Author: Oliver Lewis

Authors: Yuankun Xie、Chenxu Xiong、Xiaopeng Wang、Zhiyong Wang、Yi Lu、Xin Qi、Ruibo Fu、Yukun Liu、Zhengqi Wen、Jianhua Tao、Guanjun Li、Long Ye Paper: https://arxiv.org/abs/2408.10853 Evaluating the Effectiveness of Current Deepfake Audio Detection Models on ALM-based Deepfake Audio Introduction The rapid advancements in large language models and audio neural codecs have significantly lowered the barrier to creating deepfake audio. These advancements have led to the emergence of Audio Language Models (ALMs), which can generate highly realistic and diverse types of deepfake audio, posing severe threats to society. The ability to generate deepfake audio that is indistinguishable from real audio has raised concerns about fraud, misleading public opinion, and privacy…

Authors: Coşku Can Horuz、Matthias Karlbauer、Timothy Praditia、Sergey Oladyshkin、Wolfgang Nowak、Sebastian Otte Paper: https://arxiv.org/abs/2408.10649 Introduction Background Spatiotemporal partial differential equations (PDEs) are fundamental in modeling various scientific and engineering phenomena. These equations describe how physical quantities evolve over space and time, making them crucial for understanding complex systems such as fluid dynamics, heat transfer, and wave propagation. Traditional approaches to solving PDEs have relied heavily on numerical methods grounded in physics. However, the advent of machine learning (ML) has introduced new possibilities for enhancing these models. Problem Statement Despite the advancements in both physics-based and machine learning models, there remains a gap in…

Authors: Yuanhao Zeng、Fei Ren、Xinpeng Zhou、Yihang Wang、Yingxia Shao Paper: https://arxiv.org/abs/2408.10841 Introduction Large Language Models (LLMs) have shown exceptional capabilities across various tasks, but their application in specific domains often necessitates additional fine-tuning. Instruction tuning has become a popular method to address this need, aiming to enable LLMs to follow task-specific instructions. However, research indicates that instruction tuning primarily fits models to specific task formats rather than imparting new knowledge or capabilities. This limitation is particularly evident with smaller datasets, contradicting the ideal scenario where LLMs learn adaptable downstream task capabilities. The core issue arises from the discrepancy between instruction tuning data…

Authors: Yerim Jeon、Subeen Lee、Jihwan Kim、Jae-Pil Heo Paper: https://arxiv.org/abs/2408.09734 Introduction Object counting has seen significant advancements with the advent of deep learning. Traditional methods, however, are often limited to specific categories like humans or cars and require extensive labeled data. Few-shot object counting aims to address these limitations by enabling the counting of arbitrary objects in a query image based on a few exemplar images. The prevalent extract-and-match approach, while effective, suffers from a target confusion problem, especially in multi-class scenarios. This is because query and exemplar features are extracted independently, leading to insufficient target awareness. To tackle this, the authors…

Authors: Arwen Bradley、Preetum Nakkiran Paper: https://arxiv.org/abs/2408.09000 Classifier-Free Guidance is a Predictor-Corrector: A Detailed Interpretive Blog Introduction In the realm of text-to-image diffusion models, Classifier-Free Guidance (CFG) has emerged as a pivotal method for conditional sampling. Despite its widespread adoption, the theoretical underpinnings of CFG remain somewhat ambiguous. This paper, authored by Arwen Bradley and Preetum Nakkiran, delves into the theoretical foundations of CFG, aiming to dispel common misconceptions and provide a clearer understanding of its mechanics. The authors propose that CFG can be viewed as a predictor-corrector method, which they term Predictor-Corrector Guidance (PCG). Related Work Diffusion Models and Conditional…

Authors: Chengyu Song、Linru Ma、Jianming Zheng、Jinzhi Liao、Hongyu Kuang、Lin Yang Paper: https://arxiv.org/abs/2408.08902 Introduction Insider threats pose a significant challenge in cybersecurity, as they are often carried out by authorized users with legitimate access to sensitive information. Traditional Insider Threat Detection (ITD) methods, which rely on monitoring and analyzing logs, face issues such as overfitting and lack of interpretability. The emergence of Large Language Models (LLMs) offers new possibilities for ITD, leveraging their extensive commonsense knowledge and multi-step reasoning capabilities. However, LLMs face challenges such as handling diverse activity types, overlong log files, and faithfulness hallucination. To address these challenges, the paper introduces…