Zhongyuan Wang

Can Machines Intelligently Propose Novel and Reasonable Scientific Hypotheses?

2017

Machine intelligence is attracting increasing attention from both industry and academia. However, the problem of how to make machines innovate novel hypothesis is underexplored. Automatic hypothesis generation can effectively shorten research process. In this work, …

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

2019 · arXiv (Cornell University)

Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this …

CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models

2024 · arXiv (Cornell University)

Cognitive dynamics are pivotal to advance human understanding of the world. Recent advancements in large language models (LLMs) reveal their potential for cognitive simulation. However, these LLM-based cognitive studies primarily focus on static modeling, overlooking …

Towards Effective Code-Integrated Reasoning

2025 · arXiv (Cornell University)

In this paper, we investigate code-integrated reasoning, where models generate code when necessary and integrate feedback by executing it through a code interpreter. To acquire this capability, models must learn when and how to use …

S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

2020

Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained …

Multi-modal Knowledge Graphs for Recommender Systems

2020

Recommender systems have shown great potential to solve the information explosion problem and enhance user experience in various online applications. To tackle data sparsity and cold start problems in recommender systems, researchers propose knowledge graphs …

ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding

2021 · arXiv (Cornell University)

Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (unsup-SimCSE). Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same …