Search Results for author: Sach Mukherjee

Found 12 papers, 6 papers with code

Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers

no code implementations21 Jun 2023 Kai Lagemann, Christian Lagemann, Sach Mukherjee

We focus on the setting in which data are available from multiple different instances of a system whose underlying dynamical model is entirely unknown at the outset.

Inductive Bias

Deep Learning of Causal Structures in High Dimensions

no code implementations9 Dec 2022 Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee

Recent years have seen rapid progress at the intersection between causality and machine learning.

Vocal Bursts Intensity Prediction

High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data

no code implementations21 Nov 2022 Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee

Methods for learning such graphical models are well developed in the case where all variables are either continuous or discrete, including in high-dimensions.

Vocal Bursts Intensity Prediction

Scalable Regularised Joint Mixture Models

1 code implementation3 May 2022 Thomas Lartigue, Sach Mukherjee

Building on developments at the intersection of unsupervised learning and regularised regression, we propose an approach for heterogeneous data that allows joint learning of (i) explicit multivariate feature distributions, (ii) high-dimensional regression models and (iii) latent group labels, with both (i) and (ii) specific to latent groups and both elements informing (iii).

Computational Efficiency regression

On unsupervised projections and second order signals

no code implementations11 Apr 2022 Thomas Lartigue, Sach Mukherjee

Motivated by such applications, in this paper we ask whether linear projections can preserve differences in second order structure between latent groups.

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 Feb 2020 Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander

TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.

Alzheimer's Disease Detection Disease Prediction

Ancestral causal learning in high dimensions with a human genome-wide application

no code implementations27 May 2019 Umberto Noè, Bernd Taschler, Joachim Täger, Peter Heutink, Sach Mukherjee

We focus on the setting in which some causal (ancestral) relationships are known (via background knowledge or experimental data) and put forward a general approach that scales to large problems.

Vocal Bursts Intensity Prediction

Model-based clustering in very high dimensions via adaptive projections

1 code implementation22 Feb 2019 Bernd Taschler, Frank Dondelinger, Sach Mukherjee

In some examples, MCAP performs well even when the mean signal is entirely removed, leaving differential covariance structure in the high-dimensional space as the only signal.

Clustering Vocal Bursts Intensity Prediction

Exact Estimation of Multiple Directed Acyclic Graphs

no code implementations4 Apr 2014 Chris. J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens

This paper considers the problem of estimating the structure of multiple related directed acyclic graph (DAG) models.

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