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Probing The Full Monty Hall Problem

Probing The Full Monty Hall Problem

A tutorial on the Monty Hall problem in statistics.

High Dimension Data Analysis - A tutorial and review for Dimensionality Reduction Techniques

High Dimension Data Analysis - A tutorial and review for Dimensionality Reduction Techniques

This article explains and provides a comparative study of a few techniques for dimensionality reduction. It dives into the mathematical explanation of several feature selection and feature transformation techniques, while also providing the algorithmic representation and implementation of some other techniques. Lastly, it also provides a very brief review of various other works done in this space.

The application of Hamilton-Jacobi equation in reaction-diffusion equations.

The application of Hamilton-Jacobi equation in reaction-diffusion equations.

Hamilton-Jacobi equation

Probability: The Basics

Probability: The Basics

These notes are intended to provide a quick refresher on basics of probability before introduction to the use of randomization in algorithms.

Group Equivariant Convolutional Networks in Medical Image Analysis

Group Equivariant Convolutional Networks in Medical Image Analysis

This is a brief review of G-CNNs' applications in medical image analysis, including fundamental knowledge of group equivariant convolutional networks, and applications in medical images' classification and segmentation.

Information Theory in Machine Learning

Information Theory in Machine Learning

This review gives a comprehensive study of application of information theory in Machine Learning methods and algorithms.

Topological Aspects Of Gauge Theories

Topological Aspects Of Gauge Theories

In this article we shall discuss gauge theories in non-trivial topology configuration spaces.

The Smart Fire Extinguisher - A Sprinkler System Alternative for Early Fire Detection and Prevention

The Smart Fire Extinguisher - A Sprinkler System Alternative for Early Fire Detection and Prevention

This article describes the development of a modular, compact, low cost sprinkler system alternative for early fire detection and prevention. It goes through building a functional system and includes a video summary featuring the finalized prototype.

Good Vibes- Characterizing Low Cost Piezoelectric Sensors for Industry

Good Vibes- Characterizing Low Cost Piezoelectric Sensors for Industry

This project characterizes piezoelectric vibration sensors and determines their utility in impact detection applications. The team developed one-dimensional and two-dimensional testing apparatuses in order to predict the height and location of impact of a dropped ball using this inexpensive sensor. After attempting a method of trilateration to limited success, a discrete integration-based approach was developed to predict the location of impact. Through optimized experimentation processes and discretized algorithms, the project was able to predict the impact location within a 10 cm by 10 cm square with a calculated accuracy of 79%.

Invariant Information Clustering

Invariant Information Clustering

This paper introduces a principled clustering objective based on maximizing Mutual Information (MI) between paired data samples under a bottleneck, equivalent to distilling their shared abstract content (co-clustering), that tends to avoid degenerate clustering solutions.

The use of Mathpix OCR with EDICO scientific editor to help blind Students in STEM education

The use of Mathpix OCR with EDICO scientific editor to help blind Students in STEM education

In this tutorial we'll show how Mathpix OCR is helpful to instantly transpose math and science assignments both in braille and speech. We'll use the free EDICO Scientific Editor to demonstrate how a math assignment can be imported using Mathpix technology, and how it can be solved using a Refreshable Braille Display.

L^p^ → L^∞^

L^p^ → L^∞^

A short proof of a thought-provoking relation between the
-norms.

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms.

Event Camera: the Next Generation of Visual Perception System

Event Camera: the Next Generation of Visual Perception System

Event camera can extend computer vision to scenarios where it is currently incompetent. In the following decades, it is hopeful that event cameras will be mature enough to be mass-produced, to have dedicated algorithms, and to show up in widely-used products.

Manipulative Attacks in Group Identification

Manipulative Attacks in Group Identification

This review provides an introduction to the group identification problem and gives an overview of the feasibility and computational complexity of manipulative attacks in group identification.

Data Augmentation in Automatic Speech Recognition

Data Augmentation in Automatic Speech Recognition

An overview of recent advancements in data augmentation for automatic speech recognition.

Generative Adversarial Networks

Generative Adversarial Networks

Generative Adversarial Networks, their variants and their evaluation

Go-Explore: Reinforcement Learning Algorithms Tackling Hard-Explore Tasks

Go-Explore: Reinforcement Learning Algorithms Tackling Hard-Explore Tasks

A new family of reinforcement learning algorithms, Go-Explore, surpasses all previous approaches on hard-explore Atari games by addressing detachment and derailment.

About Unsupervised Domain Adaptation for Image Classification

About Unsupervised Domain Adaptation for Image Classification

The bulk of machine learning models have a tendancy to rely too strongly to the distribution of the data on which they have been trained. Through this review paper I propose to discuss about ways to design an image classifier able to generalize well on a different but related distribution from its training one.

Deep Sketch Generation Models

Deep Sketch Generation Models

This article reviews the recent aproaches and datasets in the deep generative sketch modelling field that takes the human-machine creative collaboration one step closer.

Visual Relationship Detection using Scene Graphs - A Survey

Visual Relationship Detection using Scene Graphs - A Survey

This review gives an introduction to Scene Graphs and their usage in various downstream tasks. Many of the recent methods for its generation have been discussed here in detail along with a detailed comparison between them.

Handwriting Text Recognition

Handwriting Text Recognition

This review introduces Handwriting Text Recognition (HTR), then mentions the different group of approaches for HTR, and finally summarizes the latest research in OCR techniques for offline handwritten recognition on documents.

Biomorphic AI

Biomorphic AI

In this chapter, we analyze current advances in AI development and their biological implications. I believe that it will be possible not only to convey information more easily, but also to create new ideas of improvement of modern algorithms using AI.

Approximation Algorithm: Survivable Network Design Problem

Approximation Algorithm: Survivable Network Design Problem

These are lecture notes for a course on approximation algorithms. Chapter 13: Survivable Network Design Problem.

Ground-penetrating radar-based underground environmental perception radar for robotic system

Ground-penetrating radar-based underground environmental perception radar for robotic system

In recent years, the environmental perception technology for robotic system has attracted a lot of attention from researchers, but only a little of studies on environmental perception technology are focused on the space underground Meanwhile, in the field of mobile robotic systems, with the development of research on underground emergency hedging and buried targets' high-resolution fault imaging, more and more attention has also been paid to underground environ-mental detection and perception. This article proposes a ground-penetrating radar-based underground environmental perception radar for mobile robotic system indoors.

Electron-Positron Pair Production in Plasma's: Implications for Magneto-sonic Waves

Electron-Positron Pair Production in Plasma's: Implications for Magneto-sonic Waves

I discuss the temperature limit in which pair production becomes important in plasma's. I also discuss the effect of pair production on plasma's modeled in the fluid limit.

Reconfigurable Computing Course Associated with the Long Short-Term Memory and the High-Level-Synthesize

Reconfigurable Computing Course Associated with the Long Short-Term Memory and the High-Level-Synthesize

Long short-term memory(LSTM)is a periodic neural network that is suitable for predicting and processing time series intervals and relatively long-delayed important events. In this paper, there are five Lab about the basic principles of LSTM and the methods of generating IP core in the Vivado HLS that has been finished. In addition, the results are confirmed on Zedboard. This experimental course help us understand the principle of LSTM and acquire the skills of cooperative development of ARM and FPGA.

Introduction to Factor Analysis

Introduction to Factor Analysis

These notes from Andrew Ng's CS229 course in Machine Learning discuss factor analysis.

Production principle of road and Bridge

Production principle of road and Bridge

This paper introduces some methods and principles in the production process of roads and bridges, and some places that are easy to ignore, for beginners' reference.

Targeted Proton Therapy for Safer Treatment of Brain Tumors

Targeted Proton Therapy for Safer Treatment of Brain Tumors

Radiation therapy tends to harm healthy tissue near the treated tumor, thereby increasing the risk of side effects and fatal outcomes in the context of brain tumors. This research paper investigates proton radiation therapy as a viable and generally safer option for the treatment of tumors in sensitive areas like the brain.

Help Me, Help You - Deep Learning for Quantum Control

Help Me, Help You - Deep Learning for Quantum Control

The enhanced processing power inherent in a proposed error-corrected quantum computer promises to accelerate the training of deep neural networks, among many other applications. In this review, we outline a major component of current quantum computers which requires improvement before this promise can be fulfilled, and reflect on the ways in which deep learning itself can alleviate this problem.

