Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Mocanu, Elena"'
Autor:
Wu, Boqian, Xiao, Qiao, Wang, Shunxin, Strisciuglio, Nicola, Pechenizkiy, Mykola, van Keulen, Maurice, Mocanu, Decebal Constantin, Mocanu, Elena
It is generally perceived that Dynamic Sparse Training opens the door to a new era of scalability and efficiency for artificial neural networks at, perhaps, some costs in accuracy performance for the classification task. At the same time, Dense Train
Externí odkaz:
http://arxiv.org/abs/2410.03030
Autor:
Wu, Boqian, Xiao, Qiao, Liu, Shiwei, Yin, Lu, Pechenizkiy, Mykola, Mocanu, Decebal Constantin, Van Keulen, Maurice, Mocanu, Elena
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 computation cost of deep neural networks have become the primary barrier to d
Externí odkaz:
http://arxiv.org/abs/2312.04727
Autor:
Grooten, Bram, Sokar, Ghada, Dohare, Shibhansh, Mocanu, Elena, Taylor, Matthew E., Pechenizkiy, Mykola, Mocanu, Decebal Constantin
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 information about the home, but needs to focus on just a small subset to s
Externí odkaz:
http://arxiv.org/abs/2302.06548
Autor:
Xiao, Qiao, Wu, Boqian, Zhang, Yu, Liu, Shiwei, Pechenizkiy, Mykola, Mocanu, Elena, Mocanu, Decebal Constantin
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, the variation of signal scales across and within time series data, mak
Externí odkaz:
http://arxiv.org/abs/2212.09840
Autor:
Liu, Shiwei, Chen, Tianlong, Atashgahi, Zahra, Chen, Xiaohan, Sokar, Ghada, Mocanu, Elena, Pechenizkiy, Mykola, Wang, Zhangyang, Mocanu, Decebal Constantin
Publikováno v:
Proceedings of the International Conference on Machine Learning (ICLR 2022)
The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning literature. Albeit the promising results, naively training multiple dee
Externí odkaz:
http://arxiv.org/abs/2106.14568
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 achieve good performance. Hence, prohibitive computation and memory resour
Externí odkaz:
http://arxiv.org/abs/2106.04217
Autor:
Mocanu, Decebal Constantin, Mocanu, Elena, Pinto, Tiago, Curci, Selima, Nguyen, Phuong H., Gibescu, Madeleine, Ernst, Damien, Vale, Zita A.
Publikováno v:
20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)
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 reinforcement learning. Nevertheless, traditional deep learning approaches
Externí odkaz:
http://arxiv.org/abs/2103.01636
Autor:
Atashgahi, Zahra, Sokar, Ghada, van der Lee, Tim, Mocanu, Elena, Mocanu, Decebal Constantin, Veldhuis, Raymond, Pechenizkiy, Mykola
Major complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements. Feature selection, which identifies the most relevant and informative attributes of a dataset, has
Externí odkaz:
http://arxiv.org/abs/2012.00560
Publikováno v:
17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018)
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 solve this problem, the one-shot learning paradigm, which makes use of jus
Externí odkaz:
http://arxiv.org/abs/1804.07645
Autor:
Mocanu, Elena, Mocanu, Decebal Constantin, Nguyen, Phuong H., Liotta, Antonio, Webber, Michael E., Gibescu, Madeleine, Slootweg, J. G.
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 future power system, and to help the customers transition from a passive to
Externí odkaz:
http://arxiv.org/abs/1707.05878