1 citations · 1 across the 2 of their papers we have counts for
7 papers
Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
K. Michael Martini, Eslam Abdelaleem, Paarth Gulati +1
Identifying the dynamical state variables of a system from high-dimensional observations is a central problem across physical sciences. The challenge is that the state variables ar…
Accurate Estimation of Mutual Information in High Dimensional Data
Eslam Abdelaleem, K. Michael Martini, Ilya Nemenman
Mutual information (MI) quantifies statistical dependence between variables and is widely used across scientific disciplines, yet accurate estimation from finite data remains notor…
Mutual information and task-relevant latent dimensionality
Paarth Gulati, Eslam Abdelaleem, Audrey Sederberg +1
Estimating the dimensionality of the latent representation needed for prediction -- the task-relevant dimension -- is a difficult, largely unsolved problem with broad scientific ap…
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses
Eslam Abdelaleem, Ilya Nemenman, K. Michael Martini
Variational dimensionality reduction methods are widely used for their accuracy, generative capabilities, and robustness. We introduce a unifying framework that generalizes both su…
Physics-tailored machine learning reveals unexpected physics in dusty plasmas
Wentao Yu, Eslam Abdelaleem, Ilya Nemenman +1
Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb f…
Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets
Eslam Abdelaleem
The quest for simplification in physics drives the exploration of concise mathematical representations for complex systems. This Dissertation focuses on the concept of dimensionali…