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Found 11 papers, 3 papers with code
A Smartphone-based System for Real-time Early Childhood Caries Diagnosis
The identification of ECC in an early stage usually requires expertise in the field, and hence is often ignored by parents.
Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge Report
This technical report summarizes submissions and compiles from Actor-Action video classification challenge held as a final project in CSC 249/449 Machine Vision course (Spring 2020) at University of Rochester
Monitoring Depression Trend on Twitter during the COVID-19 Pandemic
The COVID-19 pandemic has severely affected people's daily lives and caused tremendous economic loss worldwide.
A Functional Representation for Graph Matching
In this paper, we present a functional representation for graph matching (FRGM) that aims to provide more geometric insights on the problem and reduce the space and time complexities of corresponding algorithms.
Texture Mixing by Interpolating Deep Statistics via Gaussian Models
More precisely, we first reveal that the statistics used in existing deep models can be unified using a stationary Gaussian scheme.
Adaptively Transforming Graph Matching
Due to an efficient Frank-Wolfe method-based optimization strategy, we can handle graphs with hundreds and thousands of nodes within an acceptable amount of time.
A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems
On the one hand, by reducing the sensitivity of the indicator in DG to the roundoff error and the magnitude of contribution weight of subcomponent, we proposed a new indicator for two variables which is much more sensitive to their interaction.
Surrogate Model Assisted Cooperative Coevolution for Large Scale Optimization
It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy.
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM).
An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing
However, in these works, the memory design optimization is neglected for weight storage, which will inevitably result in large hardware cost.
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices
As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy.
