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Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
12 Aug

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change

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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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ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation
12 Aug

ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

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arXiv:2408.04883v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation requires models to effectively integrate visual representations with open-vocabulary semantic labels. While Contrastive Language-Image Pre-training (CLIP) models shine in recognizing visual concepts from text, they often struggle with segment coherence due to their limited localization ability. In contrast, Vision Foundation Models (VFMs) excel at acquiring spatially consistent local visual representations, yet they fall short in semantic understanding. This paper introduces ProxyCLIP, an innovative framework designed to harmonize the strengths of both CLIP and VFMs, facilitating enhanced open-vocabulary semantic segmentation. ProxyCLIP leverages the spatial feature correspondence from VFMs as a form of proxy…
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Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference
12 Aug

Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference

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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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Google Cloud Research Shows Strong ROI for Early Adopters
12 Aug

Google Cloud Research Shows Strong ROI for Early Adopters

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(TierneyMJ/Shutterstock) Google Cloud shared new global research on how select global enterprises are achieving breakout success with GenAI. The ROI of Gen AI global survey commissioned by Google emphasizes that GenAI is more than just a new technology; it is a ‘key driver of business transformation’. The report is based on a survey conducted by Google Cloud and the National Research Group. The survey, which includes 2,500 business leaders from global enterprises with revenues exceeding $10 million, focuses on GenAI’s ability to impact two categories: direct financial impact and the broader business advantages gained from deploying GenAI in production.  According…
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Kolmogorov-Arnold Network for Online Reinforcement Learning
12 Aug

Kolmogorov-Arnold Network for Online Reinforcement Learning