Acquisition and Algorithms for Fluorescence Applications. - Elena Mocanu; Paul E. Sterian (BIRD, 2008)

Inexpensive user tracking using Boltzmann Machines. - Elena Mocanu; Decebal Constantin Mocanu; Haitham Bou-Ammar; Zoran Zivkovic; Antonio Liotta; Evgueni N. Smirnov (SMC, 2014)

Comfort-constrained Demand Flexibility Management for Building Aggregations using a Decentralized Approach. - L. A. Hurtado Munoz; Elena Mocanu; Phuong H. Nguyen; Madeleine Gibescu; Wil L. Kling (SMARTGREENS, 2015)

Demand forecasting at low aggregation levels using Factored Conditional Restricted Boltzmann Machine. - Elena Mocanu; Phuong H. Nguyen; Madeleine Gibescu; Emil Mahler Larsen; Pierre Pinson (PSCC, 2016)

Big IoT data mining for real-time energy disaggregation in buildings. - Decebal Constantin Mocanu; Elena Mocanu; Phuong H. Nguyen; Madeleine Gibescu; Antonio Liotta (SMC, 2016)

Energy Disaggregation for Real-Time Building Flexibility Detection. - Elena Mocanu; Phuong H. Nguyen; Madeleine Gibescu (CoRR, 2016)

A topological insight into restricted Boltzmann machines. - Decebal Constantin Mocanu; Elena Mocanu; Phuong H. Nguyen; Madeleine Gibescu; Antonio Liotta (Mach. Learn., 2016)

Deep learning versus traditional machine learning methods for aggregated energy demand prediction. - Nikolaos G. Paterakis; Elena Mocanu; Madeleine Gibescu; Bart Stappers; Walter van Alst (ISGT Europe, 2017)

Evolutionary Training of Sparse Artificial Neural Networks: A Network Science Perspective. - Decebal Constantin Mocanu; Elena Mocanu; Peter Stone 001; Phuong H. Nguyen; Madeleine Gibescu; Antonio Liotta (CoRR, 2017)

One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach. - Decebal Constantin Mocanu; Elena Mocanu (AAMAS, 2018)

Enabling Cooperative Behavior for Building Demand Response Based on Extended Joint Action Learning. - L. A. Hurtado Munoz; Elena Mocanu; Phuong H. Nguyen; Madeleine Gibescu; I. G. Kamphuis (IEEE Trans. Ind. Informatics, 2018)

Airport Restroom Cleanliness Prediction Using Real Time User Feedback Data. - Kilian Ros; Elena Mocanu; Christin Seifert (CIC, 2019)

Collaborative learning for classification and prediction of building energy flexibility. - Anil Kumar; Elena Mocanu; Muhammad Babar; Phuong H. Nguyen (ISGT Europe, 2019)

On-Line Building Energy Optimization Using Deep Reinforcement Learning. - Elena Mocanu; Decebal Constantin Mocanu; Phuong H. Nguyen; Antonio Liotta; Michael E. Webber; Madeleine Gibescu; Johannes G. Slootweg (IEEE Trans. Smart Grid, 2019)

Effectiveness of neural language models for word prediction of textual mammography reports. - Mihai David Marin; Elena Mocanu; Christin Seifert (SMC, 2020)

Sparse Training Theory for Scalable and Efficient Agents. - Decebal Constantin Mocanu; Elena Mocanu; Tiago Pinto; Selima Curci; Phuong H. Nguyen; Madeleine Gibescu; Damien Ernst; Zita A. Vale (AAMAS, 2021)

FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Zahra Atashgahi; Xiaohan Chen; Ghada Sokar; Elena Mocanu; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (CoRR, 2021)

Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Zahra Atashgahi; Xiaohan Chen; Ghada Sokar; Elena Mocanu; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (ICLR, 2022)

Dynamic Sparse Training for Deep Reinforcement Learning. - Ghada Sokar; Elena Mocanu; Decebal Constantin Mocanu; Mykola Pechenizkiy; Peter Stone 001 (IJCAI, 2022)

Dynamic Sparse Network for Time Series Classification: Learning What to "See". - Qiao Xiao; Boqian Wu; Yu Zhang 006; Shiwei Liu 003; Mykola Pechenizkiy; Elena Mocanu; Decebal Constantin Mocanu (NeurIPS, 2022)

Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders. - Zahra Atashgahi; Ghada Sokar; Tim van der Lee; Elena Mocanu; Decebal Constantin Mocanu; Raymond N. J. Veldhuis; Mykola Pechenizkiy (Mach. Learn., 2022)

Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. - Bram Grooten; Ghada Sokar; Shibhansh Dohare; Elena Mocanu; Matthew E. Taylor; Mykola Pechenizkiy; Decebal Constantin Mocanu (AAMAS, 2023)

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. - Boqian Wu; Qiao Xiao; Shiwei Liu 003; Lu Yin 006; Mykola Pechenizkiy; Decebal Constantin Mocanu; Maurice van Keulen; Elena Mocanu (NeurIPS, 2024)

Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness. - Boqian Wu; Qiao Xiao; Shunxin Wang; Nicola Strisciuglio; Mykola Pechenizkiy; Maurice van Keulen; Decebal Constantin Mocanu; Elena Mocanu (ICLR, 2025)

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling. - Bram Grooten; Farid Hasanov; Chenxiang Zhang; Qiao Xiao; Boqian Wu; Zahra Atashgahi; Ghada Sokar; Shiwei Liu 003; Lu Yin 006; Elena Mocanu; Mykola Pechenizkiy; Decebal Constantin Mocanu (CoRR, 2025)

Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity. - Qiao Xiao; Boqian Wu; Andrey Poddubnyy; Elena Mocanu; Phuong H. Nguyen; Mykola Pechenizkiy; Decebal Constantin Mocanu (CoRR, 2025)