2 papers
q-bio.GN2026
CRANE: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models
Simon Ambrozak, Ulysse McConnell, Bhargav Srinivasan +3
Nanopore sequencing can read substantially longer sequences of nucleic acid molecules, called reads, than other sequencing methods, which has led to advances in genomic analysis su…
cs.LG2025
Efficient Restarts in Non-Stationary Model-Free Reinforcement Learning
Hiroshi Nonaka, Simon Ambrozak, Sofia R. Miskala-Dinc +2
In this work, we propose three efficient restart paradigms for model-free non-stationary reinforcement learning (RL). We identify two core issues with the restart design of Mao et…