1 citations · 1 across the 8 of their papers we have counts for
8 papers
The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench
Dingling Yao, Andrea Polesello, Adeel Pervez +2
While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invaria…
Controlling Transient Amplification Improves Long-horizon Rollouts
Adeel Pervez, Francesco Locatello
Autoregressive neural simulators now match classical solvers on short-horizon prediction of physical systems, yet their accuracy degrades rapidly when rolled out over long horizons…
Learning Explicit Single-Cell Dynamics Using ODE Representations
Jan-Philipp von Bassewitz, Adeel Pervez, Marco Fumero +3
Modeling the dynamics of cellular differentiation is fundamental to advancing the understanding and treatment of diseases associated with this process, such as cancer. With the rap…
Mechanistic PDE Networks for Discovery of Governing Equations
Adeel Pervez, Efstratios Gavves, Francesco Locatello
We present Mechanistic PDE Networks -- a model for discovery of governing partial differential equations from data. Mechanistic PDE Networks represent spatiotemporal data as space-…
Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
Jiale Chen, Dingling Yao, Adeel Pervez +2
We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences…
Mechanistic Neural Networks for Scientific Machine Learning
Adeel Pervez, Francesco Locatello, Efstratios Gavves
This paper presents Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard arch…
Differentiable Mathematical Programming for Object-Centric Representation Learning
Adeel Pervez, Phillip Lippe, Efstratios Gavves
We propose topology-aware feature partitioning into disjoint partitions for given scene features as a method for object-centric representation learning. To this end, we propose…
A Fourier View of REINFORCE
Adeel Pervez
We show a connection between the Fourier spectrum of Boolean functions and the REINFORCE gradient estimator for binary latent variable models. We show that REINFORCE estimates (up…