arXiv - CS - Machine Learning最新文献_第8页

DEMAU: Decompose, Explore, Model and Analyse Uncertainties DEMAU：分解、探索、模拟和分析不确定性

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.08105

Arthur Hoarau, Vincent Lemaire

引用次数: 0

XMOL: Explainable Multi-property Optimization of Molecules XMOL：可解释的分子多性能优化

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07786

Aye Phyu Phyu Aung, Jay Chaudhary, Ji Wei Yoon, Senthilnath Jayavelu

{"title":"XMOL: Explainable Multi-property Optimization of Molecules","authors":"Aye Phyu Phyu Aung, Jay Chaudhary, Ji Wei Yoon, Senthilnath Jayavelu","doi":"arxiv-2409.07786","DOIUrl":"https://doi.org/arxiv-2409.07786","url":null,"abstract":"Molecular optimization is a key challenge in drug discovery and material\u0000science domain, involving the design of molecules with desired properties.\u0000Existing methods focus predominantly on single-property optimization,\u0000necessitating repetitive runs to target multiple properties, which is\u0000inefficient and computationally expensive. Moreover, these methods often lack\u0000transparency, making it difficult for researchers to understand and control the\u0000optimization process. To address these issues, we propose a novel framework,\u0000Explainable Multi-property Optimization of Molecules (XMOL), to optimize\u0000multiple molecular properties simultaneously while incorporating\u0000explainability. Our approach builds on state-of-the-art geometric diffusion\u0000models, extending them to multi-property optimization through the introduction\u0000of spectral normalization and enhanced molecular constraints for stabilized\u0000training. Additionally, we integrate interpretive and explainable techniques\u0000throughout the optimization process. We evaluated XMOL on the real-world\u0000molecular datasets i.e., QM9, demonstrating its effectiveness in both single\u0000property and multiple properties optimization while offering interpretable\u0000results, paving the way for more efficient and reliable molecular design.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding BLens：利用集合嵌入对二进制函数进行对比式字幕制作

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07889

Tristan Benoit, Yunru Wang, Moritz Dannehl, Johannes Kinder

{"title":"BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding","authors":"Tristan Benoit, Yunru Wang, Moritz Dannehl, Johannes Kinder","doi":"arxiv-2409.07889","DOIUrl":"https://doi.org/arxiv-2409.07889","url":null,"abstract":"Function names can greatly aid human reverse engineers, which has spurred\u0000development of machine learning-based approaches to predicting function names\u0000in stripped binaries. Much current work in this area now uses transformers,\u0000applying a metaphor of machine translation from code to function names. Still,\u0000function naming models face challenges in generalizing to projects completely\u0000unrelated to the training set. In this paper, we take a completely new approach\u0000by transferring advances in automated image captioning to the domain of binary\u0000reverse engineering, such that different parts of a binary function can be\u0000associated with parts of its name. We propose BLens, which combines multiple\u0000binary function embeddings into a new ensemble representation, aligns it with\u0000the name representation latent space via a contrastive learning approach, and\u0000generates function names with a transformer architecture tailored for function\u0000names. In our experiments, we demonstrate that BLens significantly outperforms\u0000the state of the art. In the usual setting of splitting per binary, we achieve\u0000an $F_1$ score of 0.77 compared to 0.67. Moreover, in the cross-project\u0000setting, which emphasizes generalizability, we achieve an $F_1$ score of 0.46\u0000compared to 0.29.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alignment with Preference Optimization Is All You Need for LLM Safety 与偏好优化保持一致是保证 LLM 安全的必要条件

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07772

Reda Alami, Ali Khalifa Almansoori, Ahmed Alzubaidi, Mohamed El Amine Seddik, Mugariya Farooq, Hakim Hacid

引用次数: 0

Improve Machine Learning carbon footprint using Nvidia GPU and Mixed Precision training for classification algorithms 使用 Nvidia GPU 和混合精度训练分类算法，改善机器学习的碳足迹

