Notes
Why Create This Page
I was getting tired of taking notes that involve a lot of math equations using the
Lexical Rich Text Editor, so I am creating this page, where I can upload a
file and create an article purely from the
markup. I am also creating this page to practice writing in hopes of improving my knowledge of the markup language.
I plan on using this page to provide links to notes on things, mostly math-heavy textbooks and research papers, that I took using and to keep track of research papers or math-heavy textbooks that I plan to read in the future. I plan on using
Tex4ht, combined with some post-processing, to convert the uploaded documents to HTML. You can use the document below to test out this to HTML system.
TikTok Recommendation System Paper
Monolith: Real Time Recommendation System With Collisionless Embedding Table
I heard that TikTok published a paper on their recommendation engine sometime ago, and since I know some things about recommendation systems and plan ...
Gradient-Based Learning Applied to Document Recognition
Gradient-Based Learning Applied to Document Recognition Paper
Looking for more research papers to read, I scanned my Hands-On Machine Learning notes for the many papers that were referenced there. This is one of ...
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Dropout: A Simple Way to Prevent Neural Networks from Overfitting Paper
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Neural Image Caption Generation with Visual Attention
Neural Image Caption Generation with Visual Attention Paper
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YOLOv4: An Incremental Improvement
YOLOv4: An Incremental Improvement Paper
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YOLO9000: Better, Faster, Stronger
YOLO9000: Better, Faster, Stronger Paper
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You Only Look Once: Unified, Real-Time Object Detection
You Only Look Once: Unified, Real-Time Object Detection Paper
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Fully Convolutional Networks for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation Paper
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Crowdsourcing in Computer Vision
Crowdsourcing in Computer Vision Paper
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Squeeze-and-Excitation Network
Squeeze-and-Excitation Network Paper
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Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable Convolutions Paper
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Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition Paper
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Very Deep Convlutional Networks for Large-Scale Image Recognition
Very Deep Convlutional Networks for Large-Scale Image Recognition Paper
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Going Deeper with Convolutions
Going Deeper with Convolutions Paper
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ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks Paper
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neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position
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Receptive Fields and Function Architecture of Monkey Striate Cortex
Receptive Fields and Function Architecture of Monkey Striate Cortex Paper
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Receptive Fields of Single Neurones in The Cat's Striate Cortex
Receptive Fields of Single Neurones in The Cat's Striate Cortex Paper
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Single Unit Activity in Striate Cortex of Unrestrained Cats
Single Unit Activity in Striate Cortex of Unrestrained Cats Paper
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Distributed Representations of Words and Phrases and their Compositionality
Distributed Representations of Words and Phrases and their Compositionality Paper
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Paper
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Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors
Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors Paper
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Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization Paper
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization Paper
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Some methods of speeding up the convergence of iteration methods
Some methods of speeding up the convergence of iteration methods Paper
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Paper
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Self-Normalizing Neural Networks
Self-Normalizing Neural Networks Paper
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Fast and Accurate Deep Learning by Exponential Linear Units (ELUs)
Fast and Accurate Deep Learning by Exponential Linear Units (ELUs) Paper
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Empirical Evaluation of Rectified Activations in Convolutional Network
Empirical Evaluation of Rectified Activations in Convolutional Network Paper
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Paper
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Practical recommendations for gradient-based training of deep architectures
Practical recommendations for gradient-based training of deep architectures Paper
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Using Evolutionary AutoML to Discover Neural Network Architectures
Using Evolutionary AutoML to Discover Neural Network Architectures Paper
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Population Based Training of Neural Networks
Population Based Training of Neural Networks Paper
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Wide & Deep Learning for Recommender Systems
Wide & Deep Learning for Recommender Systems Paper
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Web-scale k-means clustering
Web-scale k-means clustering Paper
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Least Square Quantization in PCM
Least Square Quantization in PCM Paper
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Fast Kernel Classifiers with Online and Active Learning
Fast Kernel Classifiers with Online and Active Learning Paper
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A Dual Coordinate Descent Method for Large-scale Linear SVM
A Dual Coordinate Descent Method for Large-scale Linear SVM Paper
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Stacked Generalization
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting Paper
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A Mathematical Theory of Communication
A Mathematical Theory of Communication Paper
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A Tutorial Introduction to the Minimum Description Length Principle
I am reading this paper because it was recommended as part of Ilya Sutskever's approx. 30 papers that he recommended to John Carmack to learn what rea...
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
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Neural Turing Machines
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Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
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Variational Lossy Autoencoder
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A Simple NN Module for Relational Reasoning
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Identity Mappings in Deep Residual Networks
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Neural Machine Translation by Jointly Learning to Align and Translate
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Neural Message Passing for Quantum Chemistry
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Multi-Scale Context Aggregation by Dilated Convolutions
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GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
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Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
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Notes
January, 2025
A Logical Calculus of Ideas Immanent in Nervous Activity
Looking for more research papers to read, I scanned my Hands-On Machine Learning notes for the many papers that were referenced there. This is one of ...
Using the Triangle Inequality to Accelerate k-Means
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Sequential Minimal Optimization: A fast Algorithm for Training Support Vector Machines
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Incremental and Decremental SVM Learning
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Nonlinear Dimensionality Reduction by Locally Linear Embedding
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MTEB: Massive Text Embedding Benchmark
I need to do a lot with text embeddings, so I am going to read this paper. This paper establishes a benchmark to test text embeddings.
ARCING THE EDGE
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Understanding the Difficulty of Training Deep Feedforward Neural Networks
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Kernel Principal Component Analysis
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Diffusion quality diffuses in the digital public square
I wanted to read this comment because it should affect the process by which comments are initially displayed on a post.
Extremely Randomized Trees
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Random Decision Forests
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The Random Subspace Method for Constructing Decision Forests
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Ensembles on Random Patches
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Pasting Small Votes for Classification in Large Databases and On-Line
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Bagging Predictors
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The Lack of A Priori Distinctions Between Learning Algorithms
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API design for machine learning software: experiences from the scikit-learn project
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Scaling to very very large corpora for natural language disambiguation
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The Unreasonable Effectiveness of Data
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Pointer Networks
I am reading this paper because it was recommended as part of Ilya Sutskever's approx. 30 papers that he recommended to John Carmack to learn what rea...
Order Matters: Sequence to Sequence for Sets
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Relational Recurrent Neural Networks
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Deep Residual Learning for Image Recognition
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Attention Is All You Need
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Recurrent Neural Network Regularization
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Understanding LSTM Networks
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ImageNet Classification with Deep Convolutional Neural Networks
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The Unreasonable Effectiveness of Recurrent Neural Networks
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Scaling Laws for Neural Language Models
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