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Medium
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30
Research on Bidiagonalization part1(Machine Learning future)
- Bidiagonal matrices are widely used in numerical linear algebra and have interesting properties.
- The inverse of a product of bidiagonal matrices is insensitive to small perturbations in the factors.
- Componentwise rounding error bounds for solving linear systems with bidiagonal matrices are derived.
- Factorizations involving bidiagonal matrices can be used to prove properties of special matrices.
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Medium
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245
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Revisting Frank-Wolfe method part1(Machine Learning future)
- An extension of the Frank-Wolfe Algorithm (FWA) called Dualized Level-Set (DLS) algorithm is proposed, which allows to address nonsmooth costs.
- A forward gradient-based Frank-Wolfe optimization algorithm is proposed for memory-efficient deep neural network training.
- The sliding Frank-Wolfe algorithm is used to recover lines in degraded images.
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Medium
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234
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Filecoin and AI: Bridging the Gap Between Data Storage and Machine Learning
- Filecoin provides a decentralized storage solution for AI companies to store large amounts of data required for training models and developing LLMs.
- Smaller AI companies benefit from Filecoin's cost-effective decentralized storage solution.
- FILLiquid is helping ensure sustainable expansion of Filecoin's storage capacity.
- Filecoin bridges the gap between data storage and machine learning by offering secure, scalable, and accessible data storage for AI projects.
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Medium
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363
Dynamics of Regularized regression part4(Machine Learning 2024)
- Calibration of machine learning classifiers is necessary to obtain reliable and interpretable predictions.
- Isotonic regression (IR) is a technique used for calibrating binary classifiers by minimizing cross-entropy on a calibration set.
- IR preserves the convex hull of the ROC curve, ensuring calibration without overfitting the calibration set.
- A novel generalization of isotonic regression is presented to accommodate classifiers with K classes, achieving multi-class calibration error of zero.
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Medium
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113
Dynamics of Regularized regression part3(Machine Learning 2024)
- We investigate popular resampling methods for estimating the uncertainty of statistical models in high-dimensional supervised regression tasks.
- Resampling methods exhibit the double-descent-like behavior typical of high-dimensional situations.
- They provide consistent and reliable error estimations only when the number of samples and dimension of covariates grow at a comparable rate.
- In the over-parametrized regime relevant to modern machine learning practice, their predictions are not consistent, even with optimal regularization.
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Medium
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325
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Dynamics of Regularized regression part2(Machine Learning 2024)
- Linear Discriminant Analysis (LDA) is an important classification approach with a simple linear form.
- This paper strengthens the connection between LDA and multivariate response regression.
- A new regression-based multi-class classification procedure is proposed, allowing for structured, regularized, and non-parametric regression methods.
- Theoretical guarantees for using ℓ1-regularization in LDA are provided and supported by simulation studies and real data analysis.
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Medium
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246
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Dynamics of Regularized regression part1(Machine Learning 2024)
- Many problems in statistics and machine learning can be formulated as model selection problems, where the goal is to choose an optimal parsimonious model among a set of candidate models.
- A generalized IC framework is proposed to consistently estimate general loss-based learning problems, specifically for Generalized Linear Model (GLM) regressions.
- The proposal introduces a computational procedure for implementing the methods in the finite sample setting and includes an extensive simulation study.
- A safe subspace screening rule is developed for the adaptive nuclear norm regularized trace regression model, which improves computational efficiency and reduces solution dimension.
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Medium
1d
45
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Understanding Local Outlier Factor (LOF) for Anomaly Detection: A Comprehensive Guide with Python…
- LOF operates on the principle of local density estimation.
- LOF focuses on the local neighbourhood of each data point.
- Implementing LOF involves several steps using scikit-learn.
- LOF offers a powerful approach to anomaly detection.
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Medium
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45
Revisiting Multiclass Classification part2(Machine Learning 2024)
- Designing proper treatment plans for managing diabetes in older adults with Type 2 Diabetes Mellitus (T2DM) is crucial, considering their remaining life and comorbidities.
- A structured dataset with 68 potential mortality predictors for 275,190 diabetic U.S. military veterans aged 65 years or older is used in this study.
- Multiple classifiers like Multinomial Logistic Regression, Random Forest, XGBoost, and One-vs-Rest are employed, but all the models consistently underperform.
