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04/28/2022
Taylor Genetic Programming for Symbolic Regression
Genetic programming (GP) is a commonly used approach to solve symbolic r...
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04/22/2022
Exploring Hidden Semantics in Neural Networks with Symbolic Regression
Many recent studies focus on developing mechanisms to explain the black-...
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09/10/2021
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
Zero-shot transfer learning for dialogue state tracking (DST) enables us...
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05/10/2021
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handl...
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03/26/2021
Gated Transformer Networks for Multivariate Time Series Classification
Deep learning model (primarily convolutional networks and LSTM) for time...
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12/31/2020
Continual Learning in Task-Oriented Dialogue Systems
Continual learning in task-oriented dialogue systems can allow us to add...
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10/24/2020
Adding Chit-Chats to Enhance Task-Oriented Dialogues
The existing dialogue corpora and models are typically designed under tw...
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05/01/2020
Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity
Open-ended human learning and information-seeking are increasingly media...
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04/24/2018
Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks
Click through rate (CTR) prediction is very important for Native adverti...
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09/13/2017
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning
As the use of cloud computing continues to rise, controlling cost become...
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03/31/2017
Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation
We proposed a deep learning method for interpretable diabetic retinopath...
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11/20/2016
Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
We propose a simple but strong baseline for time series classification f...
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10/24/2016
Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization
We propose a new model based on the deconvolutional networks and SAX dis...
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10/13/2015
Adopting Robustness and Optimality in Fitting and Learning
We generalized a modified exponentialized estimator by pushing the robus...
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06/08/2015
Adaptive Normalized Risk-Averting Training For Deep Neural Networks
This paper proposes a set of new error criteria and learning approaches,...
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06/01/2015