Search Results for author:
Found 20 papers, 2 papers with code
Learning New Concepts, Remembering the Old: A Novel Continual Learning
Concept Bottleneck Models (CBMs) enhance model interpretability by introducing human-understandable concepts within the architecture.
Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models
Among XAI techniques, Concept Bottleneck Models (CBMs) enhance transparency by using high-level semantic concepts.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation
The adaptation process has two stages: the first aligns the models on stable features using a mutual information consistency loss, and the second dynamically adjusts the perturbation level based on the loss from the first stage, encouraging the model to explore a broader range of the target domain while preserving existing performance.
Towards Multi-dimensional Explanation Alignment for Medical Classification
The lack of interpretability in the field of medical image analysis has significant ethical and legal implications.
Maintaining Informative Coherence: Migrating Hallucinations in Large Language Models via Absorbing Markov Chains
Large Language Models (LLMs) are powerful tools for text generation, translation, and summarization, but they often suffer from hallucinations-instances where they fail to maintain the fidelity and coherence of contextual information during decoding, sometimes overlooking critical details due to their sampling strategies and inherent biases from training data and fine-tuning discrepancies.
TS-ACL: A Time Series Analytic Continual Learning Framework for Privacy-Preserving and Class-Incremental Pattern Recognition
A primary challenge in this problem is catastrophic forgetting, where the incorporation of new data samples causes the models to forget previously learned information.
Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion
In the rapidly evolving field of deep learning, specialized models have driven significant advancements in tasks such as computer vision and natural language processing.
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models
Despite the transformative impact of deep learning across multiple domains, the inherent opacity of these models has driven the development of Explainable Artificial Intelligence (XAI).
DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving
Recent advancements in autonomous driving have seen a paradigm shift towards end-to-end learning paradigms, which map sensory inputs directly to driving actions, thereby enhancing the robustness and adaptability of autonomous vehicles.
PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning
Fine-grained image classification has witnessed significant advancements with the advent of deep learning and computer vision technologies.
Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling
The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies.
CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution
Spiking neural networks (SNNs) have shown promise in various dynamic visual tasks, yet those ready for practical deployment often lack the compactness and robustness essential in resource-limited and safety-critical settings.
Case-based reasoning approach for diagnostic screening of children with developmental delays
This research adopts a hybrid model combining a CNN-Transformer model with Case-Based Reasoning (CBR) to enhance the screening efficiency for children with developmental delays.
TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks
Our results validate the effectiveness of our approach in addressing the key challenges in time series forecasting, paving the way for more reliable and efficient predictive models in practical applications.
FTS: A Framework to Find a Faithful TimeSieve
The field of time series forecasting has garnered significant attention in recent years, prompting the development of advanced models like TimeSieve, which demonstrates impressive performance.
A Comprehensive Review of Community Detection in Graphs
This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based method, spectral clustering, probabilistic modelling, and deep learning.
Classifying Crime Types using Judgment Documents from Social Media
Experiments show that the proposed method achieves state-of-the-art results on the present dataset.
Cross-Domain Car Detection Model with Integrated Convolutional Block Attention Mechanism
To address these issues, we propose cross-domain Car Detection Model with integrated convolutional block Attention mechanism(CDMA) that we apply to car recognition for autonomous driving and other areas.
Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning
In this work, we propose a deep modal shared information learning module based on the covariance matrix to capture the shared information between modalities.
Multimodal Sentiment Analysis: A Survey
Multimodal sentiment analysis has become an important research area in the field of artificial intelligence.
