Hong Chen

A utility maximization strategy for the secondary system operation quality optimization of intelligent substation

2017

At present, the lack of actual operation experience of the intelligent substation secondary system results in indeterminate maintenance strategy, time-consuming and laborious routing inspection, blindness in maintenance work and some other issues. Based on this, …

Learning Similarity-specific Dictionary for Zero-shot Fine-grained Recognition

2019

In this paper, we study the problem of zero-shot fine-grained recognition. It aims to distinguish unseen subordinate categories through some other seen categories within an entry-level category. We demonstrate the necessity to learn multiple latent …

Local Differential Privacy with K-anonymous for Frequency Estimation

2019

Data release, such as statistics of data distribution, in many data analysis and machine learning tasks is needed, which poses significant risks of user's privacy. Usually, to preserve privacy of every individual, frequency estimation based …

Oracle-guided Contrastive Clustering

2022 · arXiv (Cornell University)

Deep clustering aims to learn a clustering representation through deep architectures. Most of the existing methods usually conduct clustering with the unique goal of maximizing clustering performance, that ignores the personalized demand of clustering tasks.% …

On the Global Solution of Soft k-Means

2022 · arXiv (Cornell University)

This paper presents an algorithm to solve the Soft k-Means problem globally. Unlike Fuzzy c-Means, Soft k-Means (SkM) has a matrix factorization-type objective and has been shown to have a close relation with the popular …

TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding

2024 · arXiv (Cornell University)

The advent of Large Multimodal Models (LMMs) has sparked a surge in research aimed at harnessing their remarkable reasoning abilities. However, for understanding text-rich images, challenges persist in fully leveraging the potential of LMMs, and …

Curriculum Learning for Multimedia in the Era of Large Language Models

2024

This tutorial focuses on curriculum learning (CL), an important topic in machine learning, which gains an increasing amount of attention in the research community. CL is a learning paradigm that enables machines to learn from …

Aligning Large Multimodal Model with Sequential Recommendation via Content-Behavior Guidance

2025

Large language models (LLMs) have significantly influenced advancements in sequential recommendation. Nevertheless, the integration and alignment of LLMs with sequence recommenders is often underexploited in current research. Existing LLM-based sequential recommenders mostly rely on textual …

PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution

2018

We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and …