Hao Cheng

Research on the Method of Multi-source Information Fusion Based on Bayesian Theory

2018

Facing with the incompleteness and uncertainty of target damage information, the integrated method of multi-source information fusion and bayesian theory is put forward by using target damage rank from multi-source information fusion. Target damage information …

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

2021 · ACM Transactions on Computing for Healthcare

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A …

Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing

2021 · arXiv (Cornell University)

Motivation: A perennial challenge for biomedical researchers and clinical practitioners is to stay abreast with the rapid growth of publications and medical notes. Natural language processing (NLP) has emerged as a promising direction for taming …

CESED: Exploiting Hyperspherical Predefined Evenly-Distributed Class Centroids for OOD Detection

2023 · Society for Industrial and Applied Mathematics eBooks

Out-of-distribution (OOD) detection is critical for ensuring the safe deployment of machine learning models in the open world. Due to the simplicity and intuitiveness of distance- based methods, i.e., samples are detected as OOD if …

READ: Aggregating Reconstruction Error into Out-of-Distribution Detection

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Detecting out-of-distribution (OOD) samples is crucial to the safe deployment of a classifier in the real world. However, deep neural networks are known to be overconfident for abnormal data. Existing works directly design score function …

Explainable Recommendation with Personalized Review Retrieval and Aspect Learning

2023

Hao Cheng, Shuo Wang, Wensheng Lu, Wei Zhang, Mingyang Zhou, Kezhong Lu, Hao Liao. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive Learning

2024 · ACM Transactions on Intelligent Systems and Technology

Explainable recommender systems ( ERS ) aim to enhance users’ trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the recommendations, …

LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy

2024 · arXiv (Cornell University)

The Key-Value (KV) cache is a crucial component in serving transformer-based autoregressive large language models (LLMs), enabling faster inference by storing previously computed KV vectors. However, its memory consumption scales linearly with sequence length and …

Language Models as Inductive Reasoners

2024

Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, Furu Wei. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). …

Adversarial Training for Large Neural Language Models

2020 · arXiv (Cornell University)

Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models …