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20242026
most citedA Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance

5 citations · 5 across the 2 of their papers we have counts for

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11 papers · 1 filter

cs.LG2026

IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution

Udo Schlegel, Julian Rakuschek, Thomas Seidl +3

Oscillatory signals, such as vibration, carry class-discriminative information in specific frequency bands; perturbing them in raw feature space for counterfactual analysis easily…

cs.LG2025

AiGAS-dEVL-RC: An Adaptive Growing Neural Gas Model for Recurrently Drifting Unsupervised Data Streams

Maria Arostegi, Miren Nekane Bilbao, Jesus L. Lobo +1

Concept drift and extreme verification latency pose significant challenges in data stream learning, particularly when dealing with recurring concept changes in dynamic environments…

cs.LG2025

Forward-Forward Learning achieves Highly Selective Latent Representations for Out-of-Distribution Detection in Fully Spiking Neural Networks

Erik B. Terres-Escudero, Javier Del Ser, Aitor Martínez-Seras +1

In recent years, Artificial Intelligence (AI) models have achieved remarkable success across various domains, yet challenges persist in two critical areas: ensuring robustness agai…

cs.LG2025

A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks

Erik B. Terres-Escudero, Javier Del Ser, Pablo Garcia Bringas

The so-called Forward-Forward Algorithm (FFA) has recently gained momentum as an alternative to the conventional back-propagation algorithm for neural network learning, yielding co…

cs.LG2024

Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments

Alain Andres, Lukas Schäfer, Stefano V. Albrecht +1

One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large…

cs.LG2024

When AI Eats Itself: On the Caveats of AI Autophagy

Xiaodan Xing, Fadong Shi, Jiahao Huang +8

Generative Artificial Intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music. Creating thes…