:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Marcandelli, Paolo, Mathur, Natansh, Markidis, Stefano, Siena, Martina, Mariani, Stefano
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2602.02264
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning
by: Marcandelli, Paolo, et al.
Published: (2025)

A Continuous-Variable Quantum Fourier Layer: Applications to Filtering and PDE Solving
by: Marcandelli, Paolo, et al.
Published: (2026)

Training-efficient density quantum machine learning
by: Coyle, Brian, et al.
Published: (2024)

A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference
by: Briglia, Giovanni, et al.
Published: (2025)

Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS
by: Pennati, Luca, et al.
Published: (2026)

CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
by: Azad, Abdus Salam, et al.
Published: (2022)

Transformer-Based Approach to Optimal Sensor Placement for Structural Health Monitoring of Probe Cards
by: Bejani, Mehdi, et al.
Published: (2025)

Bayesian Federated Learning for Continual Training
by: Milasheuski, Usevalad, et al.
Published: (2025)

Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
by: Xi, Zhiheng, et al.
Published: (2024)

Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
by: Lao, Dong, et al.
Published: (2023)

Exploring Expert Specialization through Unsupervised Training in Sparse Mixture of Experts
by: Nikolic, Strahinja, et al.
Published: (2025)

Entropy-Driven Curriculum for Multi-Task Training in Human Mobility Prediction
by: Fang, Tianye, et al.
Published: (2025)

GraphDancer: Training LLMs to Explore and Reason over Graphs via Two-Stage Curriculum Post-Training
by: Bai, Yuyang, et al.
Published: (2026)

Sobolev Training for Operator Learning
by: Cho, Namkyeong, et al.
Published: (2024)

Physics-Informed Neural Network Surrogate Models for River Stage Prediction
by: Zoch, Maximilian, et al.
Published: (2025)

SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
by: Cundy, Chris, et al.
Published: (2023)

Enhanced Water Leak Detection with Convolutional Neural Networks and One-Class Support Vector Machine
by: Leonzio, Daniele Ugo, et al.
Published: (2025)

DLGNet: Hyperedge Classification through Directed Line Graphs for Chemical Reactions
by: Fiorini, Stefano, et al.
Published: (2024)

Enhancing Imbalanced Node Classification via Curriculum-Guided Feature Learning and Three-Stage Attention Network
by: Fofanah, Abdul Joseph, et al.
Published: (2026)

Multi-Objective Loss Balancing for Physics-Informed Deep Learning
by: Bischof, Rafael, et al.
Published: (2021)

Dealing with Uncertainty in Contextual Anomaly Detection
by: Bindini, Luca, et al.
Published: (2025)

Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens
by: Bortolotti, Samuele, et al.
Published: (2025)

Efficient Mitigation of Bus Bunching through Setter-Based Curriculum Learning
by: Shah, Avidan, et al.
Published: (2024)

Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation
by: Zhou, Yangxuan, et al.
Published: (2024)

Boosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design
by: Solbach, Markus D., et al.
Published: (2025)

Towards Principled Unsupervised Multi-Agent Reinforcement Learning
by: Zamboni, Riccardo, et al.
Published: (2025)

Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
by: von Kügelgen, Julius
Published: (2024)

Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
by: Ma, Guozheng, et al.
Published: (2023)

Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
by: An, Kang, et al.
Published: (2026)

Improved Training of Physics-Informed Neural Networks with Model Ensembles
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)

Personalized Algorithmic Recourse with Preference Elicitation
by: De Toni, Giovanni, et al.
Published: (2022)

SMAT: Staged Multi-Agent Training for Co-Adaptive Exoskeleton Control
by: Yuan, Yifei, et al.
Published: (2026)

Curriculum Abductive Learning
by: Hu, Wen-Chao, et al.
Published: (2025)

Train Long, Think Short: Curriculum Learning for Efficient Reasoning
by: Hammoud, Hasan Abed Al Kader, et al.
Published: (2025)

Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
by: Giamattei, Luca, et al.
Published: (2024)

Pure and Physics-Guided Deep Learning Solutions for Spatio-Temporal Groundwater Level Prediction at Arbitrary Locations
by: Salis, Matteo, et al.
Published: (2026)

MADiff: Offline Multi-agent Learning with Diffusion Models
by: Zhu, Zhengbang, et al.
Published: (2023)

Learning to Embed Distributions via Maximum Kernel Entropy
by: Kachaiev, Oleksii, et al.
Published: (2024)

Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
by: Dremov, Aleksandr, et al.
Published: (2025)

Learning Progress Driven Multi-Agent Curriculum
by: Zhao, Wenshuai, et al.
Published: (2022)