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Found 17 papers, 8 papers with code
Extract and Merge: Superpixel Segmentation with Regional Attributes
For a certain object in an image, the relationship between its central region and the peripheral region is not well utilized in existing superpixel segmentation methods.
Quantifying Multilingual Performance of Large Language Models Across Languages
However, currently there is no work to quantitatively measure the performance of LLMs in low-resource languages.
Retrieval-Enhanced Knowledge Editing for Multi-Hop Question Answering in Language Models
Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge updates, leading to potentially outdated or inaccurate responses.
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI.
Automated Natural Language Explanation of Deep Visual Neurons with Large Models
Deep neural networks have exhibited remarkable performance across a wide range of real-world tasks.
MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases
Focusing on medical QA using the MedQA-SMILE dataset, we evaluate the impact of different retrieval models and the number of facts provided to the LLM.
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction
By considering embeddings encompassing graph topology and attribute information as reconstruction targets, our model could capture more generalized and comprehensive knowledge.
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
Then, we design two data augmentation schemes on graphs for perturbing structural and feature information, respectively.
Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations
To evaluate our approach, we introduce a cold-start recommendation benchmark, and the results demonstrate that the enhanced small language models can achieve comparable cold-start recommendation performance to that of large models with only $17\%$ of the inference time.
Interpretation of Time-Series Deep Models: A Survey
Deep learning models developed for time-series associated tasks have become more widely researched nowadays.
ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
To tackle these limitations, we propose a novel framework that leverages the power of ChatGPT for specific tasks, such as text classification, while improving its interpretability.
Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification
Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing.
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
On the other hand, the neglect of noise sensitivity differences between image regions by existing decision-based attacks further compromises the efficiency of noise compression, especially for ViTs.
Domain Adaptation without Model Transferring
Existing unsupervised domain adaptation (UDA) methods can achieve promising performance without transferring data from source domain to target domain.
Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals
In this paper, We explore the uncertainty in deep learning to construct the prediction intervals.
Polishing Decision-Based Adversarial Noise With a Customized Sampling
We propose Customized Adversarial Boundary (CAB) attack that uses the current noise to model the sensitivity of each pixel and polish adversarial noise of each image with a customized sampling setting.
Curls & Whey: Boosting Black-Box Adversarial Attacks
On the one hand, existing iterative attacks add noises monotonically along the direction of gradient ascent, resulting in a lack of diversity and adaptability of the generated iterative trajectories.
