Deep Learning Improves Parameter Estimation in Reinforcement Learning Models

Kavli Affiliate: Marcelo Mattar | Authors: Hua-Dong Xiong, Li Ji-An, Marcelo G Mattar and Robert C Wilson | Summary: Cognitive models are widely used in psychology and neuroscience to formulate and test hypotheses about cognitive processes. These processes are characterized by model parameters, which are then used for scientific inference. The reliability of scientific conclusions […]


Continue.. Deep Learning Improves Parameter Estimation in Reinforcement Learning Models

Few-shot Unknown Class Discovery of Hyperspectral Images with Prototype Learning and Clustering

Kavli Affiliate: Zhuo Li | First 5 Authors: Chun Liu, Chun Liu, , , | Summary: Open-set few-shot hyperspectral image (HSI) classification aims to classify image pixels by using few labeled pixels per class, where the pixels to be classified may be not all from the classes that have been seen. To address the open-set […]


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Copas-Jackson-type bounds for publication bias over a general class of selection models

Kavli Affiliate: Yi Zhou | First 5 Authors: Taojun Hu, Taojun Hu, , , | Summary: Publication bias (PB) is one of the most vital threats to the accuracy of meta-analysis. Adjustment or sensitivity analysis based on selection models, which describe the probability of a study being published, provide a more objective evaluation of PB […]


Continue.. Copas-Jackson-type bounds for publication bias over a general class of selection models

Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality

Kavli Affiliate: Yi Zhou | First 5 Authors: Shaocong Ma, Shaocong Ma, , , | Summary: The goal of robust constrained reinforcement learning (RL) is to optimize an agent’s performance under the worst-case model uncertainty while satisfying safety or resource constraints. In this paper, we demonstrate that strong duality does not generally hold in robust […]


Continue.. Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality

Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality

Kavli Affiliate: Yi Zhou | First 5 Authors: Shaocong Ma, Shaocong Ma, , , | Summary: The goal of robust constrained reinforcement learning (RL) is to optimize an agent’s performance under the worst-case model uncertainty while satisfying safety or resource constraints. In this paper, we demonstrate that strong duality does not generally hold in robust […]


Continue.. Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality

Neural representation of action symbols in primate frontal cortex

Kavli Affiliate: Winrich Freiwald | Authors: Lucas Y Tian, Kedar U Garzón, Daniel J. Hanuska, Adam G Rouse, Mark AG Eldridge, Marc H Schieber, Xiao-Jing Wang, Joshua B Tenenbaum and Winrich A Freiwald | Summary: At the core of intelligence lies proficiency in solving new problems, including those that differ dramatically from problems seen before. […]


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PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs

Kavli Affiliate: Xiang Zhang | First 5 Authors: Zhilin Zhang, Zhilin Zhang, , , | Summary: Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In this work, we apply this paradigm to the paper-to-poster generation problem, a practical yet time-consuming process faced by researchers preparing for […]


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Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

Kavli Affiliate: Adam S. Charles | Authors: Kyle A. Johnsen, Nathanael A. Cruzado, Zachary C. Menard, Adam A. Willats, Adam S. Charles, Jeffrey E. Markowitz and Christopher J. Rozell | Summary: Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop […]


Continue.. Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

3D map-guided modeling of functional endometrial tissue using multi-compartment assembloids

Kavli Affiliate: Denis Wirtz | KEHAN REN, VICTORIA DUARTE, XIN DI ZHOU, BRYAN HO, ANDRE FORJAZ, ASHLEIGH CRAWFORD, GRETCHEN M ALICEA-REBECCA, SAURABH JOSHI, PRAFUL R NAIR, EBAN A HANNA and DENIS WIRTZ | Summary: The human endometrium is a dynamic tissue that lines the uterus and undergoes constant remodeling, making it especially susceptible to gynecological […]


Continue.. 3D map-guided modeling of functional endometrial tissue using multi-compartment assembloids