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Found 6 papers, 5 papers with code
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.
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds.
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.
An Optimal Control Approach to Learning in SIDARTHE Epidemic model
We formulate the problem in terms of a functional risk that depends on the learning variables through the solutions of a dynamic system.
SAILenv: Learning in Virtual Visual Environments Made Simple
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world.
