activity
19942023
most citedHands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users

891 citations · 1.4k across the 37 of their papers we have counts for

collaborators

39 papers

cs.CL2023

Reward Engineering for Generating Semi-structured Explanation

Jiuzhou Han, Wray Buntine, Ehsan Shareghi

Semi-structured explanation depicts the implicit process of a reasoner with an explicit representation. This explanation highlights how available information in a specific query is…

cs.CL2023★ 8 cited

A Survey on Out-of-Distribution Evaluation of Neural NLP Models

Xinzhe Li, Ming Liu, Shang Gao +1

Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. Howev…

cs.CL2023★ 6 cited

PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs

Jiuzhou Han, Nigel Collier, Wray Buntine +1

Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains. Due to the training objective of LLMs and their pre-training…

cs.CL2023

Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets

Jionghao Lin, Wei Tan, Ngoc Dang Nguyen +6

Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues…

cs.CL2023★ 1 cited

Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification

Wei Tan, Jionghao Lin, David Lang +4

Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obta…

cs.LG2022★ 1 cited

Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment

Qizhou Wang, Guansong Pang, Mahsa Salehi +2

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled…