559 citations · 932 across the 12 of their papers we have counts for
12 papers
DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation
Dwipam Katariya, Thomas Caputo, Akshat Shreemali +6
Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain inherently inefficient: stand…
ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting
Zepu Wang, Yuqi Nie, Peng Sun +3
The criticality of prompt and precise traffic forecasting in optimizing traffic flow management in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus. S…
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
Vijay Ekambaram, Arindam Jati, Nam Nguyen +2
Transformers have gained popularity in time series forecasting for their ability to capture long-sequence interactions. However, their high memory and computing requirements pose a…
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +4
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values. In many industrial problem, it…
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong +1
We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components:…
COVID-19 Pneumonia Severity Prediction using Hybrid Convolution-Attention Neural Architectures
Nam Nguyen, J. Morris Chang
This study proposed a novel framework for COVID-19 severity prediction, which is a combination of data-centric and model-centric approaches. First, we propose a data-centric pre-tr…
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting
Nam Nguyen, Brian Quanz
Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most prev…
SAIA: Split Artificial Intelligence Architecture for Mobile Healthcare System
Di Zhuang, Nam Nguyen, Keyu Chen +1
As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for biomedical and healthcare problems, mobile healthcare systems have received unpr…
A Scale Invariant Flatness Measure for Deep Network Minima
Akshay Rangamani, Nam H. Nguyen, Abhishek Kumar +3
It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization. However, for deep networks with pos…
Estimating Information Flow in Deep Neural Networks
Ziv Goldfeld, Ewout van den Berg, Kristjan Greenewald +4
We study the flow of information and the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the informat…
When Does Stochastic Gradient Algorithm Work Well?
Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan +2
In this paper, we consider a general stochastic optimization problem which is often at the core of supervised learning, such as deep learning and linear classification. We consider…
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Alireza Aghasi, Afshin Abdi, Nam Nguyen +1
We introduce and analyze a new technique for model reduction for deep neural networks. While large networks are theoretically capable of learning arbitrarily complex models, overfi…