[2023-1] My Reading List by SELI LEE https://padlet.com/sally20921/myreadinglist @winter vacation (Jan, Feb) en-us 2023-02-26 20:31:03 UTC 2025-11-24 16:03:48 UTC hello@padlet.com https://padlet.net/icons/png/1f48e.png Original NeRF Paper sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495500752 Mildenhall, Ben, et al. "Nerf: Representing scenes as neural radiance fields for view synthesis." Communications of the ACM 65.1 (2021): 99-106.]]> 2023-02-26 20:39:45 UTC https://padlet.com/sally20921/myreadinglist/wish/2495500752 NeRF in the Wild sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495503102 Martin-Brualla, Ricardo, et al. "Nerf in the wild: Neural radiance fields for unconstrained photo collections." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.]]> 2023-02-26 20:44:18 UTC https://padlet.com/sally20921/myreadinglist/wish/2495503102 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495504509 Kendall, Alex, and Yarin Gal. "What uncertainties do we need in bayesian deep learning for computer vision?." Advances in neural information processing systems 30 (2017).]]> 2023-02-26 20:47:26 UTC https://padlet.com/sally20921/myreadinglist/wish/2495504509 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495505942 Gustafsson, Fredrik K., Martin Danelljan, and Thomas B. Schon. "Evaluating scalable bayesian deep learning methods for robust computer vision." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020.]]> 2023-02-26 20:51:00 UTC https://padlet.com/sally20921/myreadinglist/wish/2495505942 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495506763 Abdar, Moloud, et al. "A review of uncertainty quantification in deep learning: Techniques, applications and challenges." Information Fusion 76 (2021): 243-297.]]> 2023-02-26 20:52:47 UTC https://padlet.com/sally20921/myreadinglist/wish/2495506763 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495507609 Shen, Jianxiong, et al. "Stochastic neural radiance fields: Quantifying uncertainty in implicit 3d representations." 2021 International Conference on 3D Vision (3DV). IEEE, 2021.]]> 2023-02-26 20:54:24 UTC https://padlet.com/sally20921/myreadinglist/wish/2495507609 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495508115 Jospin, Laurent Valentin, et al. "Hands-on Bayesian neural networks—A tutorial for deep learning users." IEEE Computational Intelligence Magazine 17.2 (2022): 29-48.]]> 2023-02-26 20:55:43 UTC https://padlet.com/sally20921/myreadinglist/wish/2495508115 Safe and Uncertainty-Aware Learning: How Can We Predict and React to Rare but Potentially Disastrous Events? sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495512232 Theoretical foundations of risk-sensitive decision-making and learning. Deployment of safety-critical systems in uncertain environments requires predicting and reacting to rare but potentially disastrous event. The focus is to devise risk-sensitive algorithm for various types of real world scenarios. This includes algorithms for risk-sensitive planning, for inferring the profile of risk-sensitive expert (e.g., inverse reinforcement learning, imitation learning). for interactive decision making for self-driving cars (e.g., for traffic weaving scenarios), for safe transfer of control policies from simulation environment to the real world (e.g., autonomous driving in varying weather conditions), and new techniques to merge formal methods with stochastic optimal control and deep learning for high-confidence implementation on safety-critical systems. (https://stanfordasl.github.io//projects/SafeUncertLearning/)]]> 2023-02-26 21:05:06 UTC https://padlet.com/sally20921/myreadinglist/wish/2495512232 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495513374 Tang, Xiaolin, Kai Yang, Hong Wang, Jiahang Wu, Yechen Qin, Wenhao Yu, and Dongpu Cao. "Prediction-Uncertainty-Aware Decision-Making for Autonomous Vehicles." IEEE Transactions on Intelligent Vehicles 7.4 (2022): 849-62. Web.]]> 2023-02-26 21:07:53 UTC https://padlet.com/sally20921/myreadinglist/wish/2495513374 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495514146 Yang, Guo-Wei, et al. "Recursive-NeRF: An efficient and dynamically growing NeRF." IEEE Transactions on Visualization and Computer Graphics (2022).]]> 2023-02-26 21:09:31 UTC https://padlet.com/sally20921/myreadinglist/wish/2495514146 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495514752 Pan, Xuran, et al. "ActiveNeRF: Learning Where to See with Uncertainty Estimation." Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII. Cham: Springer Nature Switzerland, 2022.]]> 2023-02-26 21:11:02 UTC https://padlet.com/sally20921/myreadinglist/wish/2495514752 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495515446 Sünderhauf, Niko, Jad Abou-Chakra, and Dimity Miller. "Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields." arXiv preprint arXiv:2209.08718 (2022).]]> 2023-02-26 21:12:41 UTC https://padlet.com/sally20921/myreadinglist/wish/2495515446 sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495515987 Hoffman, Matthew D., et al. "ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images." arXiv preprint arXiv:2210.17415 (2022).]]> 2023-02-26 21:13:54 UTC https://padlet.com/sally20921/myreadinglist/wish/2495515987 Gentle, James E. Matrix Algebra: Theory, Computations, and Applications in Statistics. Springer Science & Business Media, 2007. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495523310 Part 1 Linear Algebra
1. Basic Vector/Matrix Structure and Notation
2. Vector and Vector Spaces
3. Basic Properties of Matrices
4. Vector/Matrix Derivatives and Integrals
5. Matrix Transformation and Factorizations
6. Solutions of Linear Systems
7. Evaluation of Eigenvalues and Eigenvectors

