163 research outputs found
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
ΠΡΠ°ΠΌΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΈ ΠΊΠ°ΠΊ ΠΎΡΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ Π΄Π²ΡΡ ΡΡΠΎΡΠΎΠ½ ΡΠ·ΡΠΊΠΎΠ²ΠΎΠ³ΠΎ Π·Π½Π°ΠΊΠ°
Π‘ΡΠ°ΡΡΡ ΠΈΠ· ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π²ΡΠΏΡΡΠΊΠ° Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΠΆΡΡΠ½Π°Π»Π° "ΠΡΠ»ΡΡΡΡΠ° Π½Π°ΡΠΎΠ΄ΠΎΠ² ΠΡΠΈΡΠ΅ΡΠ½ΠΎΠΌΠΎΡΡΡ", ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½Ρ ΠΎΠ±ΡΠ΅ΠΉ ΡΠ΅ΠΌΠΎΠΉ "Π―Π·ΡΠΊ ΠΈ ΠΠΈΡ" ΠΈ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Ρ ΠΎΠ±ΡΠΈΠΌ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ Π―Π·ΡΠΊΠΎΠ·Π½Π°Π½ΠΈΡ ΠΈ ΠΏΡΠΈΡΡΠΎΡΠ΅Π½Ρ ΠΊ 80-Π»Π΅ΡΠΈΡ ΡΠΎ Π΄Π½Ρ ΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΠΈΠΊΠΎΠ»Π°Ρ ΠΠ»Π΅ΠΊΡΠ°Π½Π΄ΡΠΎΠ²ΠΈΡΠ° Π ΡΠ΄ΡΠΊΠΎΠ²Π°.Π‘ΡΠ°ΡΡΡ ΠΈΠ· ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π²ΡΠΏΡΡΠΊΠ° Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΠΆΡΡΠ½Π°Π»Π° "ΠΡΠ»ΡΡΡΡΠ° Π½Π°ΡΠΎΠ΄ΠΎΠ² ΠΡΠΈΡΠ΅ΡΠ½ΠΎΠΌΠΎΡΡΡ", ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½Ρ ΠΎΠ±ΡΠ΅ΠΉ ΡΠ΅ΠΌΠΎΠΉ "Π―Π·ΡΠΊ ΠΈ ΠΠΈΡ" ΠΈ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Ρ ΠΎΠ±ΡΠΈΠΌ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ Π―Π·ΡΠΊΠΎΠ·Π½Π°Π½ΠΈΡ ΠΈ ΠΏΡΠΈΡΡΠΎΡΠ΅Π½Ρ ΠΊ 80-Π»Π΅ΡΠΈΡ ΡΠΎ Π΄Π½Ρ ΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΠΈΠΊΠΎΠ»Π°Ρ ΠΠ»Π΅ΠΊΡΠ°Π½Π΄ΡΠΎΠ²ΠΈΡΠ° Π ΡΠ΄ΡΠΊΠΎΠ²Π°
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