The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

Deep model-based Reinforcement Learning (RL) has the potential to substantially improve the sample-efficiency of deep RL. While various challenges have long held it back, a number of papers have recently come out reporting success with deep model-based methods. This is a great development, but the lack of a consistent metric to evaluate such methods makes it difficult to compare various approaches. For example, the common single-task sample-efficiency metric conflates improvements due to model-based learning with various other aspects, such as representation learning, making it difficult to assess true progress on model-based RL. To address this, we introduce an experimental setup to evaluate model-based behavior of RL methods, inspired by work from neuroscience on detecting model-based behavior in humans and animals. Our metric based on this setup, the Local Change Adaptation (LoCA) regret, measures how quickly an RL method adapts to a local change in the environment. Our metric can identify model-based behavior, even if the method uses a poor representation and provides insight in how close a method's behavior is from optimal model-based behavior. We use our setup to evaluate the model-based behavior of MuZero on a variation of the classic Mountain Car task.Comment: NeurIPS 2020, code: https://github.com/chandar-lab/LoC

Ductal carcinoma in situ and invasive breast cancer: diagnostic accuracy and prognosis

The studies in this thesis contribute to more accurate risk assessment and prognosis prediction for DCIS and to better response evaluation of IBC treatment.For the Ductal Carcinoma In Situ (DCIS) studies, unbiased cohorts were used within the international Grand Challenge PRECISION consortium, funded by Cancer Research UK and KWF Dutch Cancer Society. DCIS is graded as low-, intermediate-, or high-grade depending on how abnormal the DCIS-cells look like. However, we showed that pathologists often disagree on grade. To overcome this limitation, we found that almost all DCIS scored as non-high-grade by the majority of pathologists express the estrogen receptor (ER) and are negative for the growth factor receptor HER2, whereas high-grade DCIS is mixed in expression for ER and HER2. We also provided insights in the recurrence risks of DCIS after treatment. See also https://cancergrandchallenges.org/teams/precision.The studies on Invasive Breast Cancer (IBC) were performed on a hospital-based cohort. We found for example substantial variation in tumour response evaluation for HER2-positive IBC after pre-operative chemotherapy due to different guidelines used. For accurate outcome analysis, reducing such variation is mandatory. Therefore, we are working on reaching international consensus of response evaluation. The Netherlands Cancer Institute-Antoni van Leeuwenhoek Ziekenhuis The Netherlands Comprehensive organisation (IKNL)LUMC / Geneeskund

Грамматические тенденции как отражение категориальной связи двух сторон языкового знака

Статья из специализированного выпуска научного журнала "Культура народов Причерноморья", материалы которого объединены общей темой "Язык и Мир" и посвящены общим вопросам Языкознания и приурочены к 80-летию со дня рождения Николая Александровича Рудякова.Статья из специализированного выпуска научного журнала "Культура народов Причерноморья", материалы которого объединены общей темой "Язык и Мир" и посвящены общим вопросам Языкознания и приурочены к 80-летию со дня рождения Николая Александровича Рудякова