Context-aware feature query to improve the prediction performance. - Mohammad Kachuee; Anahita Hosseini; Babak Moatamed; Sajad Darabi; Majid Sarrafzadeh (GlobalSIP, 2017)

Complex Event Processing of Health Data in Real-time to Predict Heart Failure Risk and Stress. - Sandeep Singh Sandha; Mohammad Kachuee; Sajad Darabi (CoRR, 2017)

BNN+: Improved Binary Network Training. - Sajad Darabi; Mouloud Belbahri; Matthieu Courbariaux; Vahid Partovi Nia (CoRR, 2018)

Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks. - Mouloud Belbahri; Eyyüb Sari; Sajad Darabi; Vahid Partovi Nia (ICIAR (2), 2019)

Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams. - Mohammad Kachuee; Orpaz Goldstein; Kimmo Kärkkäinen; Sajad Darabi; Majid Sarrafzadeh (ICLR (Poster), 2019)

Foothill: A Quasiconvex Regularization Function. - Mouloud Belbahri; Eyyüb Sari; Sajad Darabi; Vahid Partovi Nia (CoRR, 2019)

Unsupervised Representation for EHR Signals and Codes as Patient Status Vector. - Sajad Darabi; Mohammad Kachuee; Majid Sarrafzadeh (CoRR, 2019)

Group-Connected Multilayer Perceptron Networks. - Mohammad Kachuee; Sajad Darabi; Shayan Fazeli; Majid Sarrafzadeh (CoRR, 2019)

Dynamic Feature Acquisition Using Denoising Autoencoders. - Mohammad Kachuee; Sajad Darabi; Babak Moatamed; Majid Sarrafzadeh (IEEE Trans. Neural Networks Learn. Syst., 2019)

TAPER: Time-Aware Patient EHR Representation. - Sajad Darabi; Mohammad Kachuee; Shayan Fazeli; Majid Sarrafzadeh (IEEE J. Biomed. Health Informatics, 2020)

Synthesising Multi-Modal Minority Samples for Tabular Data. - Sajad Darabi; Yotam Elor (CoRR, 2021)

Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain. - Sajad Darabi; Shayan Fazeli; Ali Pazoki; Sriram Sankararaman; Majid Sarrafzadeh (CoRR, 2021)

Deep Unsupervised & Semi-supervised Methods for Health Applications. - Sajad Darabi (2021)

A Framework for Large Scale Synthetic Graph Dataset Generation. - Sajad Darabi; Piotr Bigaj; Dawid Majchrowski; Pawel M. Morkisz; Alex Fit-Florea (CoRR, 2022)

Heterogenous Ensemble of Models for Molecular Property Prediction. - Sajad Darabi; Shayan Fazeli; Jiwei Liu; Alexandre Milesi; Pawel M. Morkisz; Jean-François Puget; Gilberto Titericz (CoRR, 2022)

Generative Imputation and Stochastic Prediction. - Mohammad Kachuee; Kimmo Kärkkäinen; Orpaz Goldstein; Sajad Darabi; Majid Sarrafzadeh (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

TSPP: A Unified Benchmarking Tool for Time-series Forecasting. - Jan Baczek; Dmytro Zhylko; Gilberto Titericz; Sajad Darabi; Jean-Francois Puget; Izzy Putterman; Dawid Majchrowski; Anmol Gupta; Kyle Kranen; Pawel M. Morkisz (CoRR, 2023)

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery. - Peter St. John; Dejun Lin; Polina Binder; Malcolm Greaves; Vega Shah; John St. John; Adrian Lange; Patrick D. Hsu; Rajesh Illango; Arvind Ramanathan; Anima Anandkumar; David H. Brookes; et al. (CoRR, 2024)

A Framework for Large-Scale Synthetic Graph Dataset Generation. - Sajad Darabi; Piotr Bigaj; Dawid Majchrowski; Artur Kasymov; Pawel M. Morkisz; Alex Fit-Florea (IEEE Trans. Neural Networks Learn. Syst., 2025)