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Optimal Setting of Weights, Learning Rate, and Gain

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Optimal Setting of Weights, Learning Rate, and Gain

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Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Disentangling feature and lazy training in deep neural networks

Mixed-Precision Deep Learning Based on Computational Memory

Scaling description of generalization with number of parameters in deep learning

On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Real-time, low-latency closed-loop feedback using markerless posture tracking

Real-time, low-latency closed-loop feedback using markerless posture tracking

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

In Situ Calibration of Low-cost Air Quality Sensors using Artificial Neural Network

Deep(ly) Learning the Depth

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Disentangling feature and lazy training in deep neural networks

Mixed-Precision Deep Learning Based on Computational Memory

Real-time, low-latency closed-loop feedback using markerless posture tracking

On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Real-time, low-latency closed-loop feedback using markerless posture tracking

In Situ Calibration of Low-cost Air Quality Sensors using Artificial Neural Network

Deep(ly) Learning the Depth

Scaling description of generalization with number of parameters in deep learning