Approximation Algorithms: Primal Dual for Constrained Forest Problems

Approximation Algorithms: Primal Dual for Constrained Forest Problems

These are lecture notes for a course on approximation algorithms. Chapter 12: Primal Dual for Constrained Forest Problems.

Approximation Algorithms: Steiner Forest Problem

Approximation Algorithms: Steiner Forest Problem

These are lecture notes for a course on approximation algorithms. Chapter 11: Steiner Forest Problem.

Distribution

Distribution

This paper is concerned with definition of distribution.Some examples about distribution also are given in this paper.

Approximation Algorithms: Introduction to Network Design

Approximation Algorithms: Introduction to Network Design

These are lecture notes for a course on approximation algorithms. Chapter 10: Introduction to Network Design.

Approximation Algorithms: Clustering and Facility Location

Approximation Algorithms: Clustering and Facility Location

These are lecture notes for a course on approximation algorithms. Chapter 9: Clustering and Facility Location.

Approximation Algorithms: Introduction to Local Search

Approximation Algorithms: Introduction to Local Search

These are lecture notes for a course on approximation algorithms. Chapter 8: Introduction to Local Search.

Approximation Algorithms: Congestion Minimization in Networks

Approximation Algorithms: Congestion Minimization in Networks

These are lecture notes for a course on approximation algorithms. Chapter 7: Congestion Minimization in Networks.

Approximation Algorithms: Unrelated Machine Scheduling and Generalized Assignment

Approximation Algorithms: Unrelated Machine Scheduling and Generalized Assignment

These are lecture notes for a course on approximation algorithms. Chapter 6: Unrelated Machine Scheduling and Generalized Assignment.

Introduction to Social Dynamics through Tree Surgery

Introduction to Social Dynamics through Tree Surgery

This is a tutorial for predicting the behavior of large societies using so-called “tree surgery”. It introduces the theoretical and algorithmic methods as an alternative or a complement to agent-based simulations. The tools can be used to predict the emergent behavior of a large society of independent agents in many stylized situations - deciding to wear a facemask outdoors, to use a fork in medieval Italy [1], to rebel against an oppressive government.

Approximation Algorithms: Load Balancing and Bin Packing

Approximation Algorithms: Load Balancing and Bin Packing

These are lecture notes for a course on approximation algorithms. Chapter 5: Load Balancing and Bin Packing.

A 161mW 32Gb/s ADC-Based NRZ SerDes Receiver Front End in 28nm

A 161mW 32Gb/s ADC-Based NRZ SerDes Receiver Front End in 28nm

A 32-Gb/s NRZ ADC-based SerDes receiver front end is presented in TSMC
process. The front end consists of a degenerated CML combined with Gm-TIA Continuous Time Equalizer (CTLE) which provides equalization, gain as well as buffering at
, followed by a 32-way time-interleaved Analog-to-Digital Converter (TI-ADC), which is implemented in a
hierarchy.

A Review of Trustworthy Graph Learning

A Review of Trustworthy Graph Learning

In this review, we will explore how to develop trustworthy graph neural networks (GNNs) models from different aspects.

How Do We Move Towards True Artificial Intelligence

How Do We Move Towards True Artificial Intelligence

This review discusses the bottleneck challenges experienced in current artificial intelligence research, followed by an analysis of the essence of human intelligence, as well as the differences, advantages, and disadvantages of contemporary machine intelligence and human intelligence and a possible solution towards current problems faced in AI research is suggested.

Automatic Detection of Expressed Emotion from Five-Minute Speech Samples: Challenges and Opportunities

Automatic Detection of Expressed Emotion from Five-Minute Speech Samples: Challenges and Opportunities

Automatic Detection of Expressed Emotion from Five-Minute Speech Samples: Challenges and Opportunities

Approximation Algorithms: Packing Problems

Approximation Algorithms: Packing Problems

These are lecture notes for a course on approximation algorithms. Chapter 4: Packing Problems.

