Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Midgley, Laurence"'
Autor:
Zhang, Fengzhe, He, Jiajun, Midgley, Laurence I., Antorán, Javier, Hernández-Lobato, José Miguel
Diffusion models have shown promising potential for advancing Boltzmann Generators. However, two critical challenges persist: (1) inherent errors in samples due to model imperfections, and (2) the requirement of hundreds of functional evaluations (NF
Externí odkaz:
http://arxiv.org/abs/2409.07323
Autor:
Midgley, Laurence I., Stimper, Vincent, Antorán, Javier, Mathieu, Emile, Schölkopf, Bernhard, Hernández-Lobato, José Miguel
Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems. However, the standard coupling architecture precludes endowing flows that operate on the Cartesi
Externí odkaz:
http://arxiv.org/abs/2308.10364
Autor:
Bonnet, Clément, Luo, Daniel, Byrne, Donal, Surana, Shikha, Abramowitz, Sasha, Duckworth, Paul, Coyette, Vincent, Midgley, Laurence I., Tegegn, Elshadai, Kalloniatis, Tristan, Mahjoub, Omayma, Macfarlane, Matthew, Smit, Andries P., Grinsztajn, Nathan, Boige, Raphael, Waters, Cemlyn N., Mimouni, Mohamed A., Sob, Ulrich A. Mbou, de Kock, Ruan, Singh, Siddarth, Furelos-Blanco, Daniel, Le, Victor, Pretorius, Arnu, Laterre, Alexandre
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms. In modern RL research, there is a need for simulated environments that are performant, scalable, and modular to e
Externí odkaz:
http://arxiv.org/abs/2306.09884
Meta-gradient Reinforcement Learning (RL) allows agents to self-tune their hyper-parameters in an online fashion during training. In this paper, we identify a bias in the meta-gradient of current meta-gradient RL approaches. This bias comes from usin
Externí odkaz:
http://arxiv.org/abs/2211.10550
This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence. The aim of the SAC agent was to separate a hydrocarbon mixture in its
Externí odkaz:
http://arxiv.org/abs/2211.04327
Autor:
Midgley, Laurence Illing, Stimper, Vincent, Simm, Gregor N. C., Schölkopf, Bernhard, Hernández-Lobato, José Miguel
Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples
Externí odkaz:
http://arxiv.org/abs/2208.01893
Autor:
Midgley, Laurence Illing, Stimper, Vincent, Simm, Gregor N. C., Hernández-Lobato, José Miguel
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current flow-based approaches are limited on challenging targets where they eith
Externí odkaz:
http://arxiv.org/abs/2111.11510
Autor:
Midgley, Laurence Illing
This paper demonstrates the application of reinforcement learning (RL) to process synthesis by presenting Distillation Gym, a set of RL environments in which an RL agent is tasked with designing a distillation train, given a user defined multi-compon
Externí odkaz:
http://arxiv.org/abs/2009.13265
Autor:
Midgley, Laurence, Thomson, Michael
This thesis demonstrated, for the first time, that reinforcement learning (RL) can be applied to chemical engineering process synthesis (sequencing and design of unit operations to generate a process flowsheet). Two case studies were used, with simpl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac5dc7616768c0289be81e49a644d4d9