The Universal Approximation Theorem for neural networks
Dec. 23, 2017, 9:33 p.m.
By:
Kirti Bakshi
When it comes to the theory of artificial neural networks in mathematical terms, the universal approximation theorem brings forward ...
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
March 19, 2018, 8:43 p.m.
By:
Kirti Bakshi
In order to answer a Visual question, it requires high-order reasoning about an image, which to follow complex directives ...
Adding One Neuron Can Eliminate All Bad Local Minima
May 29, 2018, 8:19 a.m.
By:
Kirti Bakshi
One of the main difficulties in analyzing neural networks is the non-convexity of the loss function which may have ...
Echo State Networks By Geoffrey Hinton.
Aug. 14, 2018, 11:27 p.m.
By:
Kirti Bakshi
In this course, created by The University Of Toronto, you get to learn about Artificial Neural Networks ...
TDLS: Principles of Riemannian Geometry in Neural Networks.
Aug. 20, 2018, 10:43 p.m.
By:
Kirti Bakshi
The study presented in this paper mainly deals with neural networks in the sense of geometric transformations acting on ...
DeepMind Presents GraphNets: A Library for Graph Networks in Tensorflow And Sonnet.
Oct. 24, 2018, 9:38 p.m.
By:
Kirti Bakshi
Neural networks that operate on graphs, and structure their computations in accordance to the same, have been developed ...
Early-Bird: Toward more efficient training of Deep Neural Networks
May 26, 2020, 9:16 a.m.
By:
Merlyn Shelley
Houston based Rice University's Engineers have come up with a lucrative solution that could slash off the energy consumption ...
Latest Stories
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
Adding One Neuron Can Eliminate All Bad Local Minima
Echo State Networks By Geoffrey Hinton.
TDLS: Principles of Riemannian Geometry in Neural Networks.