jingtaozhan,Jingtao Zhan,Jingtao_Zhan,github

Hi there 👋 This is Jingtao Zhan.

🌱 I’m a third-year PhD student at Tsinghua IR Group supervised by Prof. Shaoping Ma and Prof. Yiqun Liu.
🔭 My research lies in Information Retrieval and Web Search. I currently focus on Dense Retrieval with a wide interest in improving its effectiveness, efficiency, and interpretability. The publications are available at my homepage.
📫 Contact me via [email protected] or twitter.

Jingtao Zhan's Projects

bert-ranking-analysis

SIGIR'20: An Analysis of BERT in Document Ranking

disentangled-retriever

An easy-to-use python toolkit for flexibly adapting various neural ranking models to any target domain.

dl4eeg-classification

The implementation of deep learning models for EEG classification.

drhard

SIGIR'21: Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track.

extrapolate-eval

CIKM 2022: Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

jpq

CIKM'21: JPQ substantially improves the efficiency of Dense Retrieval with 30x compression ratio, 10x CPU speedup and 2x GPU speedup.

pyserini

Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

rayuela.jl

Code for my PhD thesis. Library of quantization-based methods for fast similarity search in high dimensions. Presented at ECCV 18.

RepBERT is a competitive first-stage retrieval technique. It represents documents and queries with fixed-length contextualized embeddings. The inner products of them are regarded as relevance scores. Its efficiency is comparable to bag-of-words methods.