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pro vyhledávání: '"Rende P"'
The remarkable capability of over-parameterised neural networks to generalise effectively has been explained by invoking a ``simplicity bias'': neural networks prevent overfitting by initially learning simple classifiers before progressing to more co
Externí odkaz:
http://arxiv.org/abs/2410.19637
Publikováno v:
J. Chem. Phys. 161, 064503 (2024)
Policy-guided Monte Carlo is an adaptive method to simulate classical interacting systems. It adjusts the proposal distribution of the Metropolis-Hastings algorithm to maximize the sampling efficiency, using a formalism inspired by reinforcement lear
Externí odkaz:
http://arxiv.org/abs/2407.03275
The dot product attention mechanism, originally designed for natural language processing (NLP) tasks, is a cornerstone of modern Transformers. It adeptly captures semantic relationships between word pairs in sentences by computing a similarity overla
Externí odkaz:
http://arxiv.org/abs/2405.18874
Recent progress in the design and optimization of Neural-Network Quantum States (NQS) have made them an effective method to investigate ground-state properties of quantum many-body systems. In contrast to the standard approach of training a separate
Externí odkaz:
http://arxiv.org/abs/2403.07795
Autor:
Viteritti, Luciano Loris, Rende, Riccardo, Parola, Alberto, Goldt, Sebastian, Becca, Federico
Quantum magnetism in two-dimensional systems represents a lively branch of modern condensed-matter physics. In the presence of competing super-exchange couplings, magnetic order is frustrated and can be suppressed down to zero temperature, leading to
Externí odkaz:
http://arxiv.org/abs/2311.16889
Autor:
Rende, Riccardo, Viteritti, Luciano Loris, Bardone, Lorenzo, Becca, Federico, Goldt, Sebastian
Publikováno v:
Communications Physics 7, 260 (2024)
Neural-network architectures have been increasingly used to represent quantum many-body wave functions. These networks require a large number of variational parameters and are challenging to optimize using traditional methods, as gradient descent. St
Externí odkaz:
http://arxiv.org/abs/2310.05715
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Understanding the spatiotemporal characteristics and comprehensive service capabilities of various ecosystem services is crucial for maintaining regional ecosystem security, and clarifying the driving mechanisms of ecosystem services plays a
Externí odkaz:
https://doaj.org/article/2233075a03324d92b3c666d16412457a
Publikováno v:
Phys. Rev. Research 6, 023057 (2024)
Transformers are neural networks that revolutionized natural language processing and machine learning. They process sequences of inputs, like words, using a mechanism called self-attention, which is trained via masked language modeling (MLM). In MLM,
Externí odkaz:
http://arxiv.org/abs/2304.07235
Publikováno v:
Communications Physics, Vol 7, Iss 1, Pp 1-8 (2024)
Abstract Neural-network architectures have been increasingly used to represent quantum many-body wave functions. These networks require a large number of variational parameters and are challenging to optimize using traditional methods, as gradient de
Externí odkaz:
https://doaj.org/article/8d4a53f3e612419aa42ce679e59d3a25
Publikováno v:
Cailiao gongcheng, Vol 52, Iss 7, Pp 162-172 (2024)
Finite element simulation is one of the effective means to study the stress evolution of thermally grown oxides （TGO） at the interface of thermal barrier coatings （TBCs）， which can provide theoretical support for exploring the failure mecha
Externí odkaz:
https://doaj.org/article/6f16aa9ef7af4e5b9221dda859cfbacd