Part 2 Applications in Data Analysis
8 Special Matrices and Operations Useful in Modeling and Data Analysis
9 Selected Applications in Statistics

]]> 2023-02-26 21:26:10 UTC https://padlet.com/sally20921/myreadinglist/wish/2495523310 Mathematics for Intelligent Systems sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495530337 M3309.002700 

1 How to Solve Linear Equations
2 Theory of Linear Equations
3 Cholesky, LU, QR Decomposition
4 Least Square, Least Norm Problems
5 Eigenvalues and Eigenvectors
6 Positive Definite Matrices
7 Pseudo-Inverses and Generalized Inverses
8 Singular Value Decomposition
9 Gradient-Based Optimization
10 Newton Method
11 Conjugate Gradient Method
12 Linear Programming
13 Convex Optimization]]> 2023-02-26 21:40:01 UTC https://padlet.com/sally20921/myreadinglist/wish/2495530337 Haykin, Simon S. Neural Networks and Learning Machines. Prentice Hall, 2009. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495530936 1 RosenBlatt's Perceptron
2 Model Building through Regression
3 The Least-Mean-Square Algorithm
4 Multilayer Perceptrons
5 Kernel Methods and Radial-Basis Function Networks
6 Support Vector Machines
7 Regularization Theory
8 Principal-Component Analysis
9 Self-Organizing Maps
10 Information-Theoretic Learning Models
11 Stochastic Methods Rooted in Statistical Mechanics
12 Dynamic Programming
13 Neurodynamics
14 Bayesian Filtering for State Estimation of Dynamic Systems
15 Dynamically Driven Recurrent Networks

]]> 2023-02-26 21:41:25 UTC https://padlet.com/sally20921/myreadinglist/wish/2495530936 Thurner, Stefan, et al. Introduction to the Theory of Complex Systems. Oxford University Press, 2019. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495534500 1 Introduction to Complex Systems
2 Probability and Random Processes
3 Scaling
4 Networks
5 Evolutionary Processes
6 Statistical Mechanics and Information Theory for Complex Systems
7 The Future of the Science of Complex Systems

]]> 2023-02-26 21:50:30 UTC https://padlet.com/sally20921/myreadinglist/wish/2495534500 Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach, Global Edition. 2016. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495538837 Part 1 Artificial Intelligence
1 Introduction
2 Intelligent Agents

Part 2 Problem-Solving
3 Solving Problems by Searching
4 Search in Complex Environments
5 Constraint Satisfaction Problems
6 Adversarial Search and Games

Part 3 Knowledge, Reasoning, and Planning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
11 Automated Planning

