A simplified tutorial on diffusion models for beginners.
problem Educating technical audiences on diffusion models.
method Simplified mathematical explanations and heuristic derivations.
result Accessible algorithms for diffusion models.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,695 papers · 148 categories
Trend · papers per month
29 results for “beginners”
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financeq-fin.TR
FinRL simplifies deep RL for stock trading, making it accessible to beginners.
problem Lack of accessible tools for beginners in deep RL for stock trading.
method Developed a DRL library with reproducible tutorials and backtesting.
result FinRL streamlines development and comparison of trading strategies.
The present paper are the notes of a mini-course addressed mainly to non-experts. It purpose it to provide a first approach to the theory of mapping class groups of non-orientable surfaces.
This report provides an introduction to some Machine Learning tools within the most common development environments. It mainly focuses on practical problems, skipping any theoretical introduction. It is oriented to both students trying to approach Machine Learning and experts looking for new frameworks.
An introduction to KnotPlotmath.GT
KnotPlot helps beginners and veterans use software for visualizing knots.
problem None explicitly stated in the abstract.
method Software introduction and background for users.
result Provides new and veteran users with KnotPlot functionality.
Using Jeff Holman's comments in Quantitative Finance to illustrate 4 critical errors students should learn to avoid: 1) Mistaking tails (4th moment) for volatility (2nd moment), 2) Missing Jensen's Inequality, 3) Analyzing the hedging wihout the underlying, 4) The necessity of a numeraire in finance.
Explains biharmonic and biconservative submanifolds for beginners.
problem Understanding biharmonic and biconservative submanifolds.
method Explains motivations and key facts, highlights open problems.
result Provides an accessible introduction to these submanifolds.
Lecture notes on mean curvature flow for beginners.
problem Existence and uniqueness of mean curvature flow solutions.
method Conditional existence and weak-strong uniqueness theory.
result Accessible introduction for graduate students.
This is an introductory article on high dimensional knots for the beginners. High dimensional knot theory is an exciting field. It is a field of knot theory, which is one of topology and is connected with many ones. In this article we use few literal expressions, equations, functions, etc. We barely suppose that the re…
This article demonstrates that convolutional operation can be converted to matrix multiplication, which has the same calculation way with fully connected layer. The article is helpful for the beginners of the neural network to understand how fully connected layer and the convolutional layer work in the backend. To be c…
We provide a comprehensive review of classical algorithms for compressive sensing of images, focused on Total variation methods, with a view to application in LiDAR systems. Our primary focus is providing a full review for beginners in the field, as well as simulating the kind of noise found in real LiDAR systems. To t…
Lecture notes on Heegaard Floer homology for beginners.
problem Introducing Heegaard Floer homology to newcomers.
method Illustrates algebraic structures via grid homology, then defines Heegaard Floer homology geometrically.
result Defines and describes key properties of Heegaard Floer homology.
NAS helps find best neural network designs.
problem Designing optimal neural network architectures.
method Optimization algorithms and search spaces.
result Introduction to major advances in NAS for CNNs.
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, porta…
One of the problems faced by a firm that sells certain commodities is to determine the number of products that it must supply in order to maximize its profit. In this article, the authors give an answer to this problem of economic interest. The proposed problem is a generalization of the results obtained by Stirzaker (…
Introduction to geometrymath.DG
These notes give an informal and leisurely introduction to geometry for beginners. A special emphasis is placed on understanding the special linear algebraic structure in dimensions that is the pointwise model for geometry, using the octonions. The basics of -structures a…
This tutorial simplifies Gaussian process regression for beginners.
problem Understanding Gaussian process regression for machine learning.
method Explains basic concepts, provides a concise GPR description, and reviews implementation packages.
result Clear understanding of Gaussian process regression fundamentals.
Tutorial on using neural networks for single cell data analysis.
problem Handling large sequencing datasets efficiently.
method Single cell variational inference using variational auto-encoder.
result Model learns data distribution for insights.
Spektral simplifies graph neural networks with TensorFlow and Keras.
problem Building graph neural networks efficiently.
method Open-source library implementing various graph neural network methods.
result Performance of implemented methods in node, graph, and graph regression tasks.
Research adapts Multi-Armed Bandits for Minesweeper, improving player strategy.
problem Improving computational players' strategy in Minesweeper.
method Adapted Multi-Armed Bandit algorithms for Minesweeper.
result The approach was successful, especially on smaller game boards.
Hakenness and b_1math.DG
Three great theorems of Thurston read: Haken manifolds are hyperbolic; big ramified coverings are hyperbolic; big surgeries are hyperbolic. Recent developments indicate that the later two theorems are essentially a corollary of the first, that is there are much more Haken manifolds than expected by Thurston. In fact Fr…
This paper applies knot theory to modern yo-yo play.
problem No systematic knot-theoretic treatment of contemporary yo-yo play.
method Recalled fundamental knot theory results and developed a methodology for classifying string arrangements (mounts).
result Classified a range of mounts and identified corresponding yo-yo maneuvers.
Programming has been an important skill for researchers and practitioners in computer science and other related areas. To learn basic programing skills, a long-time systematic training is usually required for beginners. According to a recent market report, the computer software market is expected to continue expanding …
RE enhances DL by learning model behavior, enabling iterative self-improvement.
problem Static data representations limit DL's potential for evolving models.
method RE uses multiple mappings of data through identical deep architectures, analyzing internal representations and performance signals.
result Models can gain insight from predecessors, leading to iterative self-improvement.
A coreset (or core-set) of an input set is its small summation, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models, classifiers, loss functions). Over the past decade, coreset constructio…
tf_geometric simplifies graph deep learning in TensorFlow.
problem Efficient graph deep learning in TensorFlow.
method Kernel libraries and infrastructures for GNNs.
result tf_geometric supports various graph tasks and provides efficient GNN models.
This guide simplifies explainable deep learning for beginners.
problem Difficulty in understanding deep learning model decisions.
method Introduces three dimensions for explainable deep learning methods.
result Clarifies evaluations for model explanations.
Olympic Games consistently exceed budgets, leading to unpredictable costs.
problem High costs and unpredictability of the Olympic Games.
method Statistical analysis of historical data to explain cost risks.
result Olympic costs follow a power-law distribution with infinite mean and variance.
A theory of deep learning is emerging, focusing on training dynamics and statistics.
problem Develop a scientific theory to understand deep learning.
method Synthesize research into five areas: idealized settings, tractable limits, mathematical laws, hyperparameters, and universal behaviors.
result The emerging theory is a mechanics of the learning process, named learning mechanics.