188 citations · 445 across the 40 of their papers we have counts for
18 papers · 1 filter
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?
Ningyu Xu, Tao Gui, Ruotian Ma +4
Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, whereby it enables effective zero-shot cross-lingual transfer of syntactic knowledge. The t…
Efficient Adversarial Training with Robust Early-Bird Tickets
Zhiheng Xi, Rui Zheng, Tao Gui +2
Adversarial training is one of the most powerful methods to improve the robustness of pre-trained language models (PLMs). However, this approach is typically more expensive than tr…
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications
Andrea Cavagna, Nan Li, Alexandros Iosifidis +1
This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients o…
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
Zhongtian Dong, Nan Li, Alexandros Iosifidis +1
For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we…
Robust Lottery Tickets for Pre-trained Language Models
Rui Zheng, Rong Bao, Yuhao Zhou +6
Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching acc…
Attention-based Feature Compression for CNN Inference Offloading in Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies the computational offloading of CNN inference in device-edge co-inference systems. Inspired by the emerging paradigm semantic communication, we propose a novel a…
Towards Understanding Omission in Dialogue Summarization
Yicheng Zou, Kaitao Song, Xu Tan +4
Dialogue summarization aims to condense the lengthy dialogue into a concise summary, and has recently achieved significant progress. However, the result of existing methods is stil…
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge…
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
Kun Yi, Qi Zhang, Liang Hu +4
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable rec…
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER
Ruotian Ma, Xuanting Chen, Lin Zhang +6
As the categories of named entities rapidly increase, the deployed NER models are required to keep updating toward recognizing more entity types, creating a demand for class-increm…
Ferroelectric Solitons Crafted in Epitaxial Bismuth Ferrite Superlattices
V. Govinden, P. R. Tong, X. Guo +10
In ferroelectrics, complex interactions among various degrees of freedom enable the condensation of topologically protected polarization textures. Known as ferroelectric solitons,…
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
Ren Yang, Radu Timofte, Xin Li +49
This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compress…
Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing. To ensure inference accuracy in inference task par…
Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing network. To avoid inference accuracy loss in infere…
Searching for Optimal Subword Tokenization in Cross-domain NER
Ruotian Ma, Yiding Tan, Xin Zhou +6
Input distribution shift is one of the vital problems in unsupervised domain adaptation (UDA). The most popular UDA approaches focus on domain-invariant representation learning, tr…
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective
Xiao Wang, Shihan Dou, Limao Xiong +6
NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resultin…
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents
Yicheng Zou, Hongwei Liu, Tao Gui +5
Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation. Most s…
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective
Shihan Dou, Rui Zheng, Ting Wu +5
Natural language understanding (NLU) models tend to rely on spurious correlations (i.e., dataset bias) to achieve high performance on in-distribution datasets but poor performance…