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Found 28 papers, 14 papers with code
BuDDIE: A Business Document Dataset for Multi-task Information Extraction
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.
DocLLM: A layout-aware generative language model for multimodal document understanding
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.
A Structural-Clustering Based Active Learning for Graph Neural Networks
To address this problem, we propose the Structural-Clustering PageRank method for improved Active learning (SPA) specifically designed for graph-structured data.
Heterophily-Based Graph Neural Network for Imbalanced Classification
This leads to suboptimal performance of standard GNNs on imbalanced graphs.
Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams
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
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
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.
DF2M: An Explainable Deep Bayesian Nonparametric Model for High-Dimensional Functional Time Series
In this paper, we present Deep Functional Factor Model (DF2M), a Bayesian nonparametric model for analyzing high-dimensional functional time series.
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
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.
Ranked #1 on
Question Answering
on ConvFinQA
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
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).
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering
Active Learning (AL) techniques have proven to be highly effective in reducing data labeling costs across a range of machine learning tasks.
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
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
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.
A Comparative Study on Robust Graph Neural Networks to Structural Noises
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
In this paper, we present the Calibrated Adversarial Training, a method that reduces the adverse effects of semantic perturbations in adversarial training.
A Survey on Role-Oriented Network Embedding
A wide variety of NE methods focus on the proximity of networks.
On Generalization of Graph Autoencoders with Adversarial Training
Adversarial training is an approach for increasing model's resilience against adversarial perturbations.
Direction-Aggregated Attack for Transferable Adversarial Examples
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
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
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
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.
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection.
Simultaneous Identification of Tweet Purpose and Position
Tweet classification has attracted considerable attention recently.
Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware
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
Almost all previous methods represent a node into a point in space and focus on local structural information, i. e., neighborhood information.
