678 citations · 1.6k across the 28 of their papers we have counts for
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Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Antoine Wehenkel, Juan L. Gamella, Ozan Sener +4
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However,…
Simulation-based Inference for Cardiovascular Models
Antoine Wehenkel, Laura Manduchi, Jens Behrmann +6
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. While such tools are routinely u…
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
Umut Şimşekli, Ozan Sener, George Deligiannidis +1
Despite its success in a wide range of applications, characterizing the generalization properties of stochastic gradient descent (SGD) in non-convex deep learning problems is still…
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener, Silvio Savarese
Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset o…
Unsupervised Transductive Domain Adaptation
Ozan Sener, Hyun Oh Song, Ashutosh Saxena +1
Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still s…