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Found 7 papers, 2 papers with code
Neural Time-Reversed Generalized Riccati Equation
This paper introduces a novel neural-based approach to optimal control, with the aim of working forward-in-time.
Continual Learning with Pretrained Backbones by Tuning in the Input Space
The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks.
PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks
nature of the streamed data with samples that are smoothly evolving over time for efficient gradient computations.
Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams
Our experiments leverage 3D virtual environments and they show that the proposed agents can learn to distinguish objects just by observing the video stream.
Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments
However, in order to devise continual learning algorithms that operate in more realistic conditions, it is fundamental to gain access to rich, fully customizable and controlled experimental playgrounds.
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier
In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective.
Developing Constrained Neural Units Over Time
In this paper we present a foundational study on a constrained method that defines learning problems with Neural Networks in the context of the principle of least cognitive action, which very much resembles the principle of least action in mechanics.
