534 citations · 660 across the 6 of their papers we have counts for
7 papers
Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity
Qiao Xiao, Boqian Wu, Andrey Poddubnyy +4
Federated learning (FL) enables collaborative model training across decentralized clients while preserving data privacy, leveraging aggregated updates to build robust global models…
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
Debdeep Sanyal, Agniva Maiti, Umakanta Maharana +4
Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teac…
Sparse Training Theory for Scalable and Efficient Agents
Decebal Constantin Mocanu, Elena Mocanu, Tiago Pinto +5
A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinf…
On-line Building Energy Optimization using Deep Reinforcement Learning
Elena Mocanu, Decebal Constantin Mocanu, Phuong H. Nguyen +4
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the futur…
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone +3
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the n…
Energy Disaggregation for Real-Time Building Flexibility Detection
Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the sm…
A topological insight into restricted Boltzmann machines
Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen +2
Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features ext…