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Published in 2021 at "Nature Communications"
DOI: 10.1038/s41467-021-22364-0
Abstract: Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networks…
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Keywords:
high dimensional;
neural networks;
augmented neural;
explicit memory ... See more keywords
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Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2023.3257707
Abstract: We present a differentiable pipeline for simulating the motion of objects that represent their geometry as a continuous density field parameterized as a deep network. This includes Neural Radiance Fields (NeRFs), and other related models.…
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Keywords:
neural objects;
density field;
physics;
dynamics augmented ... See more keywords
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Published in 2022 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2021.3132063
Abstract: In-Memory Computing (IMC) has been widely studied to mitigate data transfer bottlenecks in von Neumann architectures. Recently proposed IMC circuit topologies dramatically reduce data transfer requirements by performing various operations such as Multiply-Accumulate (MAC) inside…
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Keywords:
neural network;
augmented neural;
memory augmented;
sram macro ... See more keywords