Search Results for author:
Found 48 papers, 15 papers with code
Regression and Forecasting of U.S. Stock Returns Based on LSTM
This paper analyses the investment returns of three stock sectors, Manuf, Hitec, and Other, in the U. S. stock market, based on the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model, in order to test the validity of the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model for the three sectors of the market.
Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models
With the growing prevalence of deepfake technology, which can manipulate facial features in images and videos, the potential for fraud in online transactions has escalated.
AnimateAnything: Consistent and Controllable Animation for Video Generation
We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations.
StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions
The integration of Large Language Models (LLMs), especially ChatGPT, into education is poised to revolutionize students' learning experiences by introducing innovative conversational learning methodologies.
fairBERTs: Erasing Sensitive Information Through Semantic and Fairness-aware Perturbations
Pre-trained language models (PLMs) have revolutionized both the natural language processing research and applications.
Advanced Financial Fraud Detection Using GNN-CL Model
The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks.
INDUS: Effective and Efficient Language Models for Scientific Applications
The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints.
Text-Driven Diverse Facial Texture Generation via Progressive Latent-Space Refinement
SDS boosts GANs with more generative modes, while GANs promote more efficient optimization of SDS.
Muted: Multilingual Targeted Offensive Speech Identification and Visualization
Offensive language such as hate, abuse, and profanity (HAP) occurs in various content on the web.
CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels
However, a major doubt about online learning is whether students are as engaged as they are in face-to-face classes.
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry
Findings in this study support the application of PointGAT as a powerful and versatile tool for quantum chemical property prediction that can facilitate high-accuracy modeling for fundamental exploration of chemical space as well as drug design and molecular engineering.
Model Inversion Attack via Dynamic Memory Learning
Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice.
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging
In this paper, we conduct a comprehensive study of over 10 fast adversarial training methods in terms of adversarial robustness and training costs.
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts.
Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training
Developing a practically-robust automatic speech recognition (ASR) is challenging since the model should not only maintain the original performance on clean samples, but also achieve consistent efficacy under small volume perturbations and large domain shifts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility
In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria.
FairRec: Fairness Testing for Deep Recommender Systems
Given the testing report, by adopting a simple re-ranking mitigation strategy on these identified disadvantaged groups, we show that the fairness of DRSs can be significantly improved.
ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing
We also evaluate some robust models including both adversarially trained models and other robust trained models and find that some models show worse robustness against attribute changes than vanilla models.
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection.
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective
Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.
Context-Aware Robust Fine-Tuning
Contrastive Language-Image Pre-trained (CLIP) models have zero-shot ability of classifying an image belonging to "[CLASS]" by using similarity between the image and the prompt sentence "a [CONTEXT] of [CLASS]".
Ranked #1 on
Domain Generalization
on VLCS
RoChBert: Towards Robust BERT Fine-tuning for Chinese
Despite of the superb performance on a wide range of tasks, pre-trained language models (e. g., BERT) have been proved vulnerable to adversarial texts.
Boosting Out-of-distribution Detection with Typical Features
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.
Enhance the Visual Representation via Discrete Adversarial Training
For borrowing the advantage from NLP-style AT, we propose Discrete Adversarial Training (DAT).
Ranked #1 on
Domain Generalization
on Stylized-ImageNet
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification
For the class-center involved triplet loss, the positive and negative samples in each triplet are replaced by their corresponding class centers, which enforces data representations of the same class closer to the class center.
EmotionFlow: Capture the Dialogue Level Emotion Transitions
However, the spread impact of emotions in a conversation is rarely addressed in existing researches.
Ranked #20 on
Emotion Recognition in Conversation
on MELD
Synthetic Target Domain Supervision for Open Retrieval QA
Neural passage retrieval is a new and promising approach in open retrieval question answering.
Counterfactual Adversarial Learning with Representation Interpolation
Deep learning models exhibit a preference for statistical fitting over logical reasoning.
Graph Pattern Loss based Diversified Attention Network for Cross-Modal Retrieval
In this paper, we propose a Graph Pattern Loss based Diversified Attention Network(GPLDAN) for unsupervised cross-modal retrieval to deeply analyze correlations among representations.
Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation
Extensive experiments on two real-world tasks show that AdvGraph exhibits better performance compared with previous work: (i) effective - it significantly strengthens the model robustness even under the adaptive attacks setting without negative impact on model performance over legitimate input; (ii) generic - its key component, i. e., the representation of connotative adversarial knowledge is task-agnostic, which can be reused in any Chinese-based NLP models without retraining; and (iii) efficient - it is a light-weight defense with sub-linear computational complexity, which can guarantee the efficiency required in practical scenarios.
The Open Brands Dataset: Unified brand detection and recognition at scale
To the best of our knowledge, it is the largest dataset for brand detection and recognition with rich annotations.
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training
End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.
Enhancing Neural Models with Vulnerability via Adversarial Attack
2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.
Answer Span Correction in Machine Reading Comprehension
Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair.
Multi-Stage Pre-training for Low-Resource Domain Adaptation
Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain.
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction
To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis
We firstly propose to explore the asking emphasis of a question as a key factor in DQI.
Ranked #1 on
Community Question Answering
on Quora Question Pairs
AutoRemover: Automatic Object Removal for Autonomous Driving Videos
To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically.
The TechQA Dataset
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms
With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded.
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased.
Ranked #1 on
Video Classification
on YouTube-8M
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation
Liver lesion segmentation is a difficult yet critical task for medical image analysis.
AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms
Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.
Deep Features Analysis with Attention Networks
Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc.
Review of Deep Learning
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence.
An influence-based fast preceding questionnaire model for elderly assessments
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.
