Liang He

Performance evaluation of an anomaly-detection algorithm for keystroke-typing based insider detection

2018 · Tsinghua Science & Technology

Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user's legitimacy, and the performances of these classifications should be confirmed …

Geometric Discriminant Analysis for I-vector Based Speaker Verification

2019

Many i-vector based speaker verification use linear discriminant analysis (LDA) as a post-processing stage. LDA maximizes the arithmetic mean of the Kullback-Leibler (KL) divergences between different pairs of speakers. However, for speaker verification, speakers with …

Cross-Modal Knowledge Distillation For Fine-Grained One-Shot Classification

2021

Few-shot learning can recognize a novel category based on only a few samples because it learns to learn from a lot of labeled samples during the training process. When data is insufficient, the performance is …

CausalABSC: Causal Inference for Aspect Debiasing in Aspect-Based Sentiment Classification

2023 · IEEE/ACM Transactions on Audio Speech and Language Processing

As the primary subtask of sentiment analysis, aspect-based sentiment classification (ABSC) aims to predict the sentiment polarity for a given aspect. While recent deep neural models for ABSC have shown good performance, their robustness is …

A Hierarchical Network for Multimodal Document-Level Relation Extraction

2024 · Proceedings of the AAAI Conference on Artificial Intelligence

Document-level relation extraction aims to extract entity relations that span across multiple sentences. This task faces two critical issues: long dependency and mention selection. Prior works address the above problems from the textual perspective, however, …

Enhancing Abstractive Dialogue Summarization with Internal Knowledge

2024

The task of dialogue summarization involves distilling a given dialogue into a concise and coherent summary. However, discrepancies in language styles between dialogues and summaries, scattered crucial information, incomplete utterances with ellipsis, and coreferences bring …

Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback

2025 · arXiv (Cornell University)

This paper introduces an interactive continual learning paradigm where AI models dynamically learn new skills from real-time human feedback while retaining prior knowledge. This paradigm distinctively addresses two major limitations of traditional continual learning: (1) …