Search Results for author:
Found 17 papers, 9 papers with code
Laplacian Convolutional Representation for Traffic Time Series Imputation
In this study, we first introduce a Laplacian kernel to temporal regularization for characterizing local trends in traffic time series, which can be formulated in the form of circular convolution.
Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression
In the temporal context, the complex time-varying system behaviors can be revealed by the temporal modes in the proposed model.
TCBERT: A Technical Report for Chinese Topic Classification BERT
Bidirectional Encoder Representations from Transformers or BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various NLP tasks due to its remarkable performance.
POViT: Vision Transformer for Multi-objective Design and Characterization of Nanophotonic Devices
In this work, we propose the first-ever Transformer model (POViT) to efficiently design and simulate semiconductor photonic devices with multiple objectives.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting
Modern time series datasets are often high-dimensional, incomplete/sparse, and nonstationary.
An Adaptive Deep Clustering Pipeline to Inform Text Labeling at Scale
Mining the latent intentions from large volumes of natural language inputs is a key step to help data analysts design and refine Intelligent Virtual Assistants (IVAs) for customer service and sales support.
A Semi-Supervised Deep Clustering Pipeline for Mining Intentions From Texts
The clustering quality exceeds the state-of-the-art results when BERT is fine-tuned with labeled subsets of only 2. 5% of the task data.
Ultralow complexity long short-term memory network for fiber nonlinearity mitigation in coherent optical communication systems
In this paper, we propose a center-oriented long short-term memory network (Co-LSTM) incorporating a simplified mode with a recycling mechanism in the equalization operation, which can mitigate fiber nonlinearity in coherent optical communication systems with ultralow complexity.
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing \textit{temporal variation} as a new regularization term into the completion of a third-order (sensor $\times$ time of day $\times$ day) tensor.
Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data.
Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting
To address this fundamental issue, this paper presents an incremental Bayesian tensor learning method for reconstruction of spatiotemporal missing data in SHM and forecasting of structural response.
Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework to model multivariate time series data.
A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation
Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.
Bayesian Temporal Factorization for Multidimensional Time Series Prediction
In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series -- in particular spatiotemporal data -- in the presence of missing values.
A Survey on Graph Processing Accelerators: Challenges and Opportunities
Graph is a well known data structure to represent the associated relationships in a variety of applications, e. g., data science and machine learning.
Distributed, Parallel, and Cluster Computing
In situ TensorView: In situ Visualization of Convolutional Neural Networks
We present in situ TensorView to visualize the training and functioning of CNNs as if they are systems of scientific simulations.
