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.

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Papers - Page 16

June 17, 2024

Perceptron Collaborative Filtering
Arya Chakraborty
Machine Learning Model Recommender System Elementary Perceptron Neural Collaborative Filtering

June 15, 2024

June 14, 2024

June 12, 2024

Using Quality Attribute Scenarios for ML Model Test Case Generation
Rachel Brower-Sinning, Grace A. Lewis, Sebastían Echeverría, Ipek Ozkaya
Machine Learning Machine Learning Model Quality Attribute Testing Model

June 11, 2024

Quantifying Local Model Validity using Active Learning
Sven Lämmle, Can Bogoclu, Robert Voßhall, Anselm Haselhoff, Dirk Roos
Local Inference Model Benchmark Machine Learning Model Concurrent Validity Active Learning

June 10, 2024

Multi-Objective Neural Architecture Search for In-Memory Computing
Md Hasibul Amin, Mohammadreza Mohammadi, Ramtin Zand
Memory Computing Convolutional Neural Network Convolutional Layer Neural Architecture Search Multi Objective Neural Architecture Search Machine Learning Model

June 9, 2024

Event prediction and causality inference despite incomplete information
Harrison Lam, Yuanjie Chen, Noboru Kanazawa, Mohammad Chowdhury, Anna Battista, Stephan Waldert
Machine Learning Technical Challenge Incomplete Information Complexity Change Machine Learning Model Causal Inference Trigger Point Event Prediction

June 6, 2024

June 5, 2024

June 4, 2024

Towards Neural Architecture Search for Transfer Learning in 6G Networks
Adam Orucu, Farnaz Moradi, Masoumeh Ebrahimi, Andreas Johnsson
Neural Architecture Search G Network Future Network Machine Learning Model Transfer Learning

June 3, 2024

Machine Learning Model

Papers - Page 16

Perceptron Collaborative Filtering

RMF: A Risk Measurement Framework for Machine Learning Models

Active, anytime-valid risk controlling prediction sets

Selecting Interpretability Techniques for Healthcare Machine Learning models

Challenges in explaining deep learning models for data with biological variation

Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale

Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges

An AI Architecture with the Capability to Explain Recognition Results

Using Quality Attribute Scenarios for ML Model Test Case Generation

Quantifying Local Model Validity using Active Learning

Multi-Objective Neural Architecture Search for In-Memory Computing

Event prediction and causality inference despite incomplete information

Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks

Reassessing How to Compare and Improve the Calibration of Machine Learning Models

Ethical considerations of use of hold-out sets in clinical prediction model management

MESS: Modern Electronic Structure Simulations

Towards Neural Architecture Search for Transfer Learning in 6G Networks

AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML

Model for Peanuts: Hijacking ML Models without Training Access is Possible

Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects