:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autore principale:	Li, Wenqing
Natura:	Recurso digital
Lingua:
Pubblicazione:	Zenodo 2026
Accesso online:	https://doi.org/10.5281/zenodo.19627739
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning
di: Li, Wenqing, et al.
Pubblicazione: (2026)

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
di: Stops, Laura, et al.
Pubblicazione: (2022)

Portfolio management based on a reinforcement learning framework
di: Wu Junfeng, et al.
Pubblicazione: (2024)

Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
di: Belliardo, Federico, et al.
Pubblicazione: (2023)

Self‐triggered control for linear systems based on hierarchical reinforcement learning
di: Wei Chen, et al.
Pubblicazione: (2024)

Enabling microrobotic chemotaxis via reset-free hierarchical reinforcement learning
di: Xiong, Tongzhao, et al.
Pubblicazione: (2024)

Enhancing the thermodynamic performance of rare‐earth tantalate materials by high‐entropy design
di: Siqian Li, et al.
Pubblicazione: (2025)

Model-based reinforcement learning for protein backbone design
di: Renard, Frederic, et al.
Pubblicazione: (2024)

Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning
di: Chen, Di, et al.
Pubblicazione: (2023)

MatrixFlow: System-Accelerator co-design for high-performance transformer applications
di: Liu, Qunyou, et al.
Pubblicazione: (2025)

A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
di: Sun, Ruoyu, et al.
Pubblicazione: (2025)

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
di: Kobayashi, Seijin, et al.
Pubblicazione: (2025)

Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots
di: Schmidgall, Samuel, et al.
Pubblicazione: (2023)

Rulebook: bringing co-routines to reinforcement learning environments
di: Fioravanti, Massimo, et al.
Pubblicazione: (2025)

3D hierarchical porous N, O co‐doped carbon derived from the waste walnut shell via one‐step carbonization for high‐performance supercapacitor
di: Zhentao Liu, et al.
Pubblicazione: (2024)

Enhancing reinforcement learning for population setpoint tracking in co-cultures
di: Espinel-Ríos, Sebastián, et al.
Pubblicazione: (2024)

Non‐overlapping placement of macro cells based on reinforcement learning in chip design
di: Tao Yu, et al.
Pubblicazione: (2024)

A hierarchical spatial-aware algorithm with efficient reinforcement learning for human-robot task planning and allocation in production
di: Xue, Jintao, et al.
Pubblicazione: (2026)

Rank-based loss for learning hierarchical representations
di: Nolasco, Ines, et al.
Pubblicazione: (2021)

A reinforcement learning methodology to hierarchical sliding‐mode surface H∞ control of nonlinear systems via a dynamic event‐triggered mechanism
di: Tengda Wang, et al.
Pubblicazione: (2025)

FPGA-based implementation of an S-Box cryptographic co-processor for high-performance applications
di: Moulai Khatir, Ahmed Nassim, et al.
Pubblicazione: (2025)

Automated scheduler for the SOXS instrument: design and performance
di: Asquini, Laura, et al.
Pubblicazione: (2024)

Combining reinforcement learning with graph convolutional neural networks for efficient design of TiAl/TiAlN interfaces
di: Xinyu Jiang, et al.
Pubblicazione: (2025)

Flat-band thermodynamics reveals enhanced performance across Otto, Carnot, and Stirling cycles
di: Soufy, Hadi Mohammed, et al.
Pubblicazione: (2025)

Applications of deep reinforcement learning to urban transit network design
di: Holliday, Andrew
Pubblicazione: (2025)

Domain knowledge and interpretable machine learning for designing high performance high‐entropy alloys/graphene composite
di: Duan‐Jie Cheng, et al.
Pubblicazione: (2025)

Enhanced extrapolative machine learning for designing high‐performance multi‐principal‐element superalloys
di: Qiu‐Ling Tao, et al.
Pubblicazione: (2025)

Thermodynamic properties of rare‐earth tantalates enhanced by high entropy design
di: Wenqing Liu, et al.
Pubblicazione: (2024)

RLGT: A reinforcement learning framework for extremal graph theory
di: Damnjanović, Ivan, et al.
Pubblicazione: (2026)

Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data
di: Delrocq, Ariane, et al.
Pubblicazione: (2026)

HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification
di: Jalali, Parnian, et al.
Pubblicazione: (2023)

Leveraging LLMs for reward function design in reinforcement learning control tasks
di: Cardenoso, Franklin, et al.
Pubblicazione: (2025)

Deep reinforcement learning for efficient exploration of combinatorial structural design spaces
di: Hong, Chloe S. H., et al.
Pubblicazione: (2025)

Multiarc and curve graphs are hierarchically hyperbolic
di: Kopreski, Michael C.
Pubblicazione: (2023)

Exponential speedups for quantum walks in random hierarchical graphs
di: Balasubramanian, Shankar, et al.
Pubblicazione: (2023)

Design of an all-facet illuminator for high NA EUV lithography exposure tool based on deep reinforcement learning
di: Li, Tong, et al.
Pubblicazione: (2025)

Towards deep learning sequence-structure co-generation for protein design
di: Wang, Chentong, et al.
Pubblicazione: (2024)

Uncontrolled learning: co-design of neuromorphic hardware topology for neuromorphic algorithms
di: Barrows, Frank, et al.
Pubblicazione: (2024)

Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis
di: Li, Hao, et al.
Pubblicazione: (2026)

Potential gains of communication-compute-control co-design based performance optimization methods in cyber-physical systems
di: Rácz, Sándor, et al.
Pubblicazione: (2025)