Part 4 Uncertain Knowledge and Reasoning
12 Quantifying Uncertainty
13 Probabilistic Reasoning
14 Probabilistic Reasoning over Time
15 Making Simple Decisions
16 Making Complex Decisions
17 Multi-Agent Decision Making
18 Probabilistic Programming

Part 5 Machine Learning
19 Learning from Examples
20 Knowledge in Learning
21 Learning Probabilistic Models
22 Deep Learning
23 Reinforcement Learning

Part 6 Communicating, Perceiving, and Acting
24 Natural Language Processing
25 Deep Learning for Natural Language Processing
26 Robotics
27 Computer Vision 
28 Philosophy, Ethics, and Safety of AI
29 The Future of AI

Appendix A. Mathematical Background
A.1 Complexity Analysis
A.2 Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions

]]> 2023-02-26 22:00:17 UTC https://padlet.com/sally20921/myreadinglist/wish/2495538837 Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2005. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495547601 1 Introduction
1.1 A Pictorial Introduction to Bayesian Modelling
1.2 Roadmap

2. Regression
2.1 Weight-Space View
2.2 Function-Space View
2.3 Varying the Hyperparameters
2.4 Decision Theory for Regression
2.5 An Example Application
2.6 Smoothing, Weight Functions and Equivalent Kernels
2.7 Incorporating Explicit Basic Functions

3 Classification
3.1 Classification Problems
3.2 Linear Models for Classification
3.3 Gaussian Process Classification
3.4 The Laplace Approximation for The Binary GP Classifier
3.5 Multi-Class Laplace Approximation
3.6 Expectation Propagation
3.7 Experiments

4 Covariance Functions
4.1 Preliminaries
4.2 Examples of Covariance Functions
4.3 Eigenfunction Analysis of Kernels
4.4 Kernels for non-Vectorial Input

5 Model Selection and Adaptation of Hyerparameters
5.1 The Model Selection Problem
5.2 Bayesian Model Selection
5.3 Cross-Validation
5.4 Model Selection for GP Regression
5.5 Model Selection for GP Classification

Appendix A Mathematical Background
A.1 Joint, Marginal, and Conditional Probability
A.2 Gaussian Identities
A.3 Matrix Identities
A.4 Cholesky Decomposition
A.5 Entropy and Kullback-Leibler Divergence
A.6 Limits
A.7 Measure and Integration
A.8 Fourier Transforms 
A.9 Convexity

Appendix B Gaussian Markov Processes
B.1 Fourier Analysis
B.2 Continuous-Time Gaussian Markov Processes
B.3 Discrete-Time Gaussian Markov Processes
B.4 The Relationship Between Discrete-Time and Sampled Continuous-Time GMPs
B.5 Markov Processes in Higher Dimensions]]> 2023-02-26 22:21:27 UTC https://padlet.com/sally20921/myreadinglist/wish/2495547601 Orland, Paul. Math for Programmers: 3D Graphics, Machine Learning, and Simulations with Python. Manning Publications, 2021. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495549012 2023-02-26 22:25:18 UTC https://padlet.com/sally20921/myreadinglist/wish/2495549012 Barber, David. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495551107 2023-02-26 22:30:11 UTC https://padlet.com/sally20921/myreadinglist/wish/2495551107 Hertzmann, Aaron. Introduction to Bayesian Reasoning. Course Notes, 15 Sept. 2004. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495552601 https://drive.google.com/file/d/1RMS__cJgsOJihLogESYNRrW9UDkD0fFD/view?usp=share_link]]> 2023-02-26 22:33:43 UTC https://padlet.com/sally20921/myreadinglist/wish/2495552601 Yu, Alex, et al. "pixelnerf: Neural radiance fields from one or few images." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495567274 2023-02-26 23:10:29 UTC https://padlet.com/sally20921/myreadinglist/wish/2495567274 Park, Keunhong, et al. "Nerfies: Deformable neural radiance fields." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495568305 2023-02-26 23:12:46 UTC https://padlet.com/sally20921/myreadinglist/wish/2495568305 Neural Fields in Visual Computing and Beyond sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495572267 2023-02-26 23:20:34 UTC https://padlet.com/sally20921/myreadinglist/wish/2495572267 A PyTorch Library and Engine for Neural Fields Research sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495572497 https://github.com/NVIDIAGameWorks/kaolin-wisp.git
https://github.com/NVIDIAGameWorks/kaolin-wisp.git]]> 2023-02-26 23:20:57 UTC https://padlet.com/sally20921/myreadinglist/wish/2495572497 Special Topics on Embedded Systems: Neural Rendering sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495584607 2023-02-26 23:43:27 UTC https://padlet.com/sally20921/myreadinglist/wish/2495584607 Ma, Xudong, et al. 3D Deep Learning with Python: Design and Develop Your Computer Vision Model with 3D Data Using PyTorch3D and More. Packt Publishing Ltd, 2022. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495586426 Part 1 3D Data Processing Basics
1 Introducing 3D Data Processing
1.1 Understanding Mesh Representation
1.2 Understanding Voxel Representation
1.3 Coding for Camera Models and Coordination Systems

