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of 2 981
pro vyhledávání: '"P. Josse"'
Inherently interpretable machine learning (IML) models provide valuable insights for clinical decision-making but face challenges when features have missing values. Classical solutions like imputation or excluding incomplete records are often unsuita
Externí odkaz:
http://arxiv.org/abs/2411.09591
Autor:
Yi, Xiaoling, Antonio, Ryan, Dumoulin, Joren, Sun, Jiacong, Van Delm, Josse, Paim, Guilherme, Verhelst, Marian
Deep neural networks (DNNs) face significant challenges when deployed on resource-constrained extreme edge devices due to their computational and data-intensive nature. While standalone accelerators tailored for specific application scenarios suffer
Externí odkaz:
http://arxiv.org/abs/2411.09543
We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-
Externí odkaz:
http://arxiv.org/abs/2410.16870
Randomized Controlled Trials (RCT) are the current gold standards to empirically measure the effect of a new drug. However, they may be of limited size and resorting to complementary non-randomized data, referred to as observational, is promising, as
Externí odkaz:
http://arxiv.org/abs/2410.12333
Autor:
Hamdi, Mohamed Amine, Daghero, Francesco, Sarda, Giuseppe Maria, Van Delm, Josse, Symons, Arne, Benini, Luca, Verhelst, Marian, Pagliari, Daniele Jahier, Burrello, Alessio
Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware accelerators for tensor computations, is becoming one of the crucial
Externí odkaz:
http://arxiv.org/abs/2410.08855
Autor:
Van Delm, Josse, Vandersteegen, Maarten, Burrello, Alessio, Sarda, Giuseppe Maria, Conti, Francesco, Pagliari, Daniele Jahier, Benini, Luca, Verhelst, Marian
Publikováno v:
2023 60th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2023, pp. 1-6
Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoCs makes deployment non-trivial, as they typically contain multiple het
Externí odkaz:
http://arxiv.org/abs/2406.07453
Predictive uncertainty quantification is crucial in decision-making problems. We investigate how to adequately quantify predictive uncertainty with missing covariates. A bottleneck is that missing values induce heteroskedasticity on the response's pr
Externí odkaz:
http://arxiv.org/abs/2405.15641
Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various fields. The present paper attempts to take a step back and provide a more s
Externí odkaz:
http://arxiv.org/abs/2403.19196
Autor:
Árnadóttir, Arnbjörg Soffía, de Bruyn, Josse van Dobben, Kar, Prem Nigam, Roberson, David E., Zeman, Peter
Sabidussi's theorem [Duke Math. J. 28, 1961] gives necessary and sufficient conditions under which the automorphism group of a lexicographic product of two graphs is a wreath product of the respective automorphism groups. We prove a quantum version o
Externí odkaz:
http://arxiv.org/abs/2402.12344
Conformal Inference (CI) is a popular approach for generating finite sample prediction intervals based on the output of any point prediction method when data are exchangeable. Adaptive Conformal Inference (ACI) algorithms extend CI to the case of seq
Externí odkaz:
http://arxiv.org/abs/2312.00448