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Found 12 papers, 7 papers with code
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
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.
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Tomorrow's robots will need to distinguish useful information from noise when performing different tasks.
Dynamic Sparse Network for Time Series Classification: Learning What to "see''
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).
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Our framework, FreeTickets, is defined as the ensemble of these relatively cheap sparse subnetworks.
Dynamic Sparse Training for Deep Reinforcement Learning
In this paper, we introduce for the first time a dynamic sparse training approach for deep reinforcement learning to accelerate the training process.
Sparse Training Theory for Scalable and Efficient Agents
A fundamental task for artificial intelligence is learning.
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
This method, named QuickSelection, introduces the strength of the neuron in sparse neural networks as a criterion to measure the feature importance.
One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach
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.
On-line Building Energy Optimization using Deep Reinforcement Learning
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure.
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
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
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.
A topological insight into restricted Boltzmann machines
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.
