Viral News

Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice
21 Aug

Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice

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arXiv:2408.10263v1 Announce Type: new Abstract: Kolmogorov Arnold Networks (KAN) are highly efficient in inference and can handle complex patterns once trained, making them desirable for production environments and ensuring a fast service experience in the finance and electronic shopping industries. However, we found that KAN, in general, is not suitable for fraud detection problems. We also discovered a quick method to determine whether a problem is solvable by KAN: if the data can be effectively separated using spline interpolation with varying intervals after applying Principal Component Analysis (PCA) to reduce the data dimensions to two, KAN can outperform most machine…
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NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild
21 Aug

NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild

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arXiv:2408.10258v1 Announce Type: new Abstract: Current methods for performing 3D reconstruction and novel view synthesis (NVS) in ultrasound imaging data often face severe artifacts when training NeRF-based approaches. The artifacts produced by current approaches differ from NeRF floaters in general scenes because of the unique nature of ultrasound capture. Furthermore, existing models fail to produce reasonable 3D reconstructions when ultrasound data is captured or obtained casually in uncontrolled environments, which is common in clinical settings. Consequently, existing reconstruction and NVS methods struggle to handle ultrasound motion, fail to capture intricate details, and cannot model transparent and reflective surfaces. In this…
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Resolving Lexical Bias in Edit Scoping with Projector Editor Networks
21 Aug

Resolving Lexical Bias in Edit Scoping with Projector Editor Networks

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Relational Graph Convolutional Networks Do Not Learn Sound Rules
21 Aug

Relational Graph Convolutional Networks Do Not Learn Sound Rules

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Target-Dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-Caption Translation Network using Visual-Caption Pairs
21 Aug

Target-Dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-Caption Translation Network using Visual-Caption Pairs

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arXiv:2408.10248v1 Announce Type: new Abstract: The natural language processing and multimedia field has seen a notable surge in interest in multimodal sentiment recognition. Hence, this study aims to employ Target-Dependent Multimodal Sentiment Analysis (TDMSA) to identify the level of sentiment associated with every target (aspect) stated within a multimodal post consisting of a visual-caption pair. Despite the recent advancements in multimodal sentiment recognition, there has been a lack of explicit incorporation of emotional clues from the visual modality, specifically those pertaining to facial expressions. The challenge at hand is to proficiently obtain visual and emotional clues and subsequently synchronise them…
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Value Alignment from Unstructured Text
21 Aug

Value Alignment from Unstructured Text

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Contrastive Learning on Medical Intents for Sequential Prescription Recommendation
21 Aug

Contrastive Learning on Medical Intents for Sequential Prescription Recommendation

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[Submitted on 13 Aug 2024] View a PDF of the paper titled Contrastive Learning on Medical Intents for Sequential Prescription Recommendation, by Arya Hadizadeh Moghaddam and 3 other authors View PDF HTML (experimental) Abstract:Recent advancements in sequential modeling applied to Electronic Health Records (EHR) have greatly influenced prescription recommender systems. While the recent literature on drug recommendation has shown promising performance, the study of discovering a diversity of coexisting temporal relationships at the level of medical codes over consecutive visits remains less explored. The goal of this study can be motivated from two perspectives. First, there is a need to…
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VyAnG-Net: A Novel Multi-Modal Sarcasm Recognition Model by Uncovering Visual, Acoustic and Glossary Features
21 Aug

VyAnG-Net: A Novel Multi-Modal Sarcasm Recognition Model by Uncovering Visual, Acoustic and Glossary Features

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[Submitted on 5 Aug 2024] View a PDF of the paper titled VyAnG-Net: A Novel Multi-Modal Sarcasm Recognition Model by Uncovering Visual, Acoustic and Glossary Features, by Ananya Pandey and 1 other authors View PDF Abstract:Various linguistic and non-linguistic clues, such as excessive emphasis on a word, a shift in the tone of voice, or an awkward expression, frequently convey sarcasm. The computer vision problem of sarcasm recognition in conversation aims to identify hidden sarcastic, criticizing, and metaphorical information embedded in everyday dialogue. Prior, sarcasm recognition has focused mainly on text. Still, it is critical to consider all textual information,…
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Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models
21 Aug

Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models

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arXiv:2408.10357v1 Announce Type: new Abstract: Query-focused summarization (QFS) is a fundamental task in natural language processing with broad applications, including search engines and report generation. However, traditional approaches assume the availability of relevant documents, which may not always hold in practical scenarios, especially in highly specialized topics. To address this limitation, we propose a novel knowledge-intensive approach that reframes QFS as a knowledge-intensive task setup. This approach comprises two main components: a retrieval module and a summarization controller. The retrieval module efficiently retrieves potentially relevant documents from a large-scale knowledge corpus based on the given textual query, eliminating the dependence…
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Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications
21 Aug

Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications

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arXiv:2408.10215v1 Announce Type: new Abstract: The aim of Reinforcement Learning (RL) in real-world applications is to create systems capable of making autonomous decisions by learning from their environment through trial and error. This paper emphasizes the importance of reward engineering and reward shaping in enhancing the efficiency and effectiveness of reinforcement learning algorithms. Reward engineering involves designing reward functions that accurately reflect the desired outcomes, while reward shaping provides additional feedback to guide the learning process, accelerating convergence to optimal policies. Despite significant advancements in reinforcement learning, several limitations persist. One key challenge is the sparse and delayed nature of…
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