Machine Learning Model

Machine learning models aim to create systems that can learn from data and make predictions or decisions without explicit programming. Current research emphasizes improving model accuracy, interpretability, and robustness, focusing on architectures like deep neural networks, decision tree ensembles, and transformer models, as well as exploring decentralized learning and techniques for mitigating biases and vulnerabilities. These advancements are crucial for diverse applications, ranging from optimizing resource management (e.g., smart irrigation) to improving healthcare diagnostics and enhancing the security and trustworthiness of AI systems.

Papers

September 25, 2024

The poison of dimensionality
Lê-Nguyên Hoang
Machine Learning Model Synthetic Data Poisoning Attack Data Dimensionality Random Feature

September 24, 2024

Learning To Help: Training Models to Assist Legacy Devices
Yu Wu, Anand Sarwate
LeArning Abstract Machine Learning Model Training Model Based Rejection Legacy Networking Technology

September 23, 2024

On The Specialization of Neural Modules
Devon Jarvis, Richard Klein, Benjamin Rosman, Andrew M. Saxe
Machine Learning Model Learning Dynamic Systematic Generalization Neural Module Generalization Benchmark

September 22, 2024

Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
Sven Kruschel, Nico Hambauer, Sven Weinzierl, Sandra Zilker, Mathias Kraus, Patrick Zschech
Machine Learning Model Global Evaluation Interpretable Model Black Box Model Interpretable Machine Learning Model Interpretability Generalized Additive Model

September 21, 2024

Predicting Coronary Heart Disease Using a Suite of Machine Learning Models
Jamal Al-Karaki, Philip Ilono, Sanchit Baweja, Jalal Naghiyev, Raja Singh Yadav, Muhammad Al-Zafar Khan
LeArning Abstract Machine Learning Model Human Prediction Random Forest Coronary Artery Disease Software Suite

September 20, 2024

Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework
Claudio Bonesana, Daniele Malpetti, Sandra Mitrović, Francesca Mangili, Laura Azzimonti
Machine Learning Model Federated Learning Sensitive Data Multi Party Federated Learning Framework

September 19, 2024

September 18, 2024

September 17, 2024

September 15, 2024

Extrapolative ML Models for Copolymers
Israrul H. Hashmi, Himanshu, Rahul Karmakar, Tarak K Patra
Machine Learning Model Trend Extrapolation

September 11, 2024

A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption
Marcus Rüb, Philipp Tuchel, Axel Sikora, Daniel Mueller-Gritschneder
Machine Learning Model Incremental Learning Dataset Distillation TinyML Model Model Size Tiny Machine Learning Federat\textbf{T}ed Cl\textbf{A}ss Continual Lea\textbf{R}nin\textbf{G

September 8, 2024

From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems
Constance Douwes, Romain Serizel
Neural Network Training Data Machine Learning Model Neural Network Architecture Computation Method Memory Management GIT Net

September 7, 2024

HULLMI: Human vs LLM identification with explainability
Prathamesh Dinesh Joshi, Sahil Pocker, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat
Machine Learning Model High Explainability Human Generated Interpretability Analysis AI Text Detection Visual Hull

September 6, 2024

September 5, 2024

Addressing the Gaps in Early Dementia Detection: A Path Towards Enhanced Diagnostic Models through Machine Learning
Juan A. Berrios Moya
Machine Learning Machine Learning Model Early Detection Filling Gap Root to Leaf Path Dementia Detection Diagnostic Model

September 4, 2024

AdvSecureNet: A Python Toolkit for Adversarial Machine Learning
Melih Catal, Manuel Günther
Adversarial Attack Machine Learning Model Adversarial Learning Pytorch Model Security Vulnerability Python Toolbox

Machine Learning Model

Papers

The poison of dimensionality

Learning To Help: Training Models to Assist Legacy Devices

On The Specialization of Neural Modules

Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models

Predicting Coronary Heart Disease Using a Suite of Machine Learning Models

Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework

Impact of ML Optimization Tactics on Greener Pre-Trained ML Models

Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability

Detecting LGBTQ+ Instances of Cyberbullying

Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility

Clinical Validation of a Real-Time Machine Learning-based System for the Detection of Acute Myeloid Leukemia by Flow Cytometry

Machine Learning and Theory Ladenness -- A Phenomenological Account

Extrapolative ML Models for Copolymers

A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption

From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems

HULLMI: Human vs LLM identification with explainability

CubicML: Automated ML for Large ML Systems Co-design with ML Prediction of Performance

Leveraging Machine Learning for Official Statistics: A Statistical Manifesto

Addressing the Gaps in Early Dementia Detection: A Path Towards Enhanced Diagnostic Models through Machine Learning

AdvSecureNet: A Python Toolkit for Adversarial Machine Learning