Search Results for author: Yulong Pei

Found 28 papers, 14 papers with code

BuDDIE: A Business Document Dataset for Multi-task Information Extraction

no code implementations5 Apr 2024 Ran Zmigrod, Dongsheng Wang, Mathieu Sibue, Yulong Pei, Petr Babkin, Ivan Brugere, Xiaomo Liu, Nacho Navarro, Antony Papadimitriou, William Watson, Zhiqiang Ma, Armineh Nourbakhsh, Sameena Shah

Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia.

Document Classification document understanding +5

DocLLM: A layout-aware generative language model for multimodal document understanding

no code implementations31 Dec 2023 Dongsheng Wang, Natraj Raman, Mathieu Sibue, Zhiqiang Ma, Petr Babkin, Simerjot Kaur, Yulong Pei, Armineh Nourbakhsh, Xiaomo Liu

Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalities.

document understanding Language Modelling

A Structural-Clustering Based Active Learning for Graph Neural Networks

1 code implementation7 Dec 2023 Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy

To address this problem, we propose the Structural-Clustering PageRank method for improved Active learning (SPA) specifically designed for graph-structured data.

Active Learning Clustering +2

Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams

no code implementations12 Oct 2023 Ethan Callanan, Amarachi Mbakwe, Antony Papadimitriou, Yulong Pei, Mathieu Sibue, Xiaodan Zhu, Zhiqiang Ma, Xiaomo Liu, Sameena Shah

Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models.

MOLE: MOdular Learning FramEwork via Mutual Information Maximization

no code implementations15 Aug 2023 Tianchao Li, Yulong Pei

This paper is to introduce an asynchronous and local learning framework for neural networks, named Modular Learning Framework (MOLE).

Enhancing Adversarial Training via Reweighting Optimization Trajectory

1 code implementation25 Jun 2023 Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlaod Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy

Despite the fact that adversarial training has become the de facto method for improving the robustness of deep neural networks, it is well-known that vanilla adversarial training suffers from daunting robust overfitting, resulting in unsatisfactory robust generalization.

Adversarial Robustness

DF2M: An Explainable Deep Bayesian Nonparametric Model for High-Dimensional Functional Time Series

no code implementations23 May 2023 Yirui Liu, Xinghao Qiao, Yulong Pei, Liying Wang

In this paper, we present Deep Functional Factor Model (DF2M), a Bayesian nonparametric model for analyzing high-dimensional functional time series.

Time Series Variational Inference

Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks

no code implementations10 May 2023 Xianzhi Li, Samuel Chan, Xiaodan Zhu, Yulong Pei, Zhiqiang Ma, Xiaomo Liu, Sameena Shah

The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation.

Binary Classification named-entity-recognition +5

You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets

1 code implementation28 Nov 2022 Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu

Recent works have impressively demonstrated that there exists a subnetwork in randomly initialized convolutional neural networks (CNNs) that can match the performance of the fully trained dense networks at initialization, without any optimization of the weights of the network (i. e., untrained networks).

Out-of-Distribution Detection

FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering

1 code implementation21 Sep 2022 Ricky Fajri, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy

Active Learning (AL) techniques have proven to be highly effective in reducing data labeling costs across a range of machine learning tasks.

Active Learning Clustering +1

Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training

no code implementations30 May 2022 Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu

Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically sparse neural networks from scratch.

Semantic-Based Few-Shot Learning by Interactive Psychometric Testing

no code implementations16 Dec 2021 Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

In this work, we advance the few-shot learning towards this more challenging scenario, the semantic-based few-shot learning, and propose a method to address the paradigm by capturing the inner semantic relationships using interactive psychometric learning.

Few-Shot Learning

A Comparative Study on Robust Graph Neural Networks to Structural Noises

1 code implementation11 Dec 2021 Zeyu Zhang, Yulong Pei

Although a series of robust GNNs have been proposed, they are evaluated with different structural noises, and it lacks a systematic comparison with consistent settings.

Calibrated Adversarial Training

1 code implementation1 Oct 2021 Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

In this paper, we present the Calibrated Adversarial Training, a method that reduces the adverse effects of semantic perturbations in adversarial training.

Direction-Aggregated Attack for Transferable Adversarial Examples

1 code implementation19 Apr 2021 Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy

Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptible changes to the inputs.

Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks

1 code implementation16 Apr 2021 Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy

Although various approaches have been proposed to solve this problem, two major limitations exist: (1) unsupervised approaches usually work much less efficiently due to the lack of supervisory signal, and (2) existing anomaly detection methods only use local contextual information to detect anomalous nodes, e. g., one- or two-hop information, but ignore the global contextual information.

Self-Supervised Anomaly Detection Supervised Anomaly Detection

Selfish Sparse RNN Training

1 code implementation22 Jan 2021 Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy

Sparse neural networks have been widely applied to reduce the computational demands of training and deploying over-parameterized deep neural networks.

Bridging the Performance Gap between FGSM and PGD Adversarial Training

1 code implementation7 Nov 2020 Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

In addition, it achieves comparable performance of adversarial robustness on MNIST dataset under white-box attack, and it achieves better performance than adv. PGD under white-box attack and effectively defends the transferable adversarial attack on CIFAR-10 dataset.

Adversarial Attack Adversarial Robustness

ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks

1 code implementation30 Sep 2020 Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy

Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection.

Anomaly Detection Intrusion Detection

Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware

4 code implementations26 Jan 2019 Shiwei Liu, Decebal Constantin Mocanu, Amarsagar Reddy Ramapuram Matavalam, Yulong Pei, Mykola Pechenizkiy

Despite the success of ANNs, it is challenging to train and deploy modern ANNs on commodity hardware due to the ever-increasing model size and the unprecedented growth in the data volumes.

struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding

no code implementations25 May 2018 Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy

Almost all previous methods represent a node into a point in space and focus on local structural information, i. e., neighborhood information.

Clustering Network Embedding

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