Results 121 to 130 of about 339,627 (329)

LLM‐based keyword augmentation for title‐driven evidence selection: A practical approach

Journal of Forensic Sciences, EarlyView.
Abstract Keyword‐based search is widely used in digital forensic investigations, yet its effectiveness depends strongly on investigator experience, leading to inconsistent results and missed evidence. While previous studies have explored machine learning and large language models (LLMs) to address this, practical deployment is often constrained by ...
Sanghyun Yoo, Doowon Jeong
wiley   +1 more source

Generalisable deep Learning framework to overcome catastrophic forgetting

Intelligent Systems with Applications
Generalisation across multiple tasks is a major challenge in deep learning for medical imaging applications, as it can cause a catastrophic forgetting problem.
Zaenab Alammar, Laith Alzubaidi, Jinglan Zhang, Yuefeng Li, Ashish Gupta, Yuantong Gu   +5 more
doaj   +1 more source

Alleviating Catastrophic Forgetting in Continual Learning

, 2022
Machine learning has enjoyed rapid and substantial advances in the past few years. However, machine learning models cannot learn continually as we humans do. Humans are continual learners, meaning they can accumulate knowledge, use the previous knowledge to learn from new experiences better, and retain knowledge from previous experiences.
openaire   +2 more sources

On the Dangers of Large‐Language Model Mediated Learning for Human Capital

Human Resource Management Journal, EarlyView.
ABSTRACT Against the dominant view in HRM concerning the value‐creating use of large language models (LLMs) in relation to Human Capital, our provocation asks whether LLMs will enhance or compromise Human Capital at work in the long‐run. We feel compelled to ask this question because Human Capital represents employees' accumulated learning experiences,
Dirk Lindebaum, Eimear Nolan, Juani Swart   +2 more
wiley   +1 more source

Exploring multi-granularity balance strategy for class incremental learning via three-way granular computing

Brain Informatics
Class incremental learning (CIL) is a specific scenario in incremental learning. It aims to continuously learn new classes from the data stream, which suffers from the challenge of catastrophic forgetting.
Yan Xian, Hong Yu, Ye Wang, Guoyin Wang
doaj   +1 more source

Exploring Kolmogorov–Arnold Network Expansions in Vision Transformers for Mitigation of Catastrophic Forgetting in Continual Learning

Mathematics
Continual Learning (CL), the ability of a model to learn new tasks without forgetting previously acquired knowledge, remains a critical challenge in artificial intelligence. This is particularly true for Vision Transformers (ViTs) that utilize Multilayer
Zahid Ullah, Jihie Kim
doaj   +1 more source

Bridging the Late Antique Gap in Northwest Arabia: New Archaeological Evidence on the Occupation of Wādī al‐Qurā (al‐ʿUlā [AlUla], Saudi Arabia) Between the Third and Seventh Centuries CE

Arabian Archaeology and Epigraphy, EarlyView.
ABSTRACT In 2019, the Dadan Archaeological Project (CNRS/RCU/AFALULA) identified a Late Antique village 1 km south of ancient Dadan in the al‐ʿUlā valley (northwest Saudi Arabia). Three excavation seasons at this site (2021–2023) have uncovered a massive building constructed in the late third or early fourth cent.
Jérôme Rohmer, Abdulrahman Alsuhaibani, Charlène Bouchaud, Elora Chambraud, Vladimir Dabrowski, Hatem Djerbi, Fabien Lesguer, Hervé Monchot, Paul Picavet, Louise Purdue, Bertrand Riba, Shadi Shabo   +11 more
wiley   +1 more source

Map-based experience replay: a memory-efficient solution to catastrophic forgetting in reinforcement learning. [PDF]

Front Neurorobot, 2023
Hafez MB, Immisch T, Weber T, Wermter S.
europepmc   +1 more source

What is (de)politicization and what is wrong with it?

American Journal of Political Science, EarlyView.
Abstract This article attempts to clarify the meaning of (de)politicization. Politicization sometimes refers to the inappropriate intrusion of partisan loyalties in nonpolitical social domains (affective politicization). Politicization can also constitute an ideal of civic agency and energy (contestatory politicization).
Dimitrios Halikias
wiley   +1 more source

CIELO: Class-Incremental Continual Learning for Overcoming Catastrophic Forgetting With Smartphone-Based Indoor Localization

IEEE Access
With dynamically evolving indoor environments, class-incremental learning (CIL) plays a crucial role in enabling indoor localization systems to adapt to new indoor areas.
Akhil Singampalli, Danish Gufran, Sudeep Pasricha   +2 more
doaj   +1 more source