Approximation Algorithms: Knapsack

Approximation Algorithms: Knapsack

These are lecture notes for a course on approximation algorithms. Chapter 3: Knapsack.

Approximation Algorithms: Introduction

Approximation Algorithms: Introduction

These are lecture notes for a course on approximation algorithms. Chapter 1: Introduction.

Approximation Algorithms: Covering problems

Approximation Algorithms: Covering problems

These are lecture notes for a course on approximation algorithms. Chapter 2: Covering problems.

The set of matrices as a vector space

The set of matrices as a vector space

Definition for the specific case of matrices 2 times 3 of the matrices as a vector space

Find the inverse matrix using Gauss-Jordan method

Find the inverse matrix using Gauss-Jordan method

Here an explanation about how to find the inverse of a matrix using Gauss Jordan method

\ell_0 sampling, and priority sampling

\ell_0 sampling, and priority sampling

Notes from lecture 9 of Chandra Chekuri's course on algorithms for big data.

Quantiles and selection in multiple passes

Quantiles and selection in multiple passes

Notes from lecture 10 of Chandra Chekuri's course on algorithms for big data.

Count and Count-Min Sketches

Count and Count-Min Sketches

Notes from lecture 6 of Chandra Chekuri's course on algorithms for big data.

Estimating F_2 norm, Sketching, Johnson-Lindenstrauss Lemma

Estimating F_2 norm, Sketching, Johnson-Lindenstrauss Lemma

Notes from lecture 5 of Chandra Chekuri's course on algorithms for big data.

Using Mathpix and NaviLens to create accessible math flashcards

Using Mathpix and NaviLens to create accessible math flashcards

Students with print disabilities, due to blindness, low vision, learning disabilities or physical disabilities, can greatly benefit from accessible math flashcards and tutorials. MathPix greatly reduces the amount of work required to create these by making quick work of the most time-consuming part of this task by easily capturing text and math from a variety of sources.

Estimating F_2 norm, Sketching, Johnson-Lindenstrauss Lemma

Estimating F_2 norm, Sketching, Johnson-Lindenstrauss Lemma

Notes from lecture 4 of Chandra Chekuri's course on algorithms for big data.

Estimating F_k norms via AMS sampling

Estimating F_k norms via AMS sampling

Notes from lecture 3 of Chandra Chekuri's course on algorithms for big data.

Estimating the Number of Distinct Elements in a Stream

Estimating the Number of Distinct Elements in a Stream

Notes from lecture 2 of Chandra Chekuri's course on algorithms for big data.

Online Publishing Platform, 2010

Online Publishing Platform, 2010

A proposal submitted to (and rejected by) the National Science Foundation in 2010.

Heap sort

Heap sort

This is an article about heap sort, mainly analyzes the heap sort construction process and cost analysis, including the two construction methods of up and down and its algorithm analysis, compared to get a better time complexity construction algorithm, and further reduce the time complexity to make a theoretical analysis.

Basics of Probability, Probabilistic Counting (Morris's algorithm), and Reservoir Sampling

Basics of Probability, Probabilistic Counting (Morris's algorithm), and Reservoir Sampling

Notes from lecture 1 of Chandra Chekuri's course on algorithms for big data.

Introduction to Probability

Introduction to Probability

The true logic of this world is in the calculus of probabilities.

The Theory of Gravitation

The Theory of Gravitation

Every object in the universe attracts every other object with a force which for any two bodies is proportional to the mass of each and varies inversely as the square of the distance between them.

Introduction to Quantum Behavior

Introduction to Quantum Behavior

“Quantum mechanics” is the description of the behavior of matter in all its details and, in particular, of the happenings on an atomic scale. Things on a very small scale behave like nothing that you have any direct experience about.

Mathematical model of bearing lubrication under themal conditions

Mathematical model of bearing lubrication under themal conditions

The article covers mathematical modelling of bearing lubrication under thermal conditions for rolling bearing.

Regularization: Some Calculations from Bias Variance

Regularization: Some Calculations from Bias Variance

These notes contain a reprise of the eigenvalue arguments to understand how variance is reduced by regularization. It also describes different ways regularization can occur including from the algorithm or initialization.

