Zhen Qin

Leveraging Image Visual Features in Content-Based Recommender System

2018 · Scientific Programming

Content-based (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mtext>CB</mml:mtext></mml:mrow></mml:math>) and collaborative filtering (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mtext>CF</mml:mtext></mml:mrow></mml:math>) recommendation algorithms are widely used in modern e-commerce recommender systems (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mtext>RSs</mml:mtext></mml:mrow></mml:math>) to improve user experience of personalized services. Item content features and user-item …

Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering

2018

User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types …

Multitask Mixture of Sequential Experts for User Activity Streams

2020

It is often desirable to model multiple objectives in real-world web applications, such as user satisfaction and user engagement in recommender systems. Multi-task learning has become the standard approach for such applications recently.

The Devil in Linear Transformer

2022 · arXiv (Cornell University)

Linear transformers aim to reduce the quadratic space-time complexity of vanilla transformers. However, they usually suffer from degraded performances on various tasks and corpus. In this paper, we examine existing kernel-based linear transformers and identify …

Quantum State Tomography for Matrix Product Density Operators

2023 · arXiv (Cornell University)

The reconstruction of quantum states from experimental measurements, often achieved using quantum state tomography (QST), is crucial for the verification and benchmarking of quantum devices. However, performing QST for a generic unstructured quantum state requires …

CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

2024 · Computers, materials & continua/Computers, materials & continua (Print)

In the intelligent medical diagnosis area, Artificial Intelligence (AI)’s trustworthiness, reliability, and interpretability are critical, especially in cancer diagnosis. Traditional neural networks, while excellent at processing natural images, often lack interpretability and adaptability when processing …

Scaling Laws for Linear Complexity Language Models

2024 · arXiv (Cornell University)

The interest in linear complexity models for large language models is on the rise, although their scaling capacity remains uncertain. In this study, we present the scaling laws for linear complexity language models to establish …

Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I.

2024

The traditional evaluation of information retrieval (IR) systems is generally very costly as it requires manual relevance annotation from human experts. Recent advancements in generative artificial intelligence -specifically large language models (LLMs)- can generate relevance …