534 citations · 699 across the 11 of their papers we have counts for
12 papers
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
Boqian Wu, Qiao Xiao, Shiwei Liu +5
Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computa…
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Bram Grooten, Ghada Sokar, Shibhansh Dohare +4
Tomorrow's robots will need to distinguish useful information from noise when performing different tasks. A household robot for instance may continuously receive a plethora of info…
Dynamic Sparse Network for Time Series Classification: Learning What to "see''
Qiao Xiao, Boqian Wu, Yu Zhang +4
The receptive field (RF), which determines the region of time series to be ``seen'' and used, is critical to improve the performance for time series classification (TSC). However,…
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Shiwei Liu, Tianlong Chen, Zahra Atashgahi +6
The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning l…
Dynamic Sparse Training for Deep Reinforcement Learning
Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu +2
Deep reinforcement learning (DRL) agents are trained through trial-and-error interactions with the environment. This leads to a long training time for dense neural networks to achi…
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…
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
Zahra Atashgahi, Ghada Sokar, Tim van der Lee +4
Major complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements. Feature selection, which iden…
One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach
Decebal Constantin Mocanu, Elena Mocanu
Deep learning, even if it is very successful nowadays, traditionally needs very large amounts of labeled data to perform excellent on the classification task. In an attempt to solv…
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…