Introduction to the Multivariate Gaussian Distribution

Introduction to the Multivariate Gaussian Distribution

Notes from Andrew Ng’s CS229 course in Machine Learning about multivariate gaussian distribution.

Introduction to Learning Theory

Introduction to Learning Theory

These notes from Andrew Ng’s CS299 course in Machine Learning cover learning theory.

Introduction to Deep Learning

Introduction to Deep Learning

This set of notes gives an overview of neural networks, discusses vectorization and discusses training neural networks with backpropagation. These notes are from Andrew Ng’s famous CS229 course at Stanford.

Introduction to Bias-Variance and Error Analysis

Introduction to Bias-Variance and Error Analysis

Notes on bias-variance and error analysis for students of Andrew Ng’s CS229 course in Machine Learning.

Introduction to Reinforcement Learning and Control

Introduction to Reinforcement Learning and Control

These notes from Andrew Ng’s course CS299 cover the topics of reinforcement learning and adaptive control.

The Expectation Maximization Algorithm

The Expectation Maximization Algorithm

This set of notes gives a broader view of the EM algorithm, and show how it can be applied to a large family of estimation problems with latent variables.

Mathematical Error In Court Rulings Due To Group Size

Mathematical Error In Court Rulings Due To Group Size

The error in a court ruling composed of a single judge is shown statistically to be 100 percent.

Linear Algebra Review and Reference for Machine Learning

Linear Algebra Review and Reference for Machine Learning

A review of the concepts of linear algebra for machine learning students of Andrew Ng’s CS229 course at Stanford.

Introduction to Principal Components Analysis

Introduction to Principal Components Analysis

These notes develop a method, Principal Components Analysis (PCA), that also tries to identify the subspace in which the data approximately lies.

Introduction to Supervised Learning

Introduction to Supervised Learning

The first section of the famous Machine Learning lecture notes from Andrew Ng’s CS229 course at Stanford. The notes cover supervised learning, linear regression, classification and logistic regression, and generalized linear models.

Introduction to Kernel Methods

Introduction to Kernel Methods

Notes from Andrew Ng’s CS229 course in Machine Learning about kernal methods and support vector machines.

Introduction to Generative Learning Algorithms

Introduction to Generative Learning Algorithms

Andrew Ng’s CS229 lecture notes on generative learning algorithms.

Introduction to Independent Components Analysis

Introduction to Independent Components Analysis

Similar to PCA, Independent Component Analysis will find a new basis in which to represent our data. However, the goal is very different.

Introduction to Regularization and Model Selection

Introduction to Regularization and Model Selection

Notes from Andrew Ng’s CS229 course in Machine Learning about regularization and model selection.

The k-means Clustering Algorithm

The k-means Clustering Algorithm

Notes from Andrew Ng’s CS229 course in Machine Learning about the k-means clustering algorithm.

Bitcoin: A Peer-to-Peer Electronic Cash System

Bitcoin: A Peer-to-Peer Electronic Cash System

The famous whitepaper that introduced the world to a decentralized financial system that eliminated the need for intermediaries.

Adversarial Learning on Graph

Adversarial Learning on Graph

This review gives an introduction to Adversarial Machine Learning on graph-structured data, including several recent papers and research ideas in this field. This review is based on our paper “A Survey of Adversarial Learning on Graph”.

Denoising ultra-high resolution cryo-electron tomograms

Denoising ultra-high resolution cryo-electron tomograms

Cryo-electron microscopy allows researchers to investigate the mechanisms of cells at detail in the Angstrom range, even enabling the visualization of single atoms in molecular machines frozen in their native states. However, as samples become more complex, so does data analysis, and the crowded molecular environment is extremely difficult to interpret. Denoising algorithms based on Noise2Noise show great promise in visualizing detail, and this brief review will aim to introduce the state of the art in denoising of 3-dimensional image volumes of cellular samples at ultra-high resolution.

Mixtures of Gaussians and the EM algorithm

Mixtures of Gaussians and the EM algorithm

These notes from Andrew Ng’s CS229 course in Machine Learning discuss the EM (Expectation-Maximization) algorithm for density estimation.

