Multi-Agent VLMs Guided Self-Training with PNU Loss for Low-Resource Offensive Content Detection

Importance sampling is a technique utilized to enhance the efficiency of deep neural network (DNN) training by minimizing the variance of gradient estimators. This paper introduces a method for estimating variance reduction during DNN training using only minibatches sampled through importance sampling. Additionally, it suggests an optimal minibatch size for automatic learning rate adjustment and presents a metric to quantify the efficiency of importance sampling, supported by theoretical analysis and experiments demonstrating improved training efficiency and model accuracy.

The paper discusses the challenges of test-time prompt tuning for vision-language models, highlighting the issue of prompt optimization bias that can lead to suboptimal performance in downstream tasks. It identifies two main causes: the model's focus on entropy minimization, which may overlook prediction accuracy, and data misalignment between visual and textual modalities. To address these issues, the authors propose a new method called Doubly Debiased Test-Time Prompt Tuning, aimed at improving model performance in zero-shot settings.

The paper titled 'Laplace Learning in Wasserstein Space' explores the manifold hypothesis, which suggests that high-dimensional data typically exists within low-dimensional subspaces. The authors investigate graph-based semi-supervised learning methods, particularly focusing on Laplace Learning in the Wasserstein space. This research extends classical graph-based learning algorithms from finite-dimensional Euclidean spaces to infinite-dimensional contexts. The study proves the variational convergence of discrete graph p-Dirichlet energy to its continuum counterpart and characterizes the Laplac…

The article presents a novel approach to volumetric medical image segmentation called Coordinative Ordinal-Relational Anatomical Learning (CORAL). This method addresses challenges in segmenting medical images by utilizing continuous anatomical similarity instead of relying on hard binary thresholds. CORAL aims to improve the accuracy of segmentation by capturing both local and global anatomical structures, thereby enhancing the understanding of anatomical progression across different patients.

The article presents FlowPath, a novel method for modeling continuous-time dynamics from sparse and irregularly-sampled time series. It addresses the limitations of existing methods that rely on fixed interpolation schemes, which can misrepresent data. FlowPath utilizes an invertible neural flow to create a continuous, data-adaptive manifold, enhancing classification accuracy significantly across 18 benchmark datasets.