115 citations · 212 across the 15 of their papers we have counts for
15 papers
Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models
Jinbo Song, Ruoran Huang, Xinyang Wang +9
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, in…
GA-Sketching: Shape Modeling from Multi-View Sketching with Geometry-Aligned Deep Implicit Functions
Jie Zhou, Zhongjin Luo, Qian Yu +2
Sketch-based shape modeling aims to bridge the gap between 2D drawing and 3D modeling by providing an intuitive and accessible approach to create 3D shapes from 2D sketches. Howeve…
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter
Jinglong Wang, Xiawei Li, Jing Zhang +5
The pre-trained text-image discriminative models, such as CLIP, has been explored for open-vocabulary semantic segmentation with unsatisfactory results due to the loss of crucial l…
Distortion-aware Transformer in 360° Salient Object Detection
Yinjie Zhao, Lichen Zhao, Qian Yu +3
With the emergence of VR and AR, 360° data attracts increasing attention from the computer vision and multimedia communities. Typically, 360° data is projected into 2D ERP (equirec…
Inversion-by-Inversion: Exemplar-based Sketch-to-Photo Synthesis via Stochastic Differential Equations without Training
Ximing Xing, Chuang Wang, Haitao Zhou +4
Exemplar-based sketch-to-photo synthesis allows users to generate photo-realistic images based on sketches. Recently, diffusion-based methods have achieved impressive performance o…
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
Ximing Xing, Chuang Wang, Haitao Zhou +3
We demonstrate that pre-trained text-to-image diffusion models, despite being trained on raster images, possess a remarkable capacity to guide vector sketch synthesis. In this pape…
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder
Zihao Fu, Wai Lam, Qian Yu +4
The sequence-to-sequence (seq2seq) task aims at generating the target sequence based on the given input source sequence. Traditionally, most of the seq2seq task is resolved by the…
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction
Jie Zhou, Xianshuai Cao, Wenhao Li +4
Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out…
Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System
Jie Zhou, Qian Yu, Chuan Luo +1
In recent years, thanks to the rapid development of deep learning (DL), DL-based multi-task learning (MTL) has made significant progress, and it has been successfully applied to re…
Product Question Answering in E-Commerce: A Survey
Yang Deng, Wenxuan Zhang, Qian Yu +1
Product question answering (PQA), aiming to automatically provide instant responses to customer's questions in E-Commerce platforms, has drawn increasing attention in recent years.…
Feasibility study of quantum computing using trapped electrons
Qian Yu, Alberto M. Alonso, Jackie Caminiti +7
We investigate the feasibility of using electrons in a linear Paul trap as qubits in a future quantum computer. We discuss the necessary experimental steps to realize such a device…
One- and two-qubit gate infidelities due to motional errors in trapped ions and electrons
R. Tyler Sutherland, Qian Yu, Kristin M. Beck +1
In this work, we derive analytic formulae that determine the effect of error mechanisms on one- and two-qubit gates in trapped ions and electrons. First, we analyze, and derive exp…
Biomedical Question Answering: A Survey of Approaches and Challenges
Qiao Jin, Zheng Yuan, Guangzhi Xiong +7
Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovat…
Further Theoretical Study of Distribution Separation Method for Information Retrieval
Peng Zhang, Qian Yu, Yuexian Hou +3
Recently, a Distribution Separation Method (DSM) is proposed for relevant feedback in information retrieval, which aims to approximate the true relevance distribution by separating…
Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principle
Xiaozhao Zhao, Yuexian Hou, Qian Yu +2
Typical dimensionality reduction methods focus on directly reducing the number of random variables while retaining maximal variations in the data. In this paper, we consider the di…