Demystifying Post-hoc Explainability for ML models

Demystifying Post-hoc Explainability for ML models

The widespread use of black-box models in AI has increased the need for explanation methods that reveal how these mysterious models arrive at concrete decisions. We will describe the problem, prominent solutions, and example applications for each of these approaches, as well as their vulnerabilities and flaws. We hope to have a enriching and an informative introduction to post-hoc machine learning explainability.

More on Multivariate Gaussians

More on Multivariate Gaussians

Notes from Andrew Ng’s CS229 course in Machine Learning about multivariate gaussian distribution continued.

Review of Probability Theory

Review of Probability Theory

Probability theory is the study of uncertainty. These notes attempt to cover the basics of probability theory at a level appropriate for Andrew Ng’s CS229 course in Machine Learning.

The Berry Phase Rectification Tensor and The Solar Rectification Vector

The Berry Phase Rectification Tensor and The Solar Rectification Vector

We introduce an operational definition of the Berry Phase Rectification Tensor as the second order change of polarization of a material in response to an ideal short pulse of electric field. Under time reversal symmetry this tensor depends exclusively on the Berry phases of the Bloch bands and not on their energy dispersions, making it an intrinsic property to each material which contains contributions from both the inter-band shift currents and the intra-band Berry Curvature Dipole. We also introduce the Solar Rectification Vector as a technologically relevant figure of merit for bulk photo-current generation which counts the number of electrons contributing to the rectified current per incoming photon under ideal black-body radiation in analogy with the classic solar cell model of Shockley and Queisser.

Towards continual task learning in artificial neural networks

Towards continual task learning in artificial neural networks

Critical appraisal of prominent current approaches to alleviating catastrophic forgetting in neural networks, drawing on inspiration from neuroscience.

Co-Tuning: An easy but effective trick to improve transfer learning

Co-Tuning: An easy but effective trick to improve transfer learning

Transfer learning is a popular method in the deep learning community, but it is usually implemented naively (eg. copying weights as initialization). Co-Tuning is a recently proposed technique to improve transfer learning that is easy to implement, and effective to a wide variety of tasks.

Knowledge evolution in neural networks

Knowledge evolution in neural networks

Deep learning relies on the availability of a large corpus of data (labeled or unlabeled). Thus, one challenging unsettled question is: how to train a deep network on a relatively small dataset? To tackle this question, Ahmed Taha, Abhinav Shrivastava, Larry Davis proposed an evolution-inspired training approach to boost performance on relatively small datasets. This article gives a detailed summary of their paper, “Knowledge evolution in neural networks”

Advances in machine learning using geometry provide new tools for computational neuroscientist

Advances in machine learning using geometry provide new tools for computational neuroscientist

A geometrical perspective proves efficient in developing machine learning tools for computational neuroscience.

Recognition of Hand Written Mathematical Expression using Scale Augmentation and Drop Attention

Recognition of Hand Written Mathematical Expression using Scale Augmentation and Drop Attention

We start by explaining about how handwritten mathematical expressions have unstable scale. Then we show how augmented layers are used for scaling those mathematical expression. We continue by explaining how an attention based encoder-decoder is used for extracting features and generating predictions. The drop attention is used when the attention distribution of the decoder is not precise. This method achieves better performance than any other existing method.

Adversarial attacks in deep learning: what can they teach us?

Adversarial attacks in deep learning: what can they teach us?

Review of the major studies discussing adversarial attacks and defences

Machine-learning Online Optimisation for Evaporative Cooling in Cold-atom Experiments

Machine-learning Online Optimisation for Evaporative Cooling in Cold-atom Experiments

As quantum systems become increasingly complex, optimisation algorithms are becoming a requirement for high-precision experiments. Machine-learning online optimisation offers an alternative to theoretical models, relying instead on experimental observations to continuously update an internal surrogate model. Two online optimisation techniques are reviewed in this paper in the context of evaporative cooling for the efficient and high-quality production of Bose-Einstein condensates (BEC). These two methods prioritise different stages of cooling with one focused on optimising experimental settings and the other on improving image acquisition.

Causal Machine Learning in Healthcare

Causal Machine Learning in Healthcare

This review gives an introduction to Causal Machine Learning with a focus on healthcare and the issues that are faced there. Several recent papers and research ideas in this area are presented.

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