Machine Learning

Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.

Papers

October 30, 2023

Strategies to Harness the Transformers' Potential: UNSL at eRisk 2023
Horacio Thompson, Leticia Cagnina, Marcelo Errecalde
Machine Learning Transformer Megatron Decepticons Information Retrieval Automatic Detection General Strategy Wire Harness Risk Detection
Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records
Bing Wang, Weizi Li, Anthony Bradlow, Antoni T. Y. Chan, Eghosa Bazuaye
Machine Learning Early Detection Unstructured Clinical Multimodal Machine Rheumatoid Arthritis Laboratory Value
Solving a Class of Cut-Generating Linear Programs via Machine Learning
Atefeh Rajabalizadeh, Danial Davarnia
Machine Learning Mixed Integer True Class Branch and Bound Graph Cut Branch and Cut
Protecting Publicly Available Data With Machine Learning Shortcuts
Nicolas M. Müller, Maximilian Burgert, Pascal Debus, Jennifer Williams, Philip Sperl, Konstantin Böttinger
Machine Learning Spurious Correlation Available Datasets Shortcut Learning Unreliable Source
Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: Task Formulations and Machine Learning Methods
Md Rakibul Hasan, Md Zakir Hossain, Shreya Ghosh, Aneesh Krishna, Tom Gedeon
Machine Learning Audio Visual Physiological Signal Empathy Detection Task Formulation Computational Empathy

October 29, 2023

Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI
Adam White, Margarita Saranti, Artur d'Avila Garcez, Thomas M. H. Hope, Cathy J. Price, Howard Bowman
Deep Learning Machine Learning Explainable AI Multimodal Data Stroke Lesion Segmentation Post Stroke
Good Tools are Half the Work: Tool Usage in Deep Learning Projects
Evangelia Panourgia, Theodoros Plessas, Ilias Balampanis, Diomidis Spinellis
Deep Learning Machine Learning MLOps System Digital Tool Tool Usage
Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector
Unai Fischer-Abaigar, Christoph Kern, Noam Barda, Frauke Kreuter
Machine Learning Machine Learning Model Easy to Use Toolkit Causal Machine Learning Public Sector
Machine Learning for the identification of phase-transitions in interacting agent-based systems
Nikolaos Evangelou, Dimitrios G. Giovanis, George A. Kevrekidis, Grigorios A. Pavliotis, Ioannis G. Kevrekidis
Machine Learning Person Identification Reduced Order Agent System Agent Based Model Phase Transition Closed Form
Comparison of Microservice Call Rate Predictions for Replication in the Cloud
Narges Mehran, Arman Haghighi, Pedram Aminharati, Nikolay Nikolov, Ahmet Soylu, Dumitru Roman, Radu Prodan
Machine Learning Consistent Comparison Cloud Computing Serial Reproduction Microservice Architecture
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
Li Ding, Masrour Zoghi, Guy Tennenholtz, Maryam Karimzadehgan
Machine Learning Knowledge Distillation Neural Architecture Search Extension Study Meta Optimization Biased Gradient Adaptive EV Evaluator Adjuster Unit

October 28, 2023

A Review on the Applications of Machine Learning for Tinnitus Diagnosis Using EEG Signals
Farzaneh Ramezani, Hamidreza Bolhasani
Machine Learning Financial Application Electroencephalography Signal Tinnitus Diagnosis
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar
Machine Learning Efficient Pipeline Medical Time Series
Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research
Nhat-Quang Tran, Anna Felipe, Thanh Nguyen Ngoc, Tom Huynh, Quang Tran, Arthur Tang, Thuy Nguyen
Machine Learning Narrative Review Current Research

October 27, 2023

Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments
Manish Osti, Aashray Thakuri, Basheer Qolomany, Aos Mulahuwaish
Machine Learning Native Robustness Split Learning Centralized Learning Weighted Sampling
A Data-Centric Online Market for Machine Learning: From Discovery to Pricing
Minbiao Han, Jonathan Light, Steven Xia, Sainyam Galhotra, Raul Castro Fernandez, Haifeng Xu
Machine Learning Machine Learning Model Scientific Discovery Curious Price Data Market Data Driven Machine Learning
Hybrid Optical Turbulence Models Using Machine Learning and Local Measurements
Christopher Jellen, Charles Nelson, John Burkhardt, Cody Brownell
Machine Learning Optical Turbulence

October 26, 2023

Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning
Anas Al-lahham, Obaidah Theeb, Khaled Elalem, Tariq A. Alshawi, Saleh A. Alshebeili
Machine Learning Forecast Utterance Univariate Time Series Forecasting Solar Irradiance Night Sky Global Horizontal Irradiance
Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning
Conor Nixon, Zachary Yahn, Ethan Duncan, Ian Neidel, Alyssa Mills, Benoît Seignovert, Andrew Larsen, Kathryn Gansler, Charles Liles, Catherine Walker, Douglas Trent, John Santerre
Machine Learning Convolutional Neural Network Classification Code Feature Extraction Image Datasets R CNN Feature Detection
Variance of ML-based software fault predictors: are we really improving fault prediction?
Xhulja Shahini, Domenic Bubel, Andreas Metzger
Machine Learning Variance Information Fault Prediction

Machine Learning

Papers

Strategies to Harness the Transformers' Potential: UNSL at eRisk 2023

Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records

Solving a Class of Cut-Generating Linear Programs via Machine Learning

Protecting Publicly Available Data With Machine Learning Shortcuts

Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: Task Formulations and Machine Learning Methods

Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI

Good Tools are Half the Work: Tool Usage in Deep Learning Projects

Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector

Machine Learning for the identification of phase-transitions in interacting agent-based systems

Comparison of Microservice Call Rate Predictions for Replication in the Cloud

Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation

A Review on the Applications of Machine Learning for Tinnitus Diagnosis Using EEG Signals

Clairvoyance: A Pipeline Toolkit for Medical Time Series

Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research

Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments

A Data-Centric Online Market for Machine Learning: From Discovery to Pricing

Hybrid Optical Turbulence Models Using Machine Learning and Local Measurements

Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning

Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning

Variance of ML-based software fault predictors: are we really improving fault prediction?