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Found 12 papers, 3 papers with code
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing
Through empirical studies on the capability for performing backdoor attack in large pre-trained models ($\textit{e. g.,}$ ViT), we find the following unique challenges of attacking large pre-trained models: 1) the inability to manipulate or even access large training datasets, and 2) the substantial computational resources required for training or fine-tuning these models.
Causal Inference with Latent Variables: Recent Advances and Future Prospectives
Depending on the type of unobserved variables and the specific CI task, various consequences can be incurred if these latent variables are carelessly handled, such as biased estimation of causal effects, incomplete understanding of causal mechanisms, lack of individual-level causal consideration, etc.
UFID: A Unified Framework for Input-level Backdoor Detection on Diffusion Models
Diffusion Models are vulnerable to backdoor attacks, where malicious attackers inject backdoors by poisoning some parts of the training samples during the training stage.
Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation
Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval. Traditional methods typically employ either classification, which dividing the Earth surface into grid cells and classifying images accordingly, or retrieval, which identifying locations by matching images with a database of image-location pairs.
Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions
Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields of study with significant implications for artificial intelligence.
Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction
To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task.
XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications
Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data.
PharmacyGPT: The AI Pharmacist
In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists.
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition.
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks
Recently, the Segment Anything Model (SAM) has gained significant attention as an image segmentation foundation model due to its strong performance on various downstream tasks.
Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models
As image databases grow each year, performing automatic segmentation with deep learning models has gradually become the standard approach for processing the data.
