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
Adding One Neuron Can Eliminate All Bad Local Minima
The Universal Approximation Theorem for neural networks
Echo State Networks By Geoffrey Hinton.
TDLS: Principles of Riemannian Geometry in Neural Networks.