Collaborative deep learning across multiple data centers

Collaborative Deep Reinforcement Learning

Besides independent learning, human learning process is highly improved by summarizing what has been learned, communicating it with peers, and subsequently fusing knowledge from di‚erent sources to assist the current learning goal. Œis collaborative learning procedure ensures that the knowledge is shared, continuously re€ned, and concluded from di‚erent perspectives to construct a more profound...

Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms

Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based model...

Deep Collaborative Learning for Visual Recognition

Deep neural networks are playing an important role in state-of-the-art visual recognition. To represent high-level visual concepts, modern networks are equipped with large convolutional layers, which use a large number of filters and contribute significantly to model complexity. For example, more than half of the weights of AlexNet are stored in the first fully-connected layer (4,096 filters). ...

Collaborative Deep Learning in Fixed Topology Networks

There is significant recent interest to parallelize deep learning algorithms in order to handle the enormous growth in data and model sizes. While most advances focus on model parallelization and engaging multiple computing agents via using a central parameter server, aspect of data parallelization along with decentralized computation has not been explored sufficiently. In this context, this pa...

Integrating Deep Learning and Multiple Instance Learning