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20172024
most citedDynamic Feature Acquisition Using Denoising Autoencoders

25 citations · 76 across the 14 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.LG2019★ 1 cited

Group-Connected Multilayer Perceptron Networks

Mohammad Kachuee, Sajad Darabi, Shayan Fazeli +1

Despite the success of deep learning in domains such as image, voice, and graphs, there has been little progress in deep representation learning for domains without a known structu…

cs.LG2019★ 4 cited

Unsupervised Representation for EHR Signals and Codes as Patient Status Vector

Sajad Darabi, Mohammad Kachuee, Majid Sarrafzadeh

Effective modeling of electronic health records presents many challenges as they contain large amounts of irregularity most of which are due to the varying procedures and diagnosis…

cs.LG2019

TAPER: Time-Aware Patient EHR Representation

Sajad Darabi, Mohammad Kachuee, Shayan Fazeli +1

Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data…

cs.LG2019★ 1 cited

Generative Imputation and Stochastic Prediction

Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein +2

In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing…

cs.LG2019★ 11 cited

Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams

Mohammad Kachuee, Orpaz Goldstein, Kimmo Karkkainen +2

In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acqu…

stat.ML2019

Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks

Mouloud Belbahri, Eyyüb Sari, Sajad Darabi +1

Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their…