destroR: Attacking Transfer Models with Obfuscous Examples to Discard Perplexity

The article explores the approximation and learning capabilities of neural networks in relation to their VC dimension, focusing on high-dimensional geometry and statistical learning theory. It highlights that while a finite VC dimension is beneficial for uniform convergence of empirical errors, it may not be ideal for approximating functions from a probability distribution relevant to specific applications. The study demonstrates that errors in approximation and empirical errors behave almost deterministically for networks with finite VC dimensions when processing large datasets.

The X-VMamba framework introduces a controllability-based interpretability approach for State Space Models (SSMs), particularly the Mamba architecture. This framework aims to enhance understanding of how Vision SSMs process spatial information, addressing the challenges posed by the lack of transparent mechanisms in existing models. Two methods are proposed: a Jacobian-based method for general SSM architectures and a Gramian-based approach for diagonal SSMs, both designed to measure the influence of input sequences on internal state dynamics efficiently.

AtlasMorph is a proposed machine learning framework designed to create conditional deformable templates for brain MRI analysis. These templates serve as prototypical anatomical representations for populations, enhancing medical image analysis tasks such as registration and segmentation. The framework utilizes convolutional registration neural networks to generate templates based on subject-specific attributes like age and sex, addressing the limitations of existing templates that may not accurately represent diverse populations.

Deceptive reviews can mislead consumers and damage businesses, undermining trust in online marketplaces. This study utilizes large language models (LLMs) to translate machine-learned lexical cues into understandable language phenomena that can distinguish deceptive reviews from genuine ones. The findings indicate that these language phenomena are empirically grounded, generalizable across domains, and more predictive than those derived from LLMs' prior knowledge or in-context learning, potentially aiding consumers in evaluating online review credibility.

Machine learning models often reflect biases found in their training data, resulting in unfair treatment of minority groups. While various bias mitigation techniques exist, they typically involve utility costs and require organizational support. This article introduces the concept of Algorithmic Collective Action, where end-users from minority groups can collaboratively relabel their data to promote fairness without changing the firm's training process. Three model-agnostic methods for effective relabeling are proposed and validated on real-world datasets, demonstrating that a minority subgroup can significantly reduce unfairness with minimal impact on prediction error.

Accurate precipitation forecasting is increasingly crucial due to climate change. Recent machine learning approaches have emerged as alternatives to traditional methods like numerical weather prediction. However, many of these methods still use standard loss functions, which may not perform well during prolonged dry spells when precipitation is below the threshold. To overcome this issue, a new advanced torrential (AT) loss function is introduced, formulated as a quadratic unconstrained binary optimization (QUBO), which aims to enhance forecasting accuracy.

The article reviews modern optimization methods for training neural networks, focusing on efficiency and scalability. It presents state-of-the-art algorithms within a unified framework, emphasizing the need to adapt to specific problem structures. The content is designed for both practitioners and researchers interested in the latest advancements in this field.