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pro vyhledávání: '"Knapp, Edward"'
Autor:
Williams, Nicholas J., Kabalan, Lara, Stojanovic, Ljiljana, Zolyomi, Viktor, Pyzer-Knapp, Edward O.
A significant challenge in computational chemistry is developing approximations that accelerate \emph{ab initio} methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing
Externí odkaz:
http://arxiv.org/abs/2408.08006
Autor:
Mangal, Prattyush, Mak, Carol, Kanakis, Theo, Donovan, Timothy, Braines, Dave, Pyzer-Knapp, Edward
The emergence of Large Language Models (LLMs) have fundamentally altered the way we interact with digital systems and have led to the pursuit of LLM powered AI agents to assist in daily workflows. LLMs, whilst powerful and capable of demonstrating so
Externí odkaz:
http://arxiv.org/abs/2408.01380
We show that common choices of kernel functions for a highly accurate and massively scalable nearest-neighbour based GP regression model (GPnn: \cite{GPnn}) exhibit gradual convergence to asymptotic behaviour as dataset-size $n$ increases. For isotro
Externí odkaz:
http://arxiv.org/abs/2404.06200
The accurate predictions and principled uncertainty measures provided by GP regression incur O(n^3) cost which is prohibitive for modern-day large-scale applications. This has motivated extensive work on computationally efficient approximations. We i
Externí odkaz:
http://arxiv.org/abs/2306.14731
Prior beliefs about the latent function to shape inductive biases can be incorporated into a Gaussian Process (GP) via the kernel. However, beyond kernel choices, the decision-making process of GP models remains poorly understood. In this work, we co
Externí odkaz:
http://arxiv.org/abs/2305.10748
Autor:
Graff, David E., Pyzer-Knapp, Edward O., Jordan, Kirk E., Shakhnovich, Eugene I., Coley, Connor W.
Quantitative structure-property relationships (QSPRs) aid in understanding molecular properties as a function of molecular structure. When the correlation between structure and property weakens, a dataset is described as "rough," but this characteris
Externí odkaz:
http://arxiv.org/abs/2305.08238
When comparing approximate Gaussian process (GP) models, it can be helpful to be able to generate data from any GP. If we are interested in how approximate methods perform at scale, we may wish to generate very large synthetic datasets to evaluate th
Externí odkaz:
http://arxiv.org/abs/2211.08036
Multifidelity and multioutput optimisation algorithms are of active interest in many areas of computational design as they allow cheaper computational proxies to be used intelligently to aid experimental searches for high-performing species. Characte
Externí odkaz:
http://arxiv.org/abs/2208.05667
Autor:
Aldeghi, Matteo, Graff, David E., Frey, Nathan, Morrone, Joseph A., Pyzer-Knapp, Edward O., Jordan, Kirk E., Coley, Connor W.
Publikováno v:
J. Chem. Inf. Model. 2022, 62, 19, 4660-4671
In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property l
Externí odkaz:
http://arxiv.org/abs/2207.09250