unified Perspective, Calvin Luo, Google Brain, 2022
Diffusion Models Beat GANs on Image Synthesis, Prafulla Dharival, Alex Nichol, OpenAI, 2021
Generative Models of Images and Neural Networks, William Smith Peebles, PhD Thesis, 2023
Denoising Diffusion Probabilistic Models, J. Ho et al, UC Berkeley, 2020
Denoising Diffusion Implicit Models, J. Song et al, 2021
Sampling, Diffusion, and Stochastic Localization, Andrea Montanari, 2023
Demystifying Variational Diffusion Models, Fabio De Sousa Ribeiro et al, Imperial College, 2024
Improved Techniques for Training Score-Based Generative Models, Y. Song, S. Ermon, 2020
Reverse Time Stochastic Differential Equations for Generative Modeling, Ludwig Winkler, 2021
Reverse Time Diffusion Equation Models, Brian DO Anderson, U. of Newcastle, 1980
On The Mathematics of Diffusion Models, David McAllester, 2023
Navier-Stokes, Fluid Dynamics, and Image and Video Inpainting, M. Bertalmio et al, 2001
An Image Inpainting Technique Based on the Fast Marching Method, A. Telea, 2004
Lecture 7 on Diffusion Models from CMU CS 10-423 (GenAI) given in Feb 2024
Denoising Diffusion Model from scratch using PyTorch, Mickael Boillaud, 2024, Medium
as pdf: here
related repo: https://github.com/Camaltra/this-is-not-real-aerial-imagery/tree/main/src/ai
related paper: Denoising Diffusion Probabilistic Models, J. Ho et al, UC Berkeley, 2020
related paper: ACC-UNet: A Completely Convolutional UNet model for the 2020s, Nabil Ibtehaz et al, Purdue U., 2023
related paper: A ConvNet for the 2020s, Z. Liu et al, Meta FAIR, 2022
related paper: U-Net: Convolutional Networks for Biomedical Image Segmentation, Olaf Ronneberger et al, U. of Freiburg, 2015
Will Diffusion Models Be The Next Frontier of Deep Learning, Devansh, Medium, 2024
Variational Inference: Foundations and Innovations, David Blei, Columbia U., slides, 2018
Variational Inference: A Review for Statisticians, David Blei et al, Columbia U., 2018
Automatic Variational Inference in Stan, Alp Kucukelbir et al, Columbia U., 2018
Automatic Differentiation Variational Inference, Alp Kucukelbir et al, Columbia U., 2017
Lecture 8 Diffusion Modeling + Variational Inference from CMU CS 10-423 (GenAI) given in Feb 2024
Python library containing Variational Inference algorithms such as ADVI : PyMC
Intuitively Understanding Variational Autoencoders, Irhum Shafkat, Towards Data Science, 2018
as a pdf file here
Diffusion Models as a kind of VAE, Angus Turner, 2021, online article
Understanding Variational Autoencoders by Joseph Rocca, Towards Data Science, Sept, 2019
as a pdf file here
Variational AutoEncoders (VAE) with PyTorch, Alexander Van de Kleut (online blog)
VAEs in Reinforcement Learning, Nicholsonjm, Medium, 2024
related Gym environment: https://gymnasium.farama.org/environments/mujoco/swimmer/
related paper: Variational State Encoding as Intrinsic Motivation in Reinforcement Learning, Martin Klissarov et al, McGill, ICLR 2019
Tutorial on Variational Autoencoders, Carl Doersch, Carnegie Mellon, UC Berkeley, 2021
Convolutional Variational Autoencoder with tensorflow, online Tensorflow page
Introduction to Variational Autoencoders, Diedrik P. Kingma, Max Welling, 2019
Auto-Encoding Variational Bayes, Diedrik P. Kingma, Max Welling, 2022
The Sparse Autoencoder, Andrew Ng, Lecture Notes CS294A
Lecture 9 on Variational Autoencoders from CMU CS 10-423 (GenAI) given in Feb 2024
Scalable Diffusion Models with Transformers, William Peebles, UC Berkeley, 2022
Masked Diffusion Transformer is a Strong Image Synthesizer, S. Gao et al Sea AI Lab, Nankai U., 2023
FiT: Flexible Vision Transformer for Diffusion Model, Z. Lu et al, Feb 2024
DiffiT: Diffusion Vision Transformers for Image Generation, A. Hatamizadeh et al, 2024
Diffusion Transformer (DiT) Models: A Beginner’s Guide, Akruti Acharya, March 18, 2024
Deep Reinforcement Learnng from Human Preferences, Paul Christiano et al, OpenAI, 2017
Training Language Models to Follow Instructions With Human Feedback, L. Ouyang et al, OpenAI, 2022
Fine Tuning Language Models from Human Preferences, Daniel M. Ziegler et al, OpenAI, 2020
Learning to Summarize from Human Feedback, Nisan Stiennon et al, OpenAI, 2022
Learning from human preferences, Dario Amodei, OpenAI blog, 2017
Reinforcement Learning fro Human Feedback, Wikipedia
SLiC-HF: Sequence Likelihood Calibration with Human Feedback, Y. Zhao et al, Google Deepmind, 2023
KTO: Model Alignment as Prospect Theoretic Optimization, K. Ethayarajh et al, Stanford U., 2024
ORPO: Monolithic Preference Optimization without Reference Model, Hong, 2024
Lecture 12 on RLHF from CMU CS 10-423 (GenAI) given in Feb 2024
CS231n: Convolutional Neural Networks for Image Recognition: Stanford CS class
github repo: https://github.com/cs231n/cs231n.github.io
CMU CS 10-423 GenAI class taught in Jan-Feb 2024
slides for the class: slides
MIT 6.S191: Introduction to Deep Learning, Alexander Amini, years 2020-2023 playlist
MIT 6.S191 (2023): Introduction to Deep Learning
MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention
MIT 6.S191 (2023): Convolutional Neural Networks
MIT 6.S191 (2023): Deep Generative Modeling
MIT 6.S191 (2023): Robust and Trustworthy Deep Learning
MIT 6.S191 (2023): Reinforcement Learning
MIT 6.S191 (2023): Deep Learning New Frontiers
MIT 6.S191 (2023): Text-to-Image Generation