172 citations · 516 across the 28 of their papers we have counts for
8 papers · 1 filter
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Lavender Yao Jiang, John Shi, Mark Cheung +2
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data. A question that arises is how much performance improvement does the underlying graph s…
Annotation-Efficient Untrimmed Video Action Recognition
Yixiong Zou, Shanghang Zhang, Guangyao Chen +3
Deep learning has achieved great success in recognizing video actions, but the collection and annotation of training data are still quite laborious, which mainly lies in two aspect…
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
Mark Cheung, John Shi, Oren Wright +3
Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains. But conventional deep learnin…
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
Yixiong Zou, Shanghang Zhang, Ke Chen +3
Few-shot learning (FSL) aims at recognizing novel classes given only few training samples, which still remains a great challenge for deep learning. However, humans can easily recog…
Evaluating and Aggregating Feature-based Model Explanations
Umang Bhatt, Adrian Weller, José M. F. Moura
A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point. As the number of proposed explanation functions grows,…
Pooling in Graph Convolutional Neural Networks
Mark Cheung, John Shi, Lavender Yao Jiang +2
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods…
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
Brian Swenson, Soummya Kar, H. Vincent Poor +2
The article discusses distributed gradient-descent algorithms for computing local and global minima in nonconvex optimization. For local optimization, we focus on distributed stoch…
Graph Fourier Transform: A Stable Approximation
João Domingos, José M. F. Moura
In Graph Signal Processing (GSP), data dependencies are represented by a graph whose nodes label the data and the edges capture dependencies among nodes. The graph is represented b…