Factorized dynamic fully-connected layers for neural networks
The design of neural network layers plays a crucial role in determining the efficiency and
performance of various computer vision tasks. However, most existing layers compromise …
performance of various computer vision tasks. However, most existing layers compromise …
[PDF][PDF] Machine learning, linear algebra, and more: Is SQL all you need?
ABSTRACT SQL is the standard language for retrieving and manipulating relational data.
Although SQL is ubiquitous for simple analytical queries, it is rarely used for more complex …
Although SQL is ubiquitous for simple analytical queries, it is rarely used for more complex …
Tensor relational algebra for distributed machine learning system design
We consider the question: what is the abstraction that should be implemented by the
computational engine of a machine learning system? Current machine learning systems …
computational engine of a machine learning system? Current machine learning systems …
The EDGE language: Extended general einsums for graph algorithms
In this work, we propose a unified abstraction for graph algorithms: the Extended General
Einsums language, or EDGE. The EDGE language expresses graph algorithms in the …
Einsums language, or EDGE. The EDGE language expresses graph algorithms in the …
A probabilistic reformulation technique for discrete RIS optimization in wireless systems
A Pradhan, HS Dhillon - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
The use of reconfigurable intelligent surfaces (RIS) can improve wireless communication by
modifying the wireless link to create virtual line-of-sight links, bypass blockages, suppress …
modifying the wireless link to create virtual line-of-sight links, bypass blockages, suppress …
Roll of Newtonian and Non‐Newtonian Motion in Analysis of Two‐Phase Hepatic Blood Flow in Artery during Jaundice
A Singh, RA Khan, S Kushwaha… - International Journal of …, 2022 - Wiley Online Library
Biomathematics is an interdisciplinary subject consisting of mathematics and biology, which
is widely applicable for the analysis of biological problems. In this paper, we provide a …
is widely applicable for the analysis of biological problems. In this paper, we provide a …
Adaptive Spiral Layers for Efficient 3D Representation Learning on Meshes
F Babiloni, M Maggioni, T Tanay… - Proceedings of the …, 2023 - openaccess.thecvf.com
The success of deep learning models on structured data has generated significant interest in
extending their application to non-Euclidean domains. In this work, we introduce a novel …
extending their application to non-Euclidean domains. In this work, we introduce a novel …
Why capsule neural networks do not scale: Challenging the dynamic parse-tree assumption
Capsule neural networks replace simple, scalar-valued neurons with vector-valued
capsules. They are motivated by the pattern recognition system in the human brain, where …
capsules. They are motivated by the pattern recognition system in the human brain, where …
Partial information decomposition via deficiency for multivariate gaussians
P Venkatesh, G Schamberg - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Bivariate partial information decompositions (PIDs) characterize how the information in a"
message" random variable is decomposed between two" constituent" random variables in …
message" random variable is decomposed between two" constituent" random variables in …
GENO--GENeric Optimization for Classical Machine Learning
S Laue, M Mitterreiter, J Giesen - Advances in Neural …, 2019 - proceedings.neurips.cc
Although optimization is the longstanding, algorithmic backbone of machine learning new
models still require the time-consuming implementation of new solvers. As a result, there are …
models still require the time-consuming implementation of new solvers. As a result, there are …