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pro vyhledávání: '"Stalder, Steven"'
Autor:
Stalder, Steven, Volpi, Michele, Büttner, Nicolas, Law, Stephen, Harttgen, Kenneth, Suel, Esra
Publikováno v:
Computers, Environment and Urban Systems 112 (2024) 102156
Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress in urban housing is limited. Deep learning-based computer vision meth
Externí odkaz:
http://arxiv.org/abs/2309.11354
Autor:
Russo, Stefania, Perraudin, Nathanaël, Stalder, Steven, Perez-Cruz, Fernando, Leitao, Joao Paulo, Obozinski, Guillaume, Wegner, Jan Dirk
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution. Efficient, accurate, and fast methods for water depth prediction are nowadays important as urban floods are increasin
Externí odkaz:
http://arxiv.org/abs/2302.10062
Autor:
Stalder, Steven, Perraudin, Nathanaël, Achanta, Radhakrishna, Perez-Cruz, Fernando, Volpi, Michele
Publikováno v:
Advances in Neural Information Processing Systems 35 (2022) 84-94
An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, doing this accurately is a difficult task due to the black-box nature o
Externí odkaz:
http://arxiv.org/abs/2205.11266
Autor:
Stalder, Steven, Volpi, Michele, Büttner, Nicolas, Law, Stephen, Harttgen, Kenneth, Suel, Esra
Publikováno v:
In Computers, Environment and Urban Systems September 2024 112
In deep reinforcement learning (RL), adversarial attacks can trick an agent into unwanted states and disrupt training. We propose a system called Robust Student-DQN (RS-DQN), which permits online robustness training alongside Q networks, while preser
Externí odkaz:
http://arxiv.org/abs/1911.00887