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Found 8 papers, 4 papers with code
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning
Deep representation learning methods struggle with continual learning, suffering from both catastrophic forgetting of useful units and loss of plasticity, often due to rigid and unuseful units.
Non-Parallel Training Approach for Emotional Voice Conversion Using CycleGAN
The focus of this research is proposing a nonparallel emotional voice conversion for Egyptian Arabic speech.
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning
Modern representation learning methods often struggle to adapt quickly under non-stationarity because they suffer from catastrophic forgetting and decaying plasticity.
HesScale: Scalable Computation of Hessian Diagonals
Second-order optimization uses curvature information about the objective function, which can help in faster convergence.
Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems
In contrast to the state-of-the-art NNs that employ dense or recurrent layers for SI modeling, the proposed NNs exploit, in a novel manner, a combination of different hidden layers (e. g., convolutional, recurrent, and/or dense) in order to model the SI with lower computational complexity than the polynomial and the state-of-the-art NN-based cancelers.
Autonomous object harvesting using synchronized optoelectronic microrobots
Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the use of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
Low Complexity Neural Network Structures for Self-Interference Cancellation in Full-Duplex Radio
The core idea of these two structures is to mimic the non-linearity and memory effect introduced to the SI signal in order to achieve proper SI cancellation while exhibiting low computational complexity.
