The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image
classification. It is a simple residual network that alternates (i) a linear layer in which image …

Recent works have cast some light on the mystery of why deep nets fit any data and
generalize despite being very overparametrized. This paper analyzes training and …

Deep learning is usually described as an experiment-driven field under continuous criticizes
of lacking theoretical foundations. This problem has been partially fixed by a large volume of …

Tomato leaf disease seriously affects the yield of tomato. It is extremely vital for agricultural
economy to identify agricultural diseases. The traditional data augmentation methods, such …

Abstract Machine learning modeling techniques have emerged as a potential means for
predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river …

In this paper, we provide a theoretical analysis of the inductive biases in convolutional
neural networks (CNNs). We start by examining the universality of CNNs, ie, the ability to …

Deep learning models need sufficient training samples to support them in the training
process; otherwise, overfitting occurs, resulting in model failure. However, in the field of …

In practice, encoding invariances into models improves sample complexity. In this work, we
study this phenomenon from a theoretical perspective. In particular, we provide minimax …

We prove bounds on the generalization error of convolutional networks. The bounds are in
terms of the training loss, the number of parameters, the Lipschitz constant of the loss and …