Author: Ben Cooper

Authors: Lianghao Xia、Chao Huang Paper: https://arxiv.org/abs/2408.10700 Introduction The proliferation of relational data structured as graphs has highlighted the need for advanced graph learning models with exceptional generalization capabilities. Traditional graph learning models often require extensive fine-tuning and struggle to adapt to diverse graph structures and distributions encountered in real-world applications. This limitation poses a significant barrier to the widespread adoption of graph learning technologies. Inspired by the success of foundation models in vision and language domains, the concept of a versatile graph foundation model, such as AnyGraph, holds immense potential to unlock new frontiers in graph learning. AnyGraph is designed…

Authors: Yunxin Tang、Siyuan Tang、Jian Zhang、Hao Chen Paper: https://arxiv.org/abs/2408.10600 Breast Tumor Classification Using Self-Supervised Contrastive Learning from Ultrasound Videos Introduction Breast cancer is the most common cancer among women and the second leading cause of cancer-related deaths. Early detection through screening significantly reduces mortality and treatment costs. Ultrasonography is a widely used method for breast cancer detection due to its affordability, non-invasiveness, and real-time imaging capabilities. However, the interpretation of ultrasound images can be challenging and time-consuming for radiologists. Automatic diagnosis systems based on deep learning have the potential to alleviate this burden by improving diagnostic accuracy and reducing variability. Despite…

Authors: Jingyun Chen、Martin King、Yading Yuan Paper: https://arxiv.org/abs/2408.10275 FedKBP: Federated Dose Prediction Framework for Knowledge-Based Planning in Radiation Therapy Introduction Radiation therapy is a cornerstone in the treatment of cancer, and the demand for efficient and effective treatment planning is ever-growing. Knowledge-based planning (KBP) has emerged as a promising approach to streamline the planning process and reduce treatment lead time. Central to KBP is the dose prediction, which automatically estimates patient-specific dose distribution for treatment plan evaluation and optimization. However, the challenge of limited training data availability has been a significant hurdle in the development of robust dose prediction models. Recent…

Authors: Florian Grötschla、Joël Mathys、Christoffer Raun、Roger Wattenhofer Paper: https://arxiv.org/abs/2408.11042 Introduction Machine learning has made significant strides in various domains, yet it still struggles with generalizing concepts and extrapolating to unseen inputs. For instance, while large language models can generate impressive text, they often fail at tasks requiring a deep understanding of algorithms, such as multiplying large numbers. This gap highlights the need for teaching machines “algorithmic thinking”—the ability to distill and apply algorithms across different situations. Finite State Automata (FSA) represent one of the simplest forms of algorithms, transitioning between states based on predefined rules. When multiple FSAs are networked, they…

Authors: Zhijun Jia、Huaying Xue、Xiulian Peng、Yan Lu Paper: https://arxiv.org/abs/2408.10096 Introduction Accent conversion (AC) is a challenging task in speech processing that aims to transform the pronunciation and prosody of a speaker’s voice to match a target accent while preserving the linguistic content and speaker identity. This technology is crucial for improving communication between speakers of different accents, as it can help break down barriers of understanding. However, the lack of parallel data, where the same speaker utters the same content in different accents, poses a significant challenge for AC systems. In this study, the authors propose a novel two-stage generative framework…

Authors: Weiqi Wu、Hongqiu Wu、Hai Zhao Paper: https://arxiv.org/abs/2408.09853 Self-Directed Turing Test for Large Language Models: An In-Depth Analysis Introduction The Turing Test, introduced by Alan Turing in 1950, has long been a benchmark for evaluating whether a machine can exhibit human-like behavior in natural language conversations. Traditionally, this test involves a human evaluator engaging in a text-based conversation with both a human and an AI, attempting to distinguish between the two. However, the conventional Turing Test has several limitations, such as its rigid dialogue format and the need for continuous human involvement, which restricts the test duration and fails to reflect…

Authors: Shiqi Wang、Zhengze Zhang、Rui Zhao、Fei Tan、Cam Tu Nguyen Paper: https://arxiv.org/abs/2408.09385 Introduction Large Language Models (LLMs) have revolutionized natural language processing (NLP) by providing unprecedented capabilities in understanding, generating, and translating human language. However, aligning these models with human preferences, such as truthfulness, harmlessness, and helpfulness, remains a significant challenge. Traditional methods like Reinforcement Learning with Human Feedback (RLHF) have proven effective but are resource-intensive and complex. This study introduces a novel approach called Reward Difference Optimization (RDO) to enhance offline RLHF methods by providing more accurate supervision signals. Related Work Reinforcement Learning with Human Feedback (RLHF) RLHF is a method…

Authors: Xinglin Wang、Peiwen Yuan、Shaoxiong Feng、Yiwei Li、Boyuan Pan、Heda Wang、Yao Hu、Kan Li Paper: https://arxiv.org/abs/2408.09150 Tracking Cognitive Development of Large Language Models: An In-depth Analysis of CogLM Introduction Background Large Language Models (LLMs) have recently demonstrated remarkable capabilities across a wide range of Natural Language Processing (NLP) tasks, such as text comprehension, reasoning, code generation, and solving mathematical problems. Despite these advancements, there is limited understanding of the cognitive development of these models. This gap in knowledge poses challenges in comprehending the evolution of LLMs’ abilities and may hinder their future development. Problem Statement Inspired by Piaget’s Theory of Cognitive Development (PTC), which…

Authors: Karthik Shivashankar、Mili Orucevic、Maren Maritsdatter Kruke、Antonio Martini Paper: https://arxiv.org/abs/2408.09128 Introduction Technical Debt (TD) is a critical concept in software development, representing the future cost of additional work due to choosing suboptimal solutions or evolving requirements. Managing TD is essential for maintaining code quality, reducing long-term maintenance costs, and ensuring the overall health of software projects. This study, conducted by Karthik Shivashankar, Mili Orucevic, Maren Maritsdatter Kruke, and Antonio Martini, advances TD classification using transformer-based models. The research focuses on enhancing the accuracy and efficiency of TD identification in large-scale software development by employing multiple binary classifiers combined through ensemble learning.…

Authors: Zhiqiang Wang、Xinyue Yu、Qianli Huang、Yongguang Gong Paper: https://arxiv.org/abs/2408.08909 Introduction In the era of big data, privacy concerns have become paramount, especially with the advent of federated learning (FL). Introduced by Google in 2016, federated learning allows multiple clients to collaboratively train a machine learning model without sharing their raw data. This decentralized approach mitigates privacy risks and reduces communication complexity. However, even though raw data is not exchanged, the computed results can still leak sensitive information. Differential privacy (DP) is a common technique used to address these privacy concerns in federated learning. However, traditional DP methods with fixed privacy budgets…