Add to ORKGIn the past years, deep learning models have been successfully applied in several cognitive tasks. Originally inspired by neuroscience, these models are specific examples of differentiable programs. In this paper we define and motivate differentiable programming, as well as specify some program characteristics that allow us to incorporate the structure of the problem in a differentiable program. We analyze different types of differentiable programs, from more general to more specific, and evaluate, for a specific problem with a graph dataset, its structure and knowledge with several differentiable programs using those characteristics. Finally, we discuss some inherent limitations of deep learning and differentiable programs, which are key c...
© 2021 Owner/Author. Deep learning is moving towards increasingly sophisticated optimization objecti...
Deep reinforcement learning has gathered much attention recently. Impressive results were achieved i...
Deep neural networks perform exceptionally well on various learning tasks with state-of-the-art resu...
Classic algorithms and machine learning systems like neural networks are both abundant in everyday l...
We study the problem of learning differentiable functions expressed as programs in a domain-specific...
Copyright © 2020, for this paper by its authors. Scientific computing is increasingly incorporating ...
This thesis investigates how general the knowledge stored in deep-Q-networks are. This general knowl...
Classical algorithms typically contain domain-specific insights. This makes them often more robust, ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
Despite the impressive performance of Deep Neural Networks (DNNs), they usually lack the explanatory...
Deep learning is a form of machine learning that enables computers to learn from experience and unde...
There are families of neural networks that can learn to compute any function, provided sufficient tr...
This thesis introduces 'Duality Between Deep Learning And Algorithm Design'. Deep learning is a data...
In the modern computer world, machine learning is one of the fields. There has been a lot of work un...
This book covers both classical and modern models in deep learning. The primary focus is on the theo...
© 2021 Owner/Author. Deep learning is moving towards increasingly sophisticated optimization objecti...
Deep reinforcement learning has gathered much attention recently. Impressive results were achieved i...
Deep neural networks perform exceptionally well on various learning tasks with state-of-the-art resu...
Classic algorithms and machine learning systems like neural networks are both abundant in everyday l...
We study the problem of learning differentiable functions expressed as programs in a domain-specific...
Copyright © 2020, for this paper by its authors. Scientific computing is increasingly incorporating ...
This thesis investigates how general the knowledge stored in deep-Q-networks are. This general knowl...
Classical algorithms typically contain domain-specific insights. This makes them often more robust, ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
Despite the impressive performance of Deep Neural Networks (DNNs), they usually lack the explanatory...
Deep learning is a form of machine learning that enables computers to learn from experience and unde...
There are families of neural networks that can learn to compute any function, provided sufficient tr...
This thesis introduces 'Duality Between Deep Learning And Algorithm Design'. Deep learning is a data...
In the modern computer world, machine learning is one of the fields. There has been a lot of work un...
This book covers both classical and modern models in deep learning. The primary focus is on the theo...
© 2021 Owner/Author. Deep learning is moving towards increasingly sophisticated optimization objecti...
Deep reinforcement learning has gathered much attention recently. Impressive results were achieved i...
Deep neural networks perform exceptionally well on various learning tasks with state-of-the-art resu...