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

March 3, 2022

Physics-Data Driven Machine Learning Based Model: A Hybrid Way for Nonlinear, Dynamic, and Open-loop Identification of IPMC Soft Artificial Muscles
Mohsen Annabestani, Mohammad Hossein Sayyad, Zahra Meskar, Mehdi Fardmanesh, Barbara Mazzolai
Machine Learning Model Nonlinear Model Hybrid Model Pneumatic Artificial Muscle Open Loop Physic Data Non Autoregressive Model

March 2, 2022

Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
Chenru Duan, Aditya Nandy, Husain Adamji, Yuriy Roman-Leshkov, Heather J. Kulik
Machine Learning Model Task Transferability Multiple Outcome Electronic Structure Catalyst Design Reactive Intermediate

February 28, 2022

February 25, 2022

MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs
Wenrui Zhang, Shijia Geng, Zhaoji Fu, Linlin Zheng, Chenyang Jiang, Shenda Hong
Deep Neural Network Machine Learning Model Fine Tuning Curriculum Learning Electrocardiogram Recording Arrhythmia Detection ECG Datasets Ventricular Tachycardia

February 23, 2022

Margin-distancing for safe model explanation
Tom Yan, Chicheng Zhang
Machine Learning Model Transparency Index Decision Boundary Margin Maximization Model Safety

February 21, 2022

February 18, 2022

February 17, 2022

February 16, 2022

Bias and unfairness in machine learning models: a systematic literature review
Tiago Palma Pagano, Rafael Bessa Loureiro, Fernanda Vitória Nascimento Lisboa, Gustavo Oliveira Ramos Cruz, Rodrigo Matos Peixoto, Guilherme Aragão de Sousa Guimarães, Lucas Lisboa dos Santos, Maira Matos Araujo, Marco Cruz, Ewerton Lopes Silva de Oliveira, Ingrid Winkler, Erick Giovani Sperandio Nascimento
Machine Learning Model Absolute Stance Bias Systematic Literature Review Fairness Metric Algorithmic Unfairness

February 15, 2022

February 11, 2022

Compute Trends Across Three Eras of Machine Learning
Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos
Machine Learning Machine Learning Model New Era Deep Learning Era Trend Analysis Training Compute

Machine Learning Model

Papers

Physics-Data Driven Machine Learning Based Model: A Hybrid Way for Nonlinear, Dynamic, and Open-loop Identification of IPMC Soft Artificial Muscles

Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis

Explainability for identification of vulnerable groups in machine learning models

AI Gone Astray: Technical Supplement

Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles

ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction

Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten

MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs

Margin-distancing for safe model explanation

Non-Volatile Memory Accelerated Posterior Estimation

Poisoning Attacks and Defenses on Artificial Intelligence: A Survey

An Integrated Optimization and Machine Learning Models to Predict the Admission Status of Emergency Patients

Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness

Handling Imbalanced Datasets Through Optimum-Path Forest

Machine learning models and facial regions videos for estimating heart rate: a review on Patents, Datasets and Literature

An overview of deep learning in medical imaging

Bias and unfairness in machine learning models: a systematic literature review

Interpreting a Machine Learning Model for Detecting Gravitational Waves

Explaining Reject Options of Learning Vector Quantization Classifiers

Compute Trends Across Three Eras of Machine Learning