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09/17/2023
Mitigating Shortcuts in Language Models with Soft Label Encoding
Recent research has shown that large language models rely on spurious co...
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09/02/2023
Explainability for Large Language Models: A Survey
Large language models (LLMs) have demonstrated impressive capabilities i...
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08/18/2023
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction
Self-supervised learning with masked autoencoders has recently gained po...
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08/08/2023
XGBD: Explanation-Guided Graph Backdoor Detection
Backdoor attacks pose a significant security risk to graph learning mode...
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07/10/2023
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification
With the popularity of deep neural networks (DNNs), model interpretabili...
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07/03/2023
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
The recent contrastive learning methods, due to their effectiveness in r...
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06/29/2023
Towards Personalized Cold-Start Recommendation with Prompts
Recommender systems play a crucial role in helping users discover inform...
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06/20/2023
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications
This paper explores new frontiers in agricultural natural language proce...
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06/09/2023
Efficient GNN Explanation via Learning Removal-based Attribution
As Graph Neural Networks (GNNs) have been widely used in real-world appl...
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05/23/2023
Interpretation of Time-Series Deep Models: A Survey
Deep learning models developed for time-series associated tasks have bec...
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05/22/2023
DEGREE: Decomposition Based Explanation For Graph Neural Networks
Graph Neural Networks (GNNs) are gaining extensive attention for their a...
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05/05/2023
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks
Recently, the Segment Anything Model (SAM) has gained significant attent...
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04/24/2023
Artificial General Intelligence (AGI) for Education
Artificial general intelligence (AGI) has gained global recognition as a...
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04/21/2023
Interactive System-wise Anomaly Detection
Anomaly detection, where data instances are discovered containing featur...
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04/13/2023
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
Large pre-trained models, also known as foundation models (FMs), are tra...
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04/12/2023
AGI for Agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a varie...
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03/21/2023
Black-box Backdoor Defense via Zero-shot Image Purification
Backdoor attacks inject poisoned data into the training set, resulting i...
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03/20/2023
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
The huge supporting training data on the Internet has been a key factor ...
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03/13/2023
A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
While deep learning has achieved great success on various tasks, the tas...
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02/24/2023
NoPPA: Non-Parametric Pairwise Attention Random Walk Model for Sentence Representation
We propose a novel non-parametric/un-trainable language model, named Non...
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01/20/2023
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education
Developing models to automatically score students' written responses to ...
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12/23/2022
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Graph neural networks (GNNs) have received remarkable success in link pr...
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11/25/2022
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
Graph Neural Networks (GNNs) have emerged as the leading paradigm for so...
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11/23/2022
SEAT: Stable and Explainable Attention
Currently, attention mechanism becomes a standard fixture in most state-...
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09/21/2022
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
Graph anomaly detection (GAD) is a vital task since even a few anomalies...
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07/20/2022
Mitigating Algorithmic Bias with Limited Annotations
Existing work on fairness modeling commonly assumes that sensitive attri...
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01/07/2022
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
We introduce a novel masked graph autoencoder (MGAE) framework to perfor...
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11/08/2021
Defense Against Explanation Manipulation
Explainable machine learning attracts increasing attention as it improve...
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11/04/2021
Modeling Techniques for Machine Learning Fairness: A Survey
Machine learning models are becoming pervasive in high-stakes applicatio...
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08/30/2021
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by re...
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08/11/2021
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Graph Neural Networks (GNNs) have recently demonstrated superior capabil...
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03/22/2021
ExAD: An Ensemble Approach for Explanation-based Adversarial Detection
Recent research has shown Deep Neural Networks (DNNs) to be vulnerable t...
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02/18/2021
Dynamic Memory based Attention Network for Sequential Recommendation
Sequential recommendation has become increasingly essential in various o...
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02/18/2021
Sparse-Interest Network for Sequential Recommendation
Recent methods in sequential recommendation focus on learning an overall...
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01/18/2021
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
With the wide use of deep neural networks (DNN), model interpretability ...
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09/16/2020
Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability
Recent years have witnessed an increasing number of interpretation metho...
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08/21/2020
Explainable Recommender Systems via Resolving Learning Representations
Recommender systems play a fundamental role in web applications in filte...
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06/15/2020
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
With the widespread use of deep neural networks (DNNs) in high-stake app...
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04/23/2020
Adversarial Machine Learning: An Interpretation Perspective
Recent years have witnessed the significant advances of machine learning...
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03/04/2020
Learning to Hash with Graph Neural Networks for Recommender Systems
Graph representation learning has attracted much attention in supporting...
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08/13/2019
Learning Credible Deep Neural Networks with Rationale Regularization
Recent explainability related studies have shown that state-of-the-art D...
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08/11/2019
Deep Structured Cross-Modal Anomaly Detection
Anomaly detection is a fundamental problem in data mining field with man...
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05/25/2019
Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
Networks have been widely used as the data structure for abstracting rea...
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04/18/2019
Deep Representation Learning for Social Network Analysis
Social network analysis is an important problem in data mining. A fundam...
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03/27/2019
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
RNN models have achieved the state-of-the-art performance in a wide rang...
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07/31/2018
Techniques for Interpretable Machine Learning
Interpretable machine learning tackles the important problem that humans...
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03/19/2018
Towards Explanation of DNN-based Prediction with Guided Feature Inversion
While deep neural networks (DNN) have become an effective computational ...
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11/28/2017