3 citations · 6 across the 6 of their papers we have counts for
6 papers
Newman's theorem via Carathéodory
Yaqiao Li, Ali Mohammad Lavasani, Mehran Shakerinava
We give a streamlined short proof of Newman's theorem in communication complexity by applying the classical and the approximate Carathéodory's theorems.
Weight-Sharing Regularization
Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh +1
Weight-sharing is ubiquitous in deep learning. Motivated by this, we propose a "weight-sharing regularization" penalty on the weights of a neural network, defi…
Utility Theory for Sequential Decision Making
Mehran Shakerinava, Siamak Ravanbakhsh
The von Neumann-Morgenstern (VNM) utility theorem shows that under certain axioms of rationality, decision-making is reduced to maximizing the expectation of some utility function.…
Transformation Coding: Simple Objectives for Equivariant Representations
Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh
We present a simple non-generative approach to deep representation learning that seeks equivariant deep embedding through simple objectives. In contrast to existing equivariant net…
Equivariant Networks for Pixelized Spheres
Mehran Shakerinava, Siamak Ravanbakhsh
Pixelizations of Platonic solids such as the cube and icosahedron have been widely used to represent spherical data, from climate records to Cosmic Microwave Background maps. Plato…
A Survey on Recent Hardware Data Prefetching Approaches with An Emphasis on Servers
Mohammad Bakhshalipour, Mehran Shakerinava, Fatemeh Golshan +3
Data prefetching, i.e., the act of predicting application's future memory accesses and fetching those that are not in the on-chip caches, is a well-known and widely-used approach t…