Search Results for author:
Found 9 papers, 3 papers with code
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants
In this paper, we introduce a fallback skill recommendation system to suggest a voice app to a customer for an unhandled voice command.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant
Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks.
Improving Community Detection Performance in Heterogeneous Music Network by Learning Edge-type Usefulness Distribution
Therefore a heterogeneous network can be converted to a homogeneous one where those conventional methods are eligible to use.
Efficient Personalized Community Detection via Genetic Evolution
Personalized community detection aims to generate communities associated with user need on graphs, which benefits many downstream tasks such as node recommendation and link prediction for users, etc.
Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning
In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities.
Typilus: Neural Type Hints
The network uses deep similarity learning to learn a TypeSpace -- a continuous relaxation of the discrete space of types -- and how to embed the type properties of a symbol (i. e. identifier) into it.
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data
Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising.
Neural Related Work Summarization with a Joint Context-driven Attention Mechanism
Conventional solutions to automatic related work summarization rely heavily on human-engineered features.
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.
