4 citations · 4 across the 3 of their papers we have counts for
5 papers
PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling
Zichao Yan, Yan Wu, Mica Xu Ji +10
Predicting the effects of perturbations in-silico on cell state can identify drivers of cell behavior at scale and accelerate drug discovery. However, modeling challenges remain du…
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
Yan Wu, Esther Wershof, Sebastian M Schmon +7
We introduce a comprehensive framework for modeling single cell transcriptomic responses to perturbations, aimed at standardizing benchmarking in this rapidly evolving field. Our a…
Off-Policy Correction For Multi-Agent Reinforcement Learning
Michał Zawalski, Błażej Osiński, Henryk Michalewski +1
Multi-agent reinforcement learning (MARL) provides a framework for problems involving multiple interacting agents. Despite apparent similarity to the single-agent case, multi-agent…
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
Matt Vitelli, Yan Chang, Yawei Ye +7
In this paper we present the first safe system for full control of self-driving vehicles trained from human demonstrations and deployed in challenging, real-world, urban environmen…
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
Oliver Scheel, Luca Bergamini, Maciej Wołczyk +2
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstration…