Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Lalchand, Vidhi"'
Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of \textit{in-vitro} drug sensitivity screens, the goal is to develop accurate predictive models that can be used to guide experimental desig
Externí odkaz:
http://arxiv.org/abs/2407.00175
Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data. Gaussian Process Latent Variable Models (GPLVMs) offer an interpretable dimensionality reduction method, but current scalable models lack effectiveness in cluster
Externí odkaz:
http://arxiv.org/abs/2405.03879
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data. DR is a critical step in many analysis pipelines as it enables visualisation, noise reduc
Externí odkaz:
http://arxiv.org/abs/2304.07658
Publikováno v:
Advances in Neural Information Processing Systems (New Orleans), 2022
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel are chosen by maximising the marginal likelihood, an approach known a
Externí odkaz:
http://arxiv.org/abs/2211.02476
Autor:
Lalchand, Vidhi, Ravuri, Aditya, Dann, Emma, Kumasaka, Natsuhiko, Sumanaweera, Dinithi, Lindeboom, Rik G. H., Madad, Shaista, Teichmann, Sarah A., Lawrence, Neil D.
Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts. Scalable dimensionality reduction techniques are in need to disentangle biological variation
Externí odkaz:
http://arxiv.org/abs/2209.06716
The Upper Indus Basin, Himalayas provides water for 270 million people and countless ecosystems. However, precipitation, a key component to hydrological modelling, is poorly understood in this area. A key challenge surrounding this uncertainty comes
Externí odkaz:
http://arxiv.org/abs/2209.04947
Publikováno v:
Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, Valencia, Spain. PMLR: Volume 151
Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensionality reduction, extending classical Gaussian processes to an unsupervised learning context. The Bayesian incarnation of the GPLVM Titsias and Lawrence
Externí odkaz:
http://arxiv.org/abs/2202.12979
Autor:
Simpson, Fergus, Davies, Ian, Lalchand, Vidhi, Vullo, Alessandro, Durrande, Nicolas, Rasmussen, Carl
Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of functions under the GP prior. This work addresses the challenge of co
Externí odkaz:
http://arxiv.org/abs/2106.08185
Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through the optimisation of kernel hyperparameters using the marginal likelihood as the objective. This classi
Externí odkaz:
http://arxiv.org/abs/2010.16344
Autor:
Lalchand, Vidhi
The aim of this work is to propose a meta-algorithm for automatic classification in the presence of discrete binary classes. Classifier learning in the presence of overlapping class distributions is a challenging problem in machine learning. Overlapp
Externí odkaz:
http://arxiv.org/abs/2001.06880