activity
20192023
most citedLearnable Embedding Sizes for Recommender Systems

20 citations · 55 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV202315 cited

detrex: Benchmarking Detection Transformers

Tianhe Ren, Shilong Liu, Feng Li +13

The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and…

cs.CV20223 cited

DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding

Shilong Liu, Yaoyuan Liang, Feng Li +5

In this paper, we study the problem of visual grounding by considering both phrase extraction and grounding (PEG). In contrast to the previous phrase-known-at-test setting, PEG req…

cs.CV20222 cited

A Unified Mutual Supervision Framework for Referring Expression Segmentation and Generation

Shijia Huang, Feng Li, Hao Zhang +3

Reference Expression Segmentation (RES) and Reference Expression Generation (REG) are mutually inverse tasks that can be naturally jointly trained. Though recent work has explored…

stat.ME2022

Robust analyses for longitudinal clinical trials with missing and non-normal continuous outcomes

Siyi Liu, Yilong Zhang, Gregory T Golm +3

Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conven…

cs.LG202120 cited

Learnable Embedding Sizes for Recommender Systems

Siyi Liu, Chen Gao, Yihong Chen +2

The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manne…

cs.IR20201 cited

Long-tail Session-based Recommendation

Siyi Liu, Yujia Zheng

Session-based recommendation focuses on the prediction of user actions based on anonymous sessions and is a necessary method in the lack of user historical data. However, none of t…