Sparse Text

Sparse text and data representation is a growing area of research focusing on efficiently processing and analyzing data with a high proportion of irrelevant or missing information. Current efforts involve developing novel model architectures, such as transformer-based networks and attention mechanisms, to effectively learn from and predict outcomes using only a subset of available data points, improving computational efficiency and reducing storage needs. This research is significant because it addresses limitations in handling large, sparse datasets across diverse applications, including autonomous driving, recommender systems, and image-text retrieval, leading to more efficient and accurate models. Improved sparse data handling promises advancements in various fields by enabling the use of larger datasets and more complex models without the constraints of computational resources.

Papers

September 14, 2024

OPUS: Occupancy Prediction Using a Sparse Set
Jiabao Wang, Zhaojiang Liu, Qiang Meng, Liujiang Yan, Ke Wang, Jie Yang, Wei Liu, Qibin Hou, Ming-Ming Cheng
Occupancy Prediction Sparse Text

July 10, 2024

Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
Jianzhe Xue, Dongcheng Yuan, Yu Sun, Tianqi Zhang, Wenchao Xu, Haibo Zhou, Xuemin, Shen
Temporal Attention Vehicle Plan Temporal Traffic Traffic State Estimation Retentive Network Sparse Text

May 21, 2024

Enhancing User Interest based on Stream Clustering and Memory Networks in Large-Scale Recommender Systems
Peng Liu, Nian Wang, Cong Xu, Ming Zhao, Bin Wang, Yi Ren
Recommender System User Interest Memory Network Large Scale Recommender System Sparse Text

August 3, 2023

Accurate Neural Network Pruning Requires Rethinking Sparse Optimization
Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh
Sparse Network Sparse Training Neural Network Pruning Sparse Optimization Natural Language Processing Benchmark Sparse Text

June 20, 2023

Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
Zixuan Wu, Sean Ye, Manisha Natarajan, Letian Chen, Rohan Paleja, Matthew C. Gombolay
Reinforcement Learning Web Tracking Observable Environment Conflict Detection Adversarial Search Sparse Text

January 30, 2023

STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
Chen Chen, Bowen Zhang, Liangliang Cao, Jiguang Shen, Tom Gunter, Albin Madappally Jose, Alexander Toshev, Jonathon Shlens, Ruoming Pang, Yinfei Yang
Image Representation Bag of Word Sparse Semantic Sparse Perception Sparse Token Sparse Text