28 citations · 38 across the 6 of their papers we have counts for
7 papers
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Chaosheng Dong, Xiaojie Jin, Weihao Gao +5
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments. The sheer volume of streaming training…
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
Da Chen, Yongliang Yang, Zunlei Feng +6
Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the met…
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo, Philip Sun, Erik Lindgren +4
Quantization based techniques are the current state-of-the-art for scaling maximum inner product search to massive databases. Traditional approaches to quantization aim to minimize…
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
Feng Mao, Xiang Wu, Hui Xue +1
High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutiona…
Low-Rank Principal Eigenmatrix Analysis
Krishna Balasubramanian, Elynn Y. Chen, Jianqing Fan +1
Sparse PCA is a widely used technique for high-dimensional data analysis. In this paper, we propose a new method called low-rank principal eigenmatrix analysis. Different from spar…
Local Orthogonal Decomposition for Maximum Inner Product Search
Xiang Wu, Ruiqi Guo, Sanjiv Kumar +1
Inverted file and asymmetric distance computation (IVFADC) have been successfully applied to approximate nearest neighbor search and subsequently maximum inner product search. In s…
Efficient Inner Product Approximation in Hybrid Spaces
Xiang Wu, Ruiqi Guo, David Simcha +2
Many emerging use cases of data mining and machine learning operate on large datasets with data from heterogeneous sources, specifically with both sparse and dense components. For…