21 citations · 58 across the 6 of their papers we have counts for
5 papers · 1 filter
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
Sachin Ravi, Sebastian Musslick, Maia Hamin +2
The terms multi-task learning and multitasking are easily confused. Multi-task learning refers to a paradigm in machine learning in which a network is trained on various related ta…
Deep Graph Similarity Learning: A Survey
Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke +1
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, su…
A View on Deep Reinforcement Learning in System Optimization
Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke +3
Many real-world systems problems require reasoning about the long term consequences of actions taken to configure and manage the system. These problems with delayed and often seque…
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
Javier S. Turek, Shailee Jain, Vy Vo +3
Recent work has shown that topological enhancements to recurrent neural networks (RNNs) can increase their expressiveness and representational capacity. Two popular enhancements ar…
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu +3
As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-d…