Search Results for author: Elena Mocanu

Found 12 papers, 7 papers with code

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

1 code implementation7 Dec 2023 Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu

E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method.

Brain Tumor Segmentation Image Segmentation +2

Dynamic Sparse Network for Time Series Classification: Learning What to "see''

1 code implementation19 Dec 2022 Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu

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).

Time Series Time Series Analysis +1

Dynamic Sparse Training for Deep Reinforcement Learning

1 code implementation8 Jun 2021 Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, Peter Stone

In this paper, we introduce for the first time a dynamic sparse training approach for deep reinforcement learning to accelerate the training process.

Continuous Control Decision Making +3

One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach

no code implementations18 Apr 2018 Decebal Constantin Mocanu, Elena Mocanu

In an attempt to solve this problem, the one-shot learning paradigm, which makes use of just one labeled sample per class and prior knowledge, becomes increasingly important.

Classification General Classification +1

Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science

2 code implementations15 Jul 2017 Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods.

Energy Disaggregation for Real-Time Building Flexibility Detection

no code implementations6 May 2016 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.

energy management Management

A topological insight into restricted Boltzmann machines

no code implementations20 Apr 2016 Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta

Thirdly, we show that, for a fixed number of weights, our proposed sparse models (which by design have a higher number of hidden neurons) achieve better generative capabilities than standard fully connected RBMs and GRBMs (which by design have a smaller number of hidden neurons), at no additional computational costs.

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