:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Milon-Harnois, Gaelle, Touhami, Chaimaa, Gutowski, Nicolas, Da Mota, Benoit, Cauchy, Thomas
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2510.00802
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Transformer Model for Predicting Chemical Products from Generic SMARTS Templates with Data Augmentation
di: Ozer, Derin, et al.
Pubblicazione: (2025)

Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties
di: Leguy, Jules, et al.
Pubblicazione: (2021)

Adding Conditional Control to Diffusion Models with Reinforcement Learning
di: Zhao, Yulai, et al.
Pubblicazione: (2024)

Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition
di: Razig, Amine, et al.
Pubblicazione: (2025)

Centralized vs Decentralized Federated Learning: A trade-off performance analysis
di: Medjadji, Chaimaa, et al.
Pubblicazione: (2026)

Quality-Driven Selective Mutation for Deep Learning
di: Ahmed, Zaheed, et al.
Pubblicazione: (2026)

Efficient Extractive Text Summarization for Online News Articles Using Machine Learning
di: Biswas, Sajib, et al.
Pubblicazione: (2025)

Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning
di: Azim, Ehtesamul, et al.
Pubblicazione: (2025)

Evolutionary System Prompt Learning for Reinforcement Learning in LLMs
di: Zhang, Lunjun, et al.
Pubblicazione: (2026)

Proximal Policy Optimization with Evolutionary Mutations
di: Czworkowski, Casimir, et al.
Pubblicazione: (2026)

Goal-Guided Efficient Exploration via Large Language Model in Reinforcement Learning
di: Qi, Yajie, et al.
Pubblicazione: (2025)

Survival Dynamics of Neural and Programmatic Policies in Evolutionary Reinforcement Learning
di: Roupassov-Ruiz, Anton, et al.
Pubblicazione: (2026)

Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design
di: Chen, Lianghong, et al.
Pubblicazione: (2025)

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning
di: Zhang, Xiangyu
Pubblicazione: (2024)

Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems
di: Cauz, Marine, et al.
Pubblicazione: (2024)

FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning
di: Medjadji, Chaimaa, et al.
Pubblicazione: (2025)

Free Random Projection for In-Context Reinforcement Learning
di: Hayase, Tomohiro, et al.
Pubblicazione: (2025)

Bridging Brain with Foundation Models through Self-Supervised Learning
di: Altaheri, Hamdi, et al.
Pubblicazione: (2025)

Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving
di: Su, Chang, et al.
Pubblicazione: (2026)

RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion
di: Hu, Tianmeng, et al.
Pubblicazione: (2026)

MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization
di: Chen, Xiangsen, et al.
Pubblicazione: (2026)

Molecular De Novo Design through Transformer-based Reinforcement Learning
di: Xu, Pengcheng, et al.
Pubblicazione: (2023)

FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation
di: Weng, Jiawen, et al.
Pubblicazione: (2024)

PETRA: Pretrained Evolutionary Transformer for SARS-CoV-2 Mutation Prediction
di: Zou, Xu
Pubblicazione: (2025)

REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
di: Altmann, Philipp, et al.
Pubblicazione: (2024)

Environment Design for Inverse Reinforcement Learning
di: Buening, Thomas Kleine, et al.
Pubblicazione: (2022)

Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning
di: Falzari, Massimiliano, et al.
Pubblicazione: (2025)

PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary Algorithms
di: Ali, Shady, et al.
Pubblicazione: (2025)

Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning
di: Tiofack, Franki Nguimatsia, et al.
Pubblicazione: (2025)

Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
di: Maus, Tom, et al.
Pubblicazione: (2026)

Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models
di: Sygkounas, Alkis, et al.
Pubblicazione: (2026)

Transformer-Guided Deep Reinforcement Learning for Optimal Takeoff Trajectory Design of an eVTOL Drone
di: Roberts II, Nathan M., et al.
Pubblicazione: (2025)

Uncertainty-Guided Checkpoint Selection for Reinforcement Finetuning of Large Language Models
di: Nguyen, Manh, et al.
Pubblicazione: (2025)

Goal-Conditioned Data Augmentation for Offline Reinforcement Learning
di: Huang, Xingshuai, et al.
Pubblicazione: (2024)

Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
di: Klarner, Leo, et al.
Pubblicazione: (2024)

Federated Imputation under Heterogeneous Feature Spaces
di: Hocine, Imane, et al.
Pubblicazione: (2026)

Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging
di: Morrison, Jacob, et al.
Pubblicazione: (2024)

Active Learning to Guide Labeling Efforts for Question Difficulty Estimation
di: Thuy, Arthur, et al.
Pubblicazione: (2024)

Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?
di: Xu, Yanchen, et al.
Pubblicazione: (2024)

Opinion-Guided Reinforcement Learning
di: Dagenais, Kyanna, et al.
Pubblicazione: (2024)