Researchers have developed an uncertainty quantification-based framework for predicting degradation trends in proton exchange ...

Biologically plausible learning mechanisms have implications for understanding brain functions and engineering intelligent systems. Inspired by the multi-scale recurrent connectivity in the brain, we ...

Abstract: Optimization of deep neural networks (DNNs) has been driving modern advancements in artificial intelligence. With DNNs characterized by a prolonged sequence of nonlinear propagation, ...

new video loaded: I’m Building an Algorithm That Doesn’t Rot Your Brain transcript “Our brains are being melted by the algorithm.” [MUSIC PLAYING] “Attention is infrastructure.” “Those algorithms are ...

Your browser does not support the audio element. The backpropagation algorithm is the cornerstone of modern artificial intelligence. Its significance goes far beyond ...

ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...

Abstract: This study proposes theories and applications of probabilistic divergences to neural network training. This theory generalizes the cross-entropy method for backpropagation to the ...

ABSTRACT: The glycemic index (GI) is a qualitative indicator of the glycemic response of a carbohydrate food. Its variability is due to the composition of the food, which in turn is related to the ...