Author: Caleb Martin

Authors: Kevin Kam Fung Yuen Paper: https://arxiv.org/abs/2408.10572 Explainable Image Classification for Dementia Stages Using CNN and Grad-CAM Introduction Dementia is a debilitating condition characterized by a decline in memory, language, problem-solving, and other cognitive abilities. Alzheimer’s disease is the most common form of dementia, accounting for 60-80% of cases. The progression of dementia is typically categorized into three stages: early (mild), middle (moderate), and late (severe). Accurate classification of these stages is crucial for effective treatment and management. This blog post delves into a study that employs Convolutional Neural Networks (CNN) and Gradient-weighted Class Activation Mapping (Grad-CAM) to classify dementia…

Authors: Bart Bogaerts、Angelos Charalambidis、Giannos Chatziagapis、Babis Kostopoulos、Samuele Pollaci、Panos Rondogiannis Paper: https://arxiv.org/abs/2408.10563 Introduction Higher-order logic programming languages have been shown to possess powerful expressive capabilities and elegant semantic properties. These languages extend classical first-order logic programming, allowing for more complex abstractions and manipulations. However, defining a stable model semantics for higher-order logic programs that generalizes the seminal work of Gelfond and Lifschitz (1988) has been a challenging task. This paper proposes a stable model semantics for higher-order logic programs using Approximation Fixpoint Theory (AFT), a formalism that has successfully unified semantics across various non-monotonic formalisms. Related Work Extensions of Logic Programming Several…

Authors: Kai Liu、Kang You、Pan Gao Paper: https://arxiv.org/abs/2408.10543 Introduction Background Point clouds, consisting of numerous discrete points with coordinates (x, y, z) and optional attributes, offer a flexible representation of diverse 3D shapes. They are extensively applied in various fields such as autonomous driving, game rendering, and robotics. With the rapid advancement of point cloud acquisition technologies and 3D applications, effective point cloud compression techniques have become indispensable to reduce transmission and storage costs. Problem Statement Traditional point cloud compression methods, such as G-PCC and V-PCC, have limitations in capturing the intricate diversity of point cloud shapes, often yielding blurry and…

Authors: Yihang Wang、Xu Huang、Bowen Tian、Yixing Fan、Jiafeng Guo Paper: https://arxiv.org/abs/2408.10497 Introduction In recent years, the rapid development of generative large language models (LLMs) has revolutionized many traditional technologies. These models, such as ChatGPT, have demonstrated remarkable success in various industrial tasks by leveraging rich contextual information. Techniques like In-Context Learning (ICL), Retrieval-Augmented Generation (RAG), and the use of agents have been instrumental in enabling these models to understand and generate contextually relevant content, addressing complex problems through multi-turn dialogues. However, as tasks become increasingly complex, the required context length for ICL also grows, leading to two significant challenges: 1. Higher inference…

Authors: Niyar R Barman、Krish Sharma、Ashhar Aziz、Shashwat Bajpai、Shwetangshu Biswas、Vasu Sharma、Vinija Jain、Aman Chadha、Amit Sheth、Amitava Das Paper: https://arxiv.org/abs/2408.10446 Introduction Background The rapid advancement of text-to-image generation systems, such as Stable Diffusion, Midjourney, Imagen, and DALL-E, has significantly increased the production of AI-generated visual content. This surge has raised concerns about the potential misuse of these images, particularly in the context of misinformation. To mitigate these risks, companies like Meta and Google have implemented watermarking techniques on AI-generated images. However, the robustness of these watermarking methods against sophisticated attacks remains questionable. Problem Statement This study investigates the vulnerability of current image watermarking techniques to…

Authors: Yuyan Chen、Chenwei Wu、Songzhou Yan、Panjun Liu、Haoyu Zhou、Yanghua Xiao Paper: https://arxiv.org/abs/2408.10947 Introduction Background Large Language Models (LLMs) have shown remarkable performance in various natural language processing (NLP) tasks, including question answering, information retrieval, reasoning, and text generation. Their potential extends beyond general NLP applications into specialized domains such as education. In the educational field, LLMs can serve as automated teaching aids, helping to alleviate the burden on human educators by recommending courses, generating practice problems, and identifying areas where students need improvement. Problem Statement While LLMs have been extensively evaluated for their comprehension and problem-solving skills, their capability as educators, particularly…

Authors: Muhammad Najib、Giuseppe Perelli Paper: https://arxiv.org/abs/2408.10074 Synthesis of Reward Machines for Multi-Agent Equilibrium Design: An Interpretive Blog Introduction In the realm of game theory, mechanism design has long been a cornerstone for crafting games that yield desired outcomes. However, a nuanced yet distinct concept known as equilibrium design has emerged, where the designer’s authority is more constrained. Instead of creating games from scratch, the designer modifies existing incentive structures to achieve specific outcomes. This paper delves into equilibrium design using dynamic incentive structures called reward machines. The study employs weighted concurrent game structures with mean-payoff objectives to model the game…

Authors: Narayanan PP、Anantharaman Palacode Narayana Iyer Paper: https://arxiv.org/abs/2408.09434 HySem: A Context Length Optimized LLM Pipeline for Unstructured Tabular Extraction Introduction In the pharmaceutical industry, regulatory compliance reporting often involves detailed tables that are under-utilized beyond their initial compliance purposes due to their unstructured format. Extracting and semantically representing this tabular data is challenging due to the diverse presentations of tables. Large Language Models (LLMs) have shown potential for semantic representation but face challenges related to accuracy and context size limitations. This study introduces HySem, a pipeline that employs a novel context length optimization technique to generate accurate semantic JSON representations…

Authors: Jinming Nian、Zhiyuan Peng、Qifan Wang、Yi Fang Paper: https://arxiv.org/abs/2408.08444 Introduction Open-domain question answering (OpenQA) systems aim to provide natural language answers to user queries. Traditionally, these systems use a “Retriever-Reader” architecture, where the retriever fetches relevant passages, and the reader generates answers. Despite the advancements in large language models (LLMs) like GPT-4 and LLaMA, these models face limitations such as fixed parametric knowledge and the tendency to generate non-factual responses, known as hallucinations. To address these issues, Retrieval-Augmented Generation (RAG) systems have been explored. RAG systems enhance LLMs by retrieving relevant information from external sources. However, training dense retrievers in RAG…

Authors: Jacob Kauffmann、Jonas Dippel、Lukas Ruff、Wojciech Samek、Klaus-Robert Müller、Grégoire Montavon Paper: https://arxiv.org/abs/2408.08041 The Clever Hans Effect in Unsupervised Learning: An Interpretive Blog Introduction Unsupervised learning has become a cornerstone of modern AI systems, providing foundational models that support a wide array of downstream applications. However, the reliability of these models is crucial, as their predictions can significantly impact subsequent tasks. This paper investigates the prevalence of the Clever Hans (CH) effect in unsupervised learning models, where models make correct predictions for the wrong reasons. Using Explainable AI techniques, the authors reveal the widespread nature of CH effects in unsupervised learning and propose…