Can Wang

Strongly correlated quantum walks with a 12-qubit superconducting processor

2019 · Science

Quantum walks are the quantum analogs of classical random walks, which allow for the simulation of large-scale quantum many-body systems and the realization of universal quantum computation without time-dependent control. We experimentally demonstrate quantum walks …

Learning Temporal Interaction Graph Embedding via Coupled Memory Networks

2020

Graph embedding has become the research focus in both academic and industrial communities due to its powerful capabilities. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, …

Spectrum-Guided Adversarial Disparity Learning

2020

It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class. In this …

Distilling Holistic Knowledge with Graph Neural Networks

2021 · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to a smaller learnable student network. Existing KD methods have mainly considered two types of knowledge, namely the individual knowledge and the …

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective

2025

Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation …

Exploring the Digital Transformation of College English Under the Background of Big Data

2025 · Journal of Humanities Arts and Social Science

As the application of big data technology in education deepens, college English teaching faces a pivotal opportunity for digital transformation. This study, grounded in research findings from an applied undergraduate university in Dalian, delves into …

Bridging the Gap: Self-Optimized Fine-Tuning for LLM-based Recommender Systems

2025 · arXiv (Cornell University)

Recent years have witnessed extensive exploration of Large Language Models (LLMs) on the field of Recommender Systems (RS). There are currently two commonly used strategies to enable LLMs to have recommendation capabilities: 1) The "Guidance-Only" …