- The high dimensionality of the input data after dummy encoding and the association of input variables with multiple target classes contribute to misclassifications.
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Medium
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91
Revisiting Multiclass Classification part1(Machine Learning 2024)
- We revisit the classical problem of multiclass classification with bandit feedback (Kakade, Shalev-Shwartz and Tewari, 2008).
- Our primary inquiry is with regard to the dependency on the number of labels K.
- Our main contribution is showing that the minimax regret of bandit multiclass is more nuanced.
- We present a new bandit classification algorithm that guarantees regret O˜(|H|+T−−√).
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Medium
1d
68
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Enhanced Accessibility for Students with Disabilities
- The limited accessibility of educational resources and materials for students with disabilities hinders the learning process.
- A chatbot named HumanAIze has been developed that can convert text into spoken language, provide descriptions for images and facilitate communication in order to make education more inclusive for these students.
- HumanAIze has the following components: secure authentication system, central dashboard, AI-driven predictions for communication, result management for users and a support and feedback system.
- AI plays a vital role in the development and functionality of HumanAIze, enabling it to provide inclusive and effective support for students with disabilities.
- Core functionalities enabled by AI inculde Text-To-Speech Transformation, Image Description, and Communication Facilitation.
- HumanAIze differentiates itself by incorporating an interactive cartoon character that speaks in its own voice to make learning more enjoyable and accessible.
- The benefits of HumanAIze include Enhanced Accessibility, Inclusive Learning Environment, Personalized Support, Empowerment and Independence, and Social Inclusion.
- The technology used for the development of HumanAIze includes Frontend Technologies, Backend Technologies, Database, AI and Machine Learning, APIs and Cloud Services.
- Through HumanAIze, students with disabilities have access to the tools they need to succeed in their educational journey.
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Medium
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285
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Seachain — Financial Market Insider ML Project
- The Seachain project enhances systems' ability to verify and filter news authenticity and provide personalized insights tailored to users' specific financial interests and needs.
- This machine learning solution covers major areas of financial news such as market trends, earnings reports, and company analyses.
- Data from various sources such as financial reports, news articles, and user-generated content on Twitter is used to train and test the tools implemented.
- The Sentiment Analysis sub-team utilizes pre-trained models like BERT and LangChain to analyze the financial news data.
- News Recommendation system uses Microsoft News Dataset (MIND) to train and evaluate collaborative and content-based filtering recommendation algorithms.
- The Market Prediction sub-team implements Long Short-Term Memory (LSTM) deep learning models to forecast industry trend direction based on data from macroeconomic indicators and stock prices.
- A chatbot is developed as part of the project to help users get relevant information about any company, stock performance, and other financial ratios.
- The project highlights the importance of collaboration and innovation in the FinTech space.
- The project can be further enhanced by expanding the knowledge base, decreasing instances of 'I don't know' and increasing accuracy and efficiency.
- The Seachain project sets a promising foundation for future innovations in the financial information space.
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Medium
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217
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How I Used AI to Help My Grandfather Manage Diabetes
- Diabetes is a chronic condition affecting millions worldwide, including my grandfather. Managing diabetes involves constant monitoring of blood sugar levels, medication, diet, and exercise.
- I used artificial intelligence (AI) techniques to predict my grandfather's blood sugar levels and provide personalized recommendations for diet and exercise.
- The project involved data collection, preprocessing, model training, evaluation, deployment, and integration into an Android app.
- The AI-based system helped in managing diabetes effectively, predicting blood sugar levels, and providing personalized recommendations for diet and exercise.
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Medium
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323
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Mastering Cosine Metrics: A Data Scientist’s Essential Toolkit
- Cosine similarity and cosine distance are popular metrics used in data science to measure the similarity or distance between vectors.
- Cosine similarity calculates the cosine of the angle between two vectors, while cosine distance represents the dissimilarity between vectors.
- These metrics are commonly used in information retrieval, text mining, and recommender systems.
- NumPy and Scikit-Learn provide convenient tools for calculating cosine similarity and cosine distance in Python.
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Medium
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305
Latest Research on Ising Models part10(Machine Learning 2024)
- A variational autoregressive architecture with a message passing mechanism is proposed to solve Ising models.
- The network can effectively utilize the interactions between spin variables.
- The new network outperforms existing methods in solving prototypical Ising spin Hamiltonians.
- The method extends the current computational limits of unsupervised neural networks.
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