Author: Luna Reed

Authors: Elia Bonetto、Aamir Ahmad Paper: https://arxiv.org/abs/2408.10831 Introduction In the realm of deep learning, the scarcity of labeled data in unconventional domains, such as wildlife scenarios, poses a significant challenge. This is particularly true for tasks like 2D pose estimation of animals, where collecting real-world data is often impractical. Zebras, for instance, are not only difficult to capture in diverse poses but also include endangered species like Grévy’s zebra, making data collection even more challenging. The study titled “ZebraPose: Zebra Detection and Pose Estimation using only Synthetic Data” by Elia Bonetto and Aamir Ahmad addresses this issue by leveraging synthetic data…

Authors: Yifei Yang、Runhan Shi、Zuchao Li、Shu Jiang、Bao-Liang Lu、Yang Yang、Hai Zhao Paper: https://arxiv.org/abs/2408.10285 Introduction Retrosynthesis analysis is a cornerstone of synthetic chemistry, particularly in drug discovery and organic chemistry. It involves identifying a set of precursor molecules that can be used to synthesize a target molecule. Despite the development of various computational tools over the past decade, AI-based systems often struggle to generalize across diverse reaction types and explore alternative synthetic pathways. This paper introduces BatGPT-Chem, a large language model with 15 billion parameters, designed to enhance retrosynthesis prediction. By integrating chemical tasks through a unified framework of natural language and SMILES…

Authors: Arya Hadizadeh Moghaddam、Mohsen Nayebi Kerdabadi、Mei Liu、Zijun Yao Paper: https://arxiv.org/abs/2408.10259 Introduction In recent years, the proliferation of Electronic Health Records (EHRs) has enabled significant advancements in personalized healthcare analysis. Sequential prescription recommender systems have emerged as a crucial tool for supporting informed treatment decisions by analyzing complex EHR data accumulated over a patient’s medical history. However, a notable challenge in this domain is the need to disentangle the complex relationships across sequential visits and establish multiple health profiles for the same patient to ensure comprehensive consideration of different medical intents in drug recommendation. To address this challenge, the study introduces…

Authors: Kaiyu He、Zhiyu Chen Paper: https://arxiv.org/abs/2408.10455 Enhancing Rule Learning in Language Agents: The IDEA Approach Introduction Background and Problem Statement The ability to discern and apply rules is a cornerstone of human intelligence. Humans identify patterns, formulate hypotheses, and refine them through interaction with their environment. This process, often involving abduction, deduction, and induction, is crucial for problem-solving and understanding the world. However, while large language models (LLMs) have shown proficiency in isolated reasoning tasks, their holistic rule-learning abilities in interactive environments remain underexplored. To address this gap, researchers from the University of Texas at Dallas have introduced RULEARN, a…

Authors: Jinluan Yang、Zhengyu Chen、Teng Xiao、Wenqiao Zhang、Yong Lin、Kun Kuang Paper: https://arxiv.org/abs/2408.09490 Introduction Graph Neural Networks (GNNs) have become a cornerstone in learning graph-structured representations, primarily excelling in homophilic graphs where connected nodes share similar features and labels. However, their performance significantly drops when applied to heterophilic graphs, where nodes often connect to others from different classes. This challenge has led to the development of Heterophilic Graph Neural Networks (HGNNs), which aim to extend the neighborhood aggregation mechanism to better handle heterophilic structures. Despite advancements, existing HGNNs often assume a consistent data distribution among nodes, neglecting the distribution shifts between training and…

Authors: Kun Li、Xiantao Cai、Jia Wu、Bo Du、Wenbin Hu Paper: https://arxiv.org/abs/2408.09106 Fragment-Masked Molecular Optimization: A Detailed Interpretive Blog Introduction Background Molecular optimization is a pivotal process in drug discovery, focusing on refining molecular structures to enhance drug efficacy and minimize side effects. Traditional methods of molecular optimization often rely on understanding specific drug target structures, which can be limiting due to the scarcity of available targets and the difficulty in capturing clear structures. This has led to the exploration of phenotypic drug discovery (PDD), which does not depend on well-defined target structures and can identify hits with novel polypharmacology signatures. Problem Statement…

Authors: Ravi Raju、Swayambhoo Jain、Bo Li、Jonathan Li、Urmish Thakkar Paper: https://arxiv.org/abs/2408.08808 Introduction Large Language Models (LLMs) have significantly transformed the machine learning landscape, becoming integral to various applications. However, existing benchmarks often fail to capture the diverse behaviors of these models in real-world scenarios. Human evaluations, while considered the gold standard, are time-consuming and expensive. To address these limitations, the concept of LLM-as-a-judge has been introduced, where one LLM evaluates the outputs of another. This paper presents a novel data pipeline to create diverse, domain-specific evaluation sets tailored for LLM-as-a-judge frameworks. Methodology Data Sources The evaluation set is constructed from a variety…

Authors: Vladimir Cherkassky、Eng Hock Lee Paper: https://arxiv.org/abs/2408.06598 Introduction In the paper titled “A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition,” Vladimir Cherkassky and Eng Hock Lee from the University of Minnesota delve into the capabilities and limitations of Large Language Models (LLMs) like GPT-4. They explore the philosophical context of human knowledge acquisition and the Turing test, and provide empirical evaluations of LLMs, particularly focusing on their understanding of abstract concepts and reasoning. Intelligent Machines: A Philosophical Perspective The Debate on AI Intelligence Large Language Models (LLMs) such as GPT-4, Llama 2, and PaLM 2 are often…