Insights from Gradient Dynamics: Gradient Autoscaled Normalization
Gradient dynamics play a central role in determining the stability and generalization of deep neural networks. In this work, we provide an empirical analysis of how variance and standard deviation of gradients evolve during training, showing consistent changes across layers and at the global scale in convolutional networks.
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Regression models from portable NIR spectra for predicting the carcass traits and meat quality of beef cattle. [PDF]
Veloso Trópia N +9 more
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The Performance of Partial Least Squares Methods in Virtual Nanosensor Array-Multiple Metal Ions Sensing Based on Multispectral Fluorescence of Quantum Dots. [PDF]
Głowacz K +2 more
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Full Characterisation of Heroin Samples Using Infrared Spectroscopy and Multivariate Calibration. [PDF]
Deconinck E +6 more
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Piercing the Shadows: Exploring the Influence of Signal Preprocessing on Interpreting Ultrasensitive Bioelectronic Sensor Data. [PDF]
Caputo M +9 more
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Could the quality characteristics of virgin olive oil influence its geographical classification using untargeted infrared spectroscopy and chemometrics? [PDF]
De Angelis D +4 more
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Hypershape Recognition: A General Framework for Moment-Based Molecular Similarity. [PDF]
Costamagna M +3 more
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Interferon gamma induced-ACSL5 shapes the lipidome of kidney tubular cells. [PDF]
Poindessous V +8 more
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AI-based system for food and beverage selection towards precision nutrition in Indonesian restaurants. [PDF]
Seminar KB +9 more
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Colorectal Cancer Detection Through Sweat Volatilome Using an Electronic Nose System and GC-MS Analysis. [PDF]
Acevedo CMD +4 more
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