2 Introducing 3D Computer Vision and Geometry
2.1 Exploring the Basic Concepts of Rendering, Rasterization, and Shading
2.3 Understanding Transformations and Rotations

3 3D Deep Learning Using PyTorch3D
3.1 Fitting Meshes to Point Clouds
3.2 Loss Functions for Regularization
3.3 Implementing the Mesh Fitting with PyTorch3D

4 Learning Object Pose Detection and Tracking by Differentiable Rendering
4.1 How to Make Rendering Differentiable
4.2 The Object Pose Estimation Problem

5 Understanding Differentiable Volumetric Rendering
5.1 Overview of Volumetric Rendering
5.2 Understanding Ray Sampling
5.3 Differentiable Volumetric Rendering

6 Exploring Neural Radiance Fields (NeRF)
6.1 Understanding NeRF
6.2 What Is a Radiance Field?
6.3 Representing Radiance Fields with NN
6.4 Training a NeRF Model
6.5 Projecting Rays into the Scene
6.6 Accumulating the Color of a Ray

Part 3 State-of-the-Art 3D Deep Learning Using PyTorch3D
7 Exploring Controllable Neural Feature Fields
7.1 Compositional 3d-Aware Image Synthesis
7.2 Generating Feature Fields
7.3 Controllable Scene Generation
7.4 Controllable Car Generation
7.5 Controllable Face Generation 
7.6 Training the GIRAFFE Model
7.7 Frechet Inception Distance


]]> 2023-02-26 23:47:13 UTC https://padlet.com/sally20921/myreadinglist/wish/2495586426 Janke, Steven J. Mathematical Structures for Computer Graphics. John Wiley & Sons, 2014. sally20921 https://padlet.com/sally20921/myreadinglist/wish/2495705365 1 Basics
1.1 Graphics Pipeline

2 Vector Algebra
3 Vector Geometry
3.1 Homogeneous Coordinates

4 Transformations
4.1 Linear Transformations
4.2 Three Dimensions
4.3 Affine Transformation

5 Orientation
5.1 Cartesian Coordinate Systems
5.2 Cameras
5.3 Polar, Cylindrical, and Spherical Coordinates

6 Polygons and Polyhedra
6.1 Interpolation
6.2 Polygons
6.3 Polyhedra

7 Curves and Surfaces

]]> 2023-02-27 02:41:35 UTC https://padlet.com/sally20921/myreadinglist/wish/2495705365 McGill University sally20921 https://padlet.com/sally20921/myreadinglist/wish/2646600902 Benjamin Fung on mobile device
Canada Research Chair in Data Mining for Cybersecurity
Graduate Program Director - MISt & Graduate Certificates
Professor, School of Information Studies
McGill University, Montreal, Canada
http://dmas.lab.mcgill.ca/fung
(click on Schedule to make an appointment)
]]> 2023-07-17 21:36:15 UTC https://padlet.com/sally20921/myreadinglist/wish/2646600902