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07853

Andrew Antonopoulos

{"title":"Improve Machine Learning carbon footprint using Nvidia GPU and Mixed Precision training for classification algorithms","authors":"Andrew Antonopoulos","doi":"arxiv-2409.07853","DOIUrl":"https://doi.org/arxiv-2409.07853","url":null,"abstract":"This study was part of my dissertation for my master degree and compares the\u0000power consumption using the default floating point (32bit) and Nvidia mixed\u0000precision (16bit and 32bit) while training a classification ML model. A custom\u0000PC with specific hardware was built to perform the experiments, and different\u0000ML hyper-parameters, such as batch size, neurons, and epochs, were chosen to\u0000build Deep Neural Networks (DNN). Additionally, various software was used\u0000during the experiments to collect the power consumption data in Watts from the\u0000Graphics Processing Unit (GPU), Central Processing Unit (CPU), Random Access\u0000Memory (RAM) and manually from a wattmeter connected to the wall. A\u0000benchmarking test with default hyper parameter values for the DNN was used as a\u0000reference, while the experiments used a combination of different settings. The\u0000results were recorded in Excel, and descriptive statistics were chosen to\u0000calculate the mean between the groups and compare them using graphs and tables.\u0000The outcome was positive when using mixed precision combined with specific\u0000hyper-parameters. Compared to the benchmarking, the optimisation for the\u0000classification reduced the power consumption between 7 and 11 Watts. Similarly,\u0000the carbon footprint is reduced because the calculation uses the same power\u0000consumption data. Still, a consideration is required when configuring\u0000hyper-parameters because it can negatively affect hardware performance.\u0000However, this research required inferential statistics, specifically ANOVA and\u0000T-test, to compare the relationship between the means. Furthermore, tests\u0000indicated no statistical significance of the relationship between the\u0000benchmarking and experiments. However, a more extensive implementation with a\u0000cluster of GPUs can increase the sample size significantly, as it is an\u0000essential factor and can change the outcome of the statistical analysis.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large Language Models are Pattern Matchers: Editing Semi-Structured and Structured Documents with ChatGPT 大语言模型是模式匹配器：使用 ChatGPT 编辑半结构化和结构化文档

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07732

Irene Weber

引用次数: 0

Heterogeneous Sheaf Neural Networks 异构片状神经网络

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.08036

Luke Braithwaite, Iulia Duta, Pietro Liò

引用次数: 0

FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection FPMT：用于交通事故检测的增强型半监督模型

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07839

Xinying Lu, Jianli Xiao

引用次数: 0

A framework for measuring the training efficiency of a neural architecture 衡量神经架构训练效率的框架

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.07925

Eduardo Cueto-Mendoza, John D. Kelleher

引用次数: 0

Click2Mask: Local Editing with Dynamic Mask Generation Click2Mask：本地编辑与动态蒙版生成

arXiv - CS - Machine Learning Pub Date : 2024-09-12 DOI: arxiv-2409.08272

Omer Regev, Omri Avrahami, Dani Lischinski

{"title":"Click2Mask: Local Editing with Dynamic Mask Generation","authors":"Omer Regev, Omri Avrahami, Dani Lischinski","doi":"arxiv-2409.08272","DOIUrl":"https://doi.org/arxiv-2409.08272","url":null,"abstract":"Recent advancements in generative models have revolutionized image generation\u0000and editing, making these tasks accessible to non-experts. This paper focuses\u0000on local image editing, particularly the task of adding new content to a\u0000loosely specified area. Existing methods often require a precise mask or a\u0000detailed description of the location, which can be cumbersome and prone to\u0000errors. We propose Click2Mask, a novel approach that simplifies the local\u0000editing process by requiring only a single point of reference (in addition to\u0000the content description). A mask is dynamically grown around this point during\u0000a Blended Latent Diffusion (BLD) process, guided by a masked CLIP-based\u0000semantic loss. Click2Mask surpasses the limitations of segmentation-based and\u0000fine-tuning dependent methods, offering a more user-friendly and contextually\u0000accurate solution. Our experiments demonstrate that Click2Mask not only\u0000minimizes user effort but also delivers competitive or superior local image\u0000manipulation results compared to SoTA methods, according to both human\u0000judgement and automatic metrics. Key contributions include the simplification\u0000of user input, the ability to freely add objects unconstrained by existing\u0000segments, and the integration potential of our dynamic mask approach within\u0000other editing methods.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0