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Model complexity of deep learning: A survey
Abstract Model complexity is a fundamental problem in deep learning. In this paper, we
conduct a systematic overview of the latest studies on model complexity in deep learning …
conduct a systematic overview of the latest studies on model complexity in deep learning …
Normalization techniques in training dnns: Methodology, analysis and application
Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …
generalization of deep neural networks (DNNs), and have successfully been used in various …
The power of quantum neural networks
It is unknown whether near-term quantum computers are advantageous for machine
learning tasks. In this work we address this question by trying to understand how powerful …
learning tasks. In this work we address this question by trying to understand how powerful …
Deep learning: a statistical viewpoint
The remarkable practical success of deep learning has revealed some major surprises from
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …
What makes multi-modal learning better than single (provably)
The world provides us with data of multiple modalities. Intuitively, models fusing data from
different modalities outperform their uni-modal counterparts, since more information is …
different modalities outperform their uni-modal counterparts, since more information is …
Benign overfitting in linear regression
The phenomenon of benign overfitting is one of the key mysteries uncovered by deep
learning methodology: deep neural networks seem to predict well, even with a perfect fit to …
learning methodology: deep neural networks seem to predict well, even with a perfect fit to …
Fantastic generalization measures and where to find them
Generalization of deep networks has been of great interest in recent years, resulting in a
number of theoretically and empirically motivated complexity measures. However, most …
number of theoretically and empirically motivated complexity measures. However, most …
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
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 …
generalize despite being very overparametrized. This paper analyzes training and …
The modern mathematics of deep learning
We describe the new field of the mathematical analysis of deep learning. This field emerged
around a list of research questions that were not answered within the classical framework of …
around a list of research questions that were not answered within the classical framework of …
On the implicit bias in deep-learning algorithms
G Vardi - Communications of the ACM, 2023 - dl.acm.org
On the Implicit Bias in Deep-Learning Algorithms Page 1 DEEP LEARNING HAS been highly
successful in recent years and has led to dramatic improvements in multiple domains …
successful in recent years and has led to dramatic improvements in multiple domains …