19 citations · 79 across the 11 of their papers we have counts for
11 papers
LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval
Yingrui Yang, Parker Carlson, Yifan Qiao +3
This paper studies fast fusion of dense retrieval and sparse lexical retrieval, and proposes a cluster-based selective dense retrieval method called CluSD guided by sparse lexical…
Weighted KL-Divergence for Document Ranking Model Refinement
Yingrui Yang, Yifan Qiao, Shanxiu He +1
Transformer-based retrieval and reranking models for text document search are often refined through knowledge distillation together with contrastive learning. A tight distribution…
Approximate Cluster-Based Sparse Document Retrieval with Segmented Maximum Term Weights
Yifan Qiao, Shanxiu He, Yingrui Yang +2
This paper revisits cluster-based retrieval that partitions the inverted index into multiple groups and skips the index partially at cluster and document levels during online infer…
Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval
Yifan Qiao, Yingrui Yang, Shanxiu He +1
Learned sparse document representations using a transformer-based neural model has been found to be attractive in both relevance effectiveness and time efficiency. This paper descr…
Optimizing Guided Traversal for Fast Learned Sparse Retrieval
Yifan Qiao, Yingrui Yang, Haixin Lin +1
Recent studies show that BM25-driven dynamic index skipping can greatly accelerate MaxScore-based document retrieval based on the learned sparse representation derived by DeepImpac…
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs
John Thorpe, Pengzhan Zhao, Jonathan Eyolfson +5
DNN models across many domains continue to grow in size, resulting in high resource requirements for effective training, and unpalatable (and often unaffordable) costs for organiza…
Dual Skipping Guidance for Document Retrieval with Learned Sparse Representations
Yifan Qiao, Yingrui Yang, Haixin Lin +3
This paper proposes a dual skipping guidance scheme with hybrid scoring to accelerate document retrieval that uses learned sparse representations while still delivering a good rele…
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking
Yingrui Yang, Yifan Qiao, Tao Yang
Transformer based re-ranking models can achieve high search relevance through context-aware soft matching of query tokens with document tokens. To alleviate runtime complexity of s…
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
John Thorpe, Yifan Qiao, Jonathan Eyolfson +8
A graph neural network (GNN) enables deep learning on structured graph data. There are two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which are e…
Composite Re-Ranking for Efficient Document Search with BERT
Yingrui Yang, Yifan Qiao, Jinjin Shao +3
Although considerable efforts have been devoted to transformer-based ranking models for document search, the relevance-efficiency tradeoff remains a critical problem for ad-hoc ran…
Algorithm-Directed Crash Consistence in Non-Volatile Memory for HPC
Shuo Yang, Kai Wu, Yifan Qiao +2
Fault tolerance is one of the major design goals for HPC. The emergence of non-volatile memories (NVM) provides a solution to build fault tolerant HPC. Data in NVM-based main memor…