activity
20202024
most citedTMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

34 citations · 44 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods

Montgomery Bohde, Meng Liu, Alexandra Saxton +1

Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in exist…

cs.SD202334 cited

TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification

Meng Liu, Ke Liang, Dayu Hu +6

Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-mod…

cs.LG20232 cited

Reinforcement Graph Clustering with Unknown Cluster Number

Yue Liu, Ke Liang, Jun Xia +5

Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks in an unsupervised manner, has attracted great attention in recent years. Although the pe…

cs.AI2023

arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering

Meng Liu, Ke Liang, Yue Liu +3

Temporal graph clustering (TGC) is a crucial task in temporal graph learning. Its focus is on node clustering on temporal graphs, and it offers greater flexibility for large-scale…

astro-ph.SR2023

Estimating Stellar Parameters and Identifying Very Metal-poor Stars Using Convolutional Neural Networks for Low-resolution Spectra (R~200)

Tianmin Wu, Yude Bu, Jianhang Xie +5

Very metal-poor (VMP, [Fe/H]<-2.0) stars offer a wealth of information on the nature and evolution of elemental production in the early galaxy and universe. The upcoming China Spac…

eess.AS20223 cited

Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022

Chang Zeng, Lin Zhang, Meng Liu +1

Current state-of-the-art automatic speaker verification (ASV) systems are vulnerable to presentation attacks, and several countermeasures (CMs), which distinguish bona fide trials…