no code implementations • 27 May 2024 • Aditya Ravuri, Neil D. Lawrence
This paper shows that the dimensionality reduction methods, UMAP and t-SNE, can be approximately recast as MAP inference methods corresponding to a generalized Wishart-based model introduced in ProbDR.
no code implementations • 6 May 2024 • Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence
Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data.
no code implementations • 25 Dec 2023 • Aditya Ravuri, Erica Cooper, Junichi Yamagishi
Predicting audio quality in voice synthesis and conversion systems is a critical yet challenging task, especially when traditional methods like Mean Opinion Scores (MOS) are cumbersome to collect at scale.
no code implementations • 15 Apr 2023 • Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data.
1 code implementation • NeurIPS 2023 • Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Samuel Stanton, Gary Tom, Bojana Rankovic, Yuanqi Du, Arian Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Durholt, Saudamini Chaurasia, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
1 code implementation • 29 Oct 2022 • Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
Ice cores record crucial information about past climate.
1 code implementation • 14 Sep 2022 • Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts.
no code implementations • 25 Feb 2022 • Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
We show how this framework is compatible with different latent variable formulations and perform experiments to compare a suite of models.
no code implementations • pproximateinference AABI Symposium 2021 • Vidhi Lalchand, Aditya Ravuri, Neil D Lawrence
The Bayesian incarnation of the GPLVM uses a variational framework, where the posterior over all unknown quantities is approximated by a well-behaved variational family, a factorised Gaussian.