Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
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However, deploying TinyML models on MCUs reveals several challenges due to the MCU's resource constraints, such as small flash memory, tight
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Deep Reinforcement Learning with Plasticity Injection
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The applications of this intervention are two-fold: first, as a diagnostic tool -- if injection increases the performance, we may conclude that an agent's network was losing its plasticity.
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Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
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CaST, which consistently outperforms existing methods with good interpretability.
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Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
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In this work, we aim to address the problem of DP for transfer learning, i.e., how to
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