A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The article introduces ProvFL, a mechanism that aids in interpreting and explaining global model predictions in Federated Learning (FL). It’s designed to track the information flow between individual clients in…
Continue readingThe paper introduces ‘Adapt & Align’, a method for continual learning of neural networks using generative models. The method addresses the issue of performance loss in neural networks when retrained…
Continue readingThis article discusses the growing issue of data leakage within organizations. To mitigate this, a statistical Data Leakage Prevention (DLP) system is proposed, which classifies documents before access is granted….
Continue readingThe article introduces the Critic-Guided Decision Transformer (CGDT), a novel approach to offline reinforcement learning (RL). Traditional Return-Conditioned Supervised Learning (RCSL) struggles with stochastic environments and diverse future trajectory distributions….
Continue readingThe paper presents a novel algorithm, Federated Learning with Accumulated Regularized Embeddings (FLARE), which addresses challenges in decentralized machine learning, specifically in federated learning (FL). The algorithm uses sparse training…
Continue readingThe paper delves into the epistemic justification for a simplicity preference in inductive inference, derived from the machine learning framework of statistical learning theory. It combines elements from earlier arguments…
Continue readingThe paper ‘Manipulating Trajectory Prediction with Backdoors’ discusses the security implications of trajectory prediction in autonomous vehicles. The authors focus on ‘backdoors’, a security threat that has been overlooked in…
Continue readingThe article discusses the potential of Large Language Models (LLMs) to extract relevant insights from vast amounts of data. The authors propose a new evaluation methodology based on a ‘capture…
Continue readingThe study explores the use of anomaly detection (AD) methods in imbalanced learning tasks, specifically for fraud detection in online credit card payments. The performance of various AD methods is…
Continue readingThis research paper introduces Fed-CO2, a universal Federated Learning (FL) framework designed to handle both label distribution skew and feature skew. It uses a cooperation mechanism between online and offline…
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