269 citations · 804 across the 16 of their papers we have counts for
16 papers
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures
Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu +4
In Elmarakeby et al., "Biologically informed deep neural network for prostate cancer discovery", a feedforward neural network with biologically informed, sparse connections (P-NET)…
Multiple Physics Pretraining for Physical Surrogate Models
Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker +11
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers. I…
xVal: A Continuous Numerical Tokenization for Scientific Language Models
Siavash Golkar, Mariel Pettee, Michael Eickenberg +11
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific…
An online algorithm for contrastive Principal Component Analysis
Siavash Golkar, David Lipshutz, Tiberiu Tesileanu +1
Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Com…
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy
Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun +2
Predictive coding has emerged as an influential normative model of neural computation, with numerous extensions and applications. As such, much effort has been put into mapping PC…
Neural circuits for dynamics-based segmentation of time series
Tiberiu Tesileanu, Siavash Golkar, Samaneh Nasiri +2
The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generate…