Search Results for author:
Found 6 papers, 2 papers with code
Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles
Can a micron sized sack of interacting molecules autonomously learn an internal model of a complex and fluctuating environment?
Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines
These results illustrate how a biochemical computer can use intrinsic chemical noise to perform complex computations.
Active Inference for Stochastic Control
Active inference has emerged as an alternative approach to control problems given its intuitive (probabilistic) formalism.
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic data.
A reaction network scheme which implements inference and learning for Hidden Markov Models
With a view towards molecular communication systems and molecular multi-agent systems, we propose the Chemical Baum-Welch Algorithm, a novel reaction network scheme that learns parameters for Hidden Markov Models (HMMs).
A Scheme for Molecular Computation of Maximum Likelihood Estimators for Log-Linear Models
We focus on the much-studied statistical inference problem of computing maximum likelihood estimators for log-linear models.
