Modularity and community structure in networks
Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or...
Publications
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513 publications
Quantifying the Sim-To-Real Gap in UAV Disturbance Rejection
Due to the safety risks and training sample inefficiency, it is often preferred to develop controllers in simulation. However, minor differences between the simulation and the r...
Momentum Contrast for Unsupervised Visual Representation Learning
We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic diction...
A survey on Image Data Augmentation for Deep Learning
Abstract Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfi...
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, ...
Non-local Neural Networks
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family...
Conceptualizing, Measuring, and Managing Customer-Based Brand Equity
The author presents a conceptual model of brand equity from the perspective of the individual consumer. Customer-based brand equity is defined as the differential effect of bran...
Finding Structure in Time
Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implici...
An application-specific protocol architecture for wireless microsensor networks
Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individ...
Gaussian Processes for Machine Learning
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over...