Aditya Ravuri - RankMe

19 papers A* 2A 1Journal 14Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout. CoRR Aditya Ravuri, Neil D. Lawrence
2025	J	`jnl`	The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. J. Mach. Learn. Res. Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy
2025	A*	`conf`	Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models. ICML Konstantin Donhauser, Kristina Ulicna, Gemma E. Moran, Aditya Ravuri, Kian Kenyon-Dean, Cian Eastwood, Jason S. Hartford
2025	J	`jnl`	Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps: An Interpretation and Potential Improvements. CoRR Aditya Ravuri, Neil D. Lawrence
2024	J	`jnl`	On Feature Learning for Titi Monkey Activity Detection. CoRR Aditya Ravuri, Jen Muir, Neil D. Lawrence
2024	J	`jnl`	Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. CoRR Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence
2024	J	`jnl`	The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. CoRR Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy
2024	J	`jnl`	Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE. CoRR Aditya Ravuri, Neil D. Lawrence
2024	J	`jnl`	Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models. CoRR Konstantin Donhauser, Kristina Ulicna, Gemma E. Moran, Aditya Ravuri, Kian Kenyon-Dean, Cian Eastwood, Jason S. Hartford
2024	—	`conf`	Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction. ICASSP Workshops Aditya Ravuri, Erica Cooper, Junichi Yamagishi
2023	J	`jnl`	Dimensionality Reduction as Probabilistic Inference. CoRR Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence
2023	A*	`conf`	GAUCHE: A Library for Gaussian Processes in Chemistry. NeurIPS Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Yuanqi Du, Samuel Stanton, Gary Tom, Bojana Rankovic, Arian Rokkum Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang
2023	J	`jnl`	Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction. CoRR Aditya Ravuri, Erica Cooper, Junichi Yamagishi
2022	J	`jnl`	GAUCHE: A Library for Gaussian Processes in Chemistry. CoRR Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Bojana Rankovic, Yuanqi Du, Arian R. Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang
2022	A	`conf`	Generalised GPLVM with Stochastic Variational Inference. AISTATS Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
2022	J	`jnl`	Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. CoRR Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
2022	J	`jnl`	Ice Core Dating using Probabilistic Programming. CoRR Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
2022	—	`conf`	Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. MLCB Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
2022	J	`jnl`	Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. CoRR Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence