357 citations · 681 across the 28 of their papers we have counts for
5 papers · 1 filter
Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure
Vikas K. Garg, Inderjit S. Dhillon, Hsiang-Fu Yu
The architecture of Transformer is based entirely on self-attention, and has been shown to outperform models that employ recurrence on sequence transduction tasks such as machine t…
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
Liwei Wu, Hsiang-Fu Yu, Nikhil Rao +2
In this paper, we consider recommender systems with side information in the form of graphs. Existing collaborative filtering algorithms mainly utilize only immediate neighborhood i…
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
Jiong Zhang, Hsiang-fu Yu, Inderjit S. Dhillon
Deep neural networks have yielded superior performance in many applications; however, the gradient computation in a deep model with millions of instances lead to a lengthy training…
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Rajat Sen, Hsiang-Fu Yu, Inderjit Dhillon
Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions. Modern datasets can have millions of correl…
Taming Pretrained Transformers for Extreme Multi-label Text Classification
Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong +2
We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input te…