Applicability Study

Applicability studies assess the effectiveness and limitations of existing models and algorithms in diverse contexts, aiming to determine their suitability for specific tasks and datasets. Current research focuses on evaluating various model architectures, including large language models, generative models, and neural networks, across domains such as legal text summarization, student performance prediction, and image quality assessment. These studies are crucial for bridging the gap between theoretical advancements and practical applications, informing the responsible development and deployment of machine learning and AI systems. Furthermore, they highlight the need for robust evaluation metrics and techniques to address challenges like data heterogeneity, model bias, and the need for explainability.

Papers

April 5, 2023

A step towards the applicability of algorithms based on invariant causal learning on observational data
Borja Guerrero Santillan
Practical Algorithm Causal Inference Causal Discovery Observational Data Causal Discovery Method Sample STEP Applicability Study Optimal Prediction Invariant Causal Feature Invariant Causal

March 14, 2023

Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review
João Vitorino, Tiago Dias, Tiago Fonseca, Eva Maia, Isabel Praça
Adversarial Attack Adversarial Learning Systematic Review Adversarial Method Applicability Study Automated Software Testing Adversarial Constraint

January 17, 2023

Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models
Gihan Panapitiya, Emily Saldanha
Machine Learning Model Feature Space Internet Service Domain Prediction Accuracy Model Prediction Material Property Prediction Applicability Study

January 10, 2023

Enhancing Evaluation Methods for Infrared Small-Target Detection in Real-world Scenarios
Saed Moradi, Alireza Memarmoghadam, Payman Moallem, Mohamad Farzan Sabahi
Computer Vision Richer RGB Infrared Feature Infrared Small Target Detection Performance Metric Target Detection Small Target Detection Applicability Study

December 20, 2022

On the Applicability of Synthetic Data for Re-Identification
Jérôme Rutinowski, Bhargav Vankayalapati, Nils Schwenzfeier, Maribel Acosta, Christopher Reining
Generative Adversarial Network Synthetic Data Whistleblower Re Identification State of the Art GAN Applicability Study Pallet Block Image

November 21, 2022

The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues
J. Waghorne, C. Howard, H. Hu, J. Pang, W. J. Peveler, L. Harris, O. Barrera
Deep Learning Generative Adversarial Network Study Feature Applicability Study Tissue Context Pore Space Soft Tissue MIMICRY Change Optimisation Dynamic Tibial Cartilage

October 6, 2022

From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
Shalaleh Rismani, Renee Shelby, Andrew Smart, Edgar Jatho, Joshua Kroll, AJung Moon, Negar Rostamzadeh
Applicability Study Functional Safety Ethical Implication Responsible Machine Learning Ethical Risk Algorithmic Harm

July 8, 2022

On the Need and Applicability of Causality for Fair Machine Learning
Rūta Binkytė, Ljupcho Grozdanovski, Sami Zhioua
Procedural Fairness Causal Pattern Community Need Fair Machine Learning Fairness Evaluation Applicability Study Causal Prediction Causal View

June 23, 2022

On the Importance and Applicability of Pre-Training for Federated Learning
Hong-You Chen, Cheng-Hao Tu, Ziwei Li, Han-Wei Shen, Wei-Lun Chao
Deep Learning Pre Trained Model Pre Training Importance Aware Centralized Learning Recognition Benchmark Learned Model Applicability Study

May 30, 2022

Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks
Luke Taylor, Andrew King, Nicol Harper
Neural Network Robust Version Neuromorphic Datasets Applicability Study Temporal Task Single Spike

January 16, 2022

A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning
Aurora Ramírez, Robert Feldt, José Raúl Romero
Machine Learning Comprehensive Taxonomy Applicability Study Test Case Test Suite Attribute Information

December 14, 2021

Applicability of Large Corporate Credit Models to Small Business Risk Assessment
Khalid El-Awady
Applicability Study Credit Risk Credit Scoring Model Credit Risk Model Credit Rating Enterprise Risk

Applicability Study

Papers

A step towards the applicability of algorithms based on invariant causal learning on observational data

Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review

Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models

Enhancing Evaluation Methods for Infrared Small-Target Detection in Real-world Scenarios

On the Applicability of Synthetic Data for Re-Identification

The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues

From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML

On the Need and Applicability of Causality for Fair Machine Learning

On the Importance and Applicability of Pre-Training for Federated Learning

Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks

A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning

Applicability of Large Corporate Credit Models to Small Business Risk Assessment