AI Blogs
Robust Subsampling Techniques for Outlier Handling in Persistent Homology
Outlier-Robust Subsampling Techniques for Persistent Homology Bernadette J. Stolz; 24(90):1−35, 2023. Abstract Persistent homology has gained popularity in various real-world data applications in recent years. However, the scalability of persistent homology algorithms...
![A Study on the UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023 (arXiv:2308.12526v1 [eess.AS])](https://instadatanews.com/wp-content/uploads/2023/08/arxiv-logo-fb-400x250.png){kind=logo}
A Study on the UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023 (arXiv:2308.12526v1 [eess.AS])
This report presents the submission of UNISOUND for both Track 1 and Track 2 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC 2023). Our system, which is trained solely on VoxCeleb2-dev, utilizes two architectures: Large-scale ResNet and RepVGG....
The Significance of Sparsification in the Sinkhorn Algorithm
Importance Sparsification for Sinkhorn Algorithm Mengyu Li, Jun Yu, Tao Li, Cheng Meng; 24(247):1−44, 2023. Abstract The Sinkhorn algorithm is widely used for approximating the solutions to optimal transport (OT) and unbalanced optimal transport (UOT) problems....
Applications of Legged Robot Locomotion: Exploring both Rewards and Constraints (arXiv:2308.12517v1 [cs.RO])
Previous studies have shown impressive control performance in complex robotic systems by using neural networks and model-free reinforcement learning. However, these controllers with natural motion and high task performance require extensive reward engineering, which...
Analysis of Concentration in Multivariate Elliptic Diffusion Processes
Concentration Analysis of Multivariate Elliptic Diffusions Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch; 24(106):1−38, 2023. Abstract This study presents concentration inequalities and associated PAC bounds for both continuous- and discrete-time additive...
Efficiency of Class Incremental Learning with Masked Autoencoders (arXiv:2308.12510v1 [cs.CV])
The concept of Class Incremental Learning (CIL) revolves around the idea of learning new classes in a sequential manner while preventing the loss of previous knowledge. In order to achieve this, we suggest the use of Masked Autoencoders (MAEs) as efficient learners...
Enhancing Multi-Try Metropolis through Local Balancing
Enhancing parallel computing in multiple-try Metropolis with local balancing Philippe Gagnon, Florian Maire, Giacomo Zanella; 24(248):1−59, 2023. Abstract Multiple-try Metropolis (MTM) is a widely used Markov chain Monte Carlo method that can be effectively...
Unraveling the Mysteries Behind Social Media Manipulations
[Submitted on 24 Aug 2023] Click here to download a PDF of the paper titled "False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations," written by Mohammad Majid Akhtar and 3 other authors. Download PDF Abstract: The spread...
Algorithmic Trends and Opportunities in Efficient Deep Learning Computation
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities Authors: Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock; Volume 24(122):1−77, 2023. Abstract In recent years, deep learning has made significant advancements. However, the increasing economic...
Improving ECG Classification through Neural Network Scale Optimization
[Submitted on 24 Aug 2023] Download a PDF of the paper titled "Optimizing Neural Network Scale for ECG Classification" by Byeong Tak Lee and 2 other authors: Download PDF Abstract: We investigate the scaling of convolutional neural networks (CNNs), specifically...
Combining Unbiased Multilevel Monte Carlo and Markov Chain Monte Carlo Methods for Intractable Distributions: An Integration of MLMC and MCMC
Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC Tianze Wang, Guanyang Wang; 24(249):1−40, 2023. Abstract The construction of unbiased estimators from Markov chain Monte Carlo (MCMC) outputs is a challenging problem that has...
Fall Detection on Offline Embedded and Low Power Devices using Knowledge Distillation Based Long Short-Term Memory
This paper introduces a cost-effective and low-power approach to unintentional fall detection. The authors utilize knowledge distillation-based LSTM models to improve accuracy. The focus is on analyzing time-series data from various sensors, enabling real-time...
Addressing the Issues and Providing a Resolution for the Brier Score in the Context of Administrative Censoring
The Brier Score under Administrative Censoring: Problems and a Solution Håvard Kvamme, Ørnulf Borgan; 24(2):1−26, 2023. Abstract The Brier score is commonly utilized to evaluate probability predictions. In survival analysis, when there are right-censored observations...
Real-time and Accurate Signal Reconstruction from Streamed Multivariate Time Series with Zero Delay
The process of digitalizing analog signals involves sampling in time and discretizing in amplitude. However, this process introduces errors in the signal reconstruction, which are influenced by the resolution of amplitude and the density of acquired samples. While...
