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Found 5 papers, 1 papers with code
An Introduction to Topological Data Analysis for Physicists: From LGM to FRBs
We close the article with a discussion of the resilience of topological data analysis to noise and some statistical and computational challenges faced by the method.
Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Theory
Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups
We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root.
Refining Architectures of Deep Convolutional Neural Networks
Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks.
Decision Forests, Convolutional Networks and the Models in-Between
We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.
Training CNNs with Low-Rank Filters for Efficient Image Classification
Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.
