
Label Disentanglement in Partitionbased Extreme Multilabel Classification
Partitionbased methods are increasinglyused in extreme multilabel cla...
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Extreme Multilabel Learning for Semantic Matching in Product Search
We consider the problem of semantic matching in product search: given a ...
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Accelerating Inference for Sparse Extreme MultiLabel Ranking Trees
Treebased models underpin many modern semantic search engines and recom...
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PECOS: Prediction for Enormous and Correlated Output Spaces
Many challenging problems in modern applications amount to finding relev...
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Extreme Multilabel Classification from Aggregated Labels
Extreme multilabel classification (XMC) is the problem of finding the r...
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Learning to Encode Position for Transformer with Continuous Dynamical Model
We introduce a new way of learning to encode position information for no...
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Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure
The architecture of Transformer is based entirely on selfattention, and...
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Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
In this paper, we consider recommender systems with side information in ...
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Think Globally, Act Locally: A Deep Neural Network Approach to HighDimensional Time Series Forecasting
Forecasting highdimensional time series plays a crucial role in many ap...
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AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
Deep neural networks have yielded superior performance in many applicati...
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A Modular Deep Learning Approach for Extreme Multilabel Text Classification
Extreme multilabel classification (XMC) aims to assign to an instance t...
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LearningWord Embeddings for Lowresource Languages by PU Learning
Word embedding is a key component in many downstream applications in pro...
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Extreme Stochastic Variational Inference: Distributed and Asynchronous
We propose extreme stochastic variational inference (ESVI), an asynchron...
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Highdimensional Time Series Prediction with Missing Values
Highdimensional time series prediction is needed in applications as div...
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HsiangFu Yu
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