A Python Library for Hyperdimensional Computing and Vector Symbolic Architectures: An Open-Source Approach
Torchhd: A Python Library for Hyperdimensional Computing and Vector Symbolic Architectures Authors: Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander Veidenbaum; Journal of Machine Learning Research,...
The Convergence of Client Heterogeneity and Generative Models in Personalized Federated Learning
[Submitted on 23 Aug 2023] Download a PDF of the paper titled "PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning," authored by Achintha Wijesinghe and 2 others. Download PDF Abstract: Recent advancements in generative...
Flexible and Modular Reinforcement Learning Library
skrl: A Modular and Flexible Library for Reinforcement Learning Authors: Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba; 24(254):1−9, 2023. Abstract skrl is a Python-based open-source library for reinforcement learning. It is...
Using synthetic data from latent diffusion models to enhance medical image classifiers
arXivLabs is a platform where collaborators can develop and share new features on the arXiv website. Both individuals and organizations that collaborate with arXivLabs share our values of openness, community, excellence, and user data privacy. We only work with...
Estimation of BLOOM’s Carbon Footprint: A Language Model with 176B Parameters
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model By Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat; 24(253):1−15, 2023. Abstract Progress in machine learning (ML) comes at an environmental cost due to the resources, energy, and...
A Self-Healing Approach for Deep Reinforcement Learning Systems Using Intentional Forgetting
Deep reinforcement learning (DRL) is being increasingly used in large-scale productions such as Netflix and Facebook. However, these data-driven systems can exhibit undesirable behaviors due to changes in the production environment, known as environmental drifts....
Enhancing Few-shot Learning using Retrieval-Augmented Language Models
Atlas: Few-shot Learning with Retrieval Augmented Language Models Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave; 24(251):1−43, 2023. Abstract Many large...
TAI-GAN: A GAN with Temporal and Anatomical Information for Correcting Dynamic Cardiac PET Motion in Early-to-Late Frame Conversion
The fast movement of rubidium-82 ($^{82}$Rb) and the wide variation in cross-frame distribution in dynamic cardiac positron emission tomography (PET) pose significant challenges for correcting motion between frames, especially in the early frames where traditional...
Minimize Hosting Costs by Deploying Thousands of Model Ensembles on GPU using Amazon SageMaker Multi-Model Endpoints
Artificial intelligence (AI) adoption is rapidly increasing across various industries and use cases. Recent advancements in deep learning (DL), large language models (LLMs), and generative AI have allowed customers to leverage state-of-the-art solutions that exhibit...
Privacy-Preserving Adaptive False Discovery Rate Control
Adaptive False Discovery Rate Control with Privacy Guarantee Xintao Xia, Zhanrui Cai; 24(252):1−35, 2023. Abstract This paper introduces a novel method for adaptively controlling the False Discovery Rate (FDR) with privacy guarantees in multiple testing procedures. By...
BaDExpert: Enhancing Backdoor Input Detection by Extracting Backdoor Functionality (arXiv:2308.12439v1 [cs.CR])
We introduce a new defense mechanism to combat backdoor attacks on Deep Neural Networks (DNNs), where adversaries secretly implant malicious behaviors known as backdoors into the DNNs. Our defense approach belongs to the category of post-development defenses, which...
Running the Spark UI on Amazon SageMaker Studio
Amazon SageMaker provides multiple options for running distributed data processing jobs with Apache Spark. These options include running Spark applications interactively from Amazon SageMaker Studio, using a pre-built SageMaker Spark container to run Spark...
Bounding Generalization in Adversarial Contrastive Learning
Generalization Bounds for Adversarial Contrastive Learning Xin Zou, Weiwei Liu; 24(114):1−54, 2023. Abstract Adversarial attacks pose a significant challenge to deep networks, and adversarial training has emerged as a popular method to train robust models. Recently,...
A Categorization of Difficulties in Implementing Deep Reinforcement Learning Systems (arXiv:2308.12438v1 [cs.LG])
Deep reinforcement learning (DRL) has demonstrated great potential in achieving human-level autonomy in various domains such as robotics, computer vision, and computer games. This has led to widespread interest and enthusiasm in both academia and industry. However,...
Creating Innovative Advertisements using Generative AI on Amazon SageMaker
Generative AI (GenAI) has the potential to revolutionize creative advertising. By retraining GenAI models and providing textual prompts, such as sentences describing scenes and objects, a wide variety of novel images can be created, including product shots. This...
Finite Trials Convex Reinforcement Learning
Convex Reinforcement Learning in Finite Trials Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli; 24(250):1−42, 2023. Abstract Convex Reinforcement Learning (RL) is a framework that extends the standard RL objective to include any convex (or...
An NLP Analysis of the Literature: Tracing the Development of ESG-focused DLT Research
The field of Distributed Ledger Technologies (DLTs) has seen significant advancements, but there is a lack of comprehensive research on the Environmental, Sustainability, and Governance (ESG) aspects of DLT. To address this gap, we conducted a systematic literature...
Creating a Centralized Monitoring and Reporting System for Amazon SageMaker with Amazon CloudWatch
Amazon SageMaker is a machine learning platform that manages ML workloads and provides a range of services. While it is recommended to use separate accounts for better policy management and resource isolation, monitoring these workloads in a multi-account environment...
Overcoming the Curse of Dimensionality
Beating the Curse of Dimensionality: A Method for High-dimensional Parameter Learning Authors: Ning Ning, Edward L. Ionides; Published in Journal of Machine Learning Research, Volume 24, Issue 82, Pages 1-76, 2023. Abstract Learning parameters in high-dimensional,...
Parameter estimation of nonlinear systems using machine learning techniques
Accurate estimation of parameters in complex nonlinear systems is of utmost importance in various scientific and engineering domains. In this study, we propose a novel approach for parameter estimation that utilizes a neural network with the Huber loss function. By...
Getting Started with Zero-shot Text Classification using Amazon SageMaker JumpStart
Natural Language Processing (NLP) is a field in Machine Learning (ML) focused on teaching computers to understand and interpret text and spoken language like humans do. Recent advancements in architectures, such as the transformer architecture, have enabled NLP models...
Examining the Resilience of Non-Lipschitz Networks: A Comprehensive Analysis
An Analysis of Robustness of Non-Lipschitz Networks Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang; 24(98):1−43, 2023. Abstract Despite significant progress, deep networks still remain highly vulnerable to adversarial attacks. One of the main...
Optimizing Self-Attention Imputation for Missing Data using Full Information Maximum Likelihood (FIML)
The arXivLabs framework enables collaborators to create and share new features for our website, arXiv. Both individuals and organizations that collaborate with arXivLabs embrace our values of openness, community, excellence, and user data privacy. We are committed to...
Amazon Translate improves translation accuracy and fluency with enhanced custom terminology
Amazon Translate is a neural machine translation service that offers fast, high-quality, affordable, and customizable language translation. It is important for machine translation to be accurate, fluent, and contextual when translating from one language to another....
Adaptive Partitioning: Examining Convergence Rates of Multivariate Density Estimation Methods
Convergence Rates of Multivariate Density Estimation Methods Based on Adaptive Partitioning Authors: Linxi Liu, Dangna Li, Wing Hung Wong; 24(50):1−64, 2023. Abstract Density estimation is a fundamental component of various statistical methods, including...
A Comprehensive Evaluation of Gender Inference from Names: Findings from a Large-Scale Performance Study (arXiv:2308.12381v1 [cs.CL])
Research across various scientific disciplines, including medicine, sociology, political science, and economics, heavily relies on a person's gender as a crucial piece of information. However, with the increasing availability of big data, gender information is often...
Creating Enterprise Search AI Applications with Haystack Pipelines and Amazon SageMaker JumpStart: Harness the Power of LLMs
This blog post is a collaboration between the author and Tuana Çelik from deepset, focusing on the importance of enterprise search in document digitization and knowledge management. The post introduces the concept of large language models (LLMs) and how they can be...
Contraction of the Posterior with Deep Gaussian Process Priors
Posterior Contraction for Deep Gaussian Process Priors Gianluca Finocchio, Johannes Schmidt-Hieber; 24(66):1−49, 2023. Abstract This study examines the rates of posterior contraction for a class of deep Gaussian process priors in the nonparametric regression setting,...
Improving Open-set Face Recognition through Neural Ensemble, Maximal Entropy Loss, and Feature Augmentation
[Submitted on 23 Aug 2023] Click here to download a PDF of the paper titled "Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation" by Rafael Henrique Vareto, Manuel Günther, and William Robson Schwartz. Download PDF Abstract:...
Simplified Amazon SageMaker JumpStart SDK: Zero-shot and few-shot prompting for the BloomZ 176B foundation model
Amazon SageMaker JumpStart is a hub for machine learning (ML) that offers algorithms, models, and ML solutions. It provides ML practitioners with a range of best performing and publicly available foundation models (FMs) such as BLOOM, Llama 2, Falcon-40B, Stable...
A Framework for Nonsmooth Nonconvex Optimization: Inertial Block Majorization Minimization
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis; 24(18):1−41, 2023. Abstract This paper presents TITAN, a new framework called inerTIal block majorizaTion minimizAtioN...
Risk-Aware Policies: A Step Towards Enhanced Algorithmic Recourse Safety
Title: SafeAR: Enhancing Algorithmic Recourse with Risk-Aware Policies Date: 23 Aug 2023 Authors: Haochen Wu and colleagues Abstract: As the use of machine learning (ML) models in critical domains like finance and healthcare continues to grow, it is crucial to provide...
Leveraging LLMs on Amazon SageMaker for Multilingual Intelligent Video and Audio Q&A
Digital assets play a crucial role in representing products, services, culture, and brand identity for businesses in the digital era. They enable interactive and personalized experiences, enhancing customer engagement and deepening connections with the target...
A Toolkit for Evaluating Neural Network Explanations and More: Promoting Explainable AI and Responsible Practices
Quantus: A Toolkit for Evaluating Neural Network Explanations and Beyond Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne; 24(34):1−11, 2023. Abstract The evaluation of...
Refining Diffusion Models: A Renormalization Approach (arXiv:2308.12355v1 [hep-th])
In this content, we discuss the application of diffusion models in learning inverse renormalization group flows of statistical and quantum field theories. Diffusion models are machine learning models used to generate samples from complex distributions by learning the...
Amazon Shopping Utilizes Amazon Rekognition Content Moderation for Assessing Harmful Images within Product Reviews
Product reviews have become a crucial part of the customer shopping journey, as they provide valuable feedback and insights from other customers. Amazon, with its vast selection of products, has become a reliable source of online reviews. However, it is important to...
Small Molecule Property Prediction in Drug Discovery Using Machine Learning
Machine learning (ML) has emerged as a promising method for predicting the properties of small molecules in the field of drug discovery. In this article, we offer a comprehensive overview of the various ML techniques that have been developed for this purpose in recent...
Thomson Reuters’ Rapid Development of Open Arena: An Enterprise-Grade Large Language Model Playground in Less Than 6 Weeks
This post is a collaboration between Shirsha Ray Chaudhuri, Harpreet Singh Baath, Rashmi B Pawar, and Palvika Bansal from Thomson Reuters. Thomson Reuters, a global company driven by content and technology, has been utilizing artificial intelligence (AI) and machine...
Enhancing Unfolding with Schrödinger Bridges in Generative Model-based Approach
arXivLabs is a platform where collaborators can develop and share new features for arXiv directly on our website. We welcome both individuals and organizations who share our values of openness, community, excellence, and user data privacy. We only collaborate with...
Using Amazon SageMaker to Train Self-Supervised Vision Transformers on Overhead Imagery
This is a guest blog post co-written by Ben Veasey, Jeremy Anderson, Jordan Knight, and June Li from Travelers. The use of satellite and aerial images has become crucial in various fields such as precision agriculture, insurance risk assessment, urban development, and...
Comparison of Linear Regression Model with Extracted Chemical Features and Graph Convolutional Neural Network for Drug Solubility Prediction
arXivLabs is a platform where collaborators can create and share new features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs have embraced and adopted our principles of openness, community, excellence, and user...
Leveraging Selective Execution in Amazon SageMaker Pipelines for Enhanced Efficiency
MLOps is a crucial discipline that oversees the process of bringing machine learning (ML) models into production. While it's common to focus on training and deploying a single model, in reality, organizations often work with multiple models and complex workflows. To...
Graph Neural Stochastic Differential Equations: A Graph-Based Approach to Modeling Stochastic Differential Equations
[Submitted on 23 Aug 2023] Download a PDF of the paper titled "Graph Neural Stochastic Differential Equations" by Richard Bergna and 3 other authors Download PDF Abstract: We introduce a new model called Graph Neural Stochastic Differential Equations (Graph Neural...
Leveraging Amazon Redshift Data, Scale ML Feature Development with Amazon SageMaker Feature Store
Amazon Redshift is a widely used cloud data warehouse that enables customers to analyze large amounts of data on a daily basis. Many users of Redshift are now looking to extend their datasets for machine learning purposes using Amazon SageMaker. In this post, we will...
Cross-Domain Representation Learning with Reliable Trustworthiness (arXiv:2308.12315v1 [cs.LG])
With the widespread use of AI systems in our daily lives, there are both benefits and social issues that arise. In order to ensure that AI systems are trustworthy, extensive research has been conducted to establish guidelines. One crucial aspect of AI systems is...
Implementing AWS Lake Formation’s Fine-Grained Data Access Controls in Amazon SageMaker Data Wrangler
Amazon SageMaker Data Wrangler is a tool that significantly reduces the time needed for collecting and preparing data for machine learning. Instead of spending weeks on data preparation, Data Wrangler allows you to streamline the entire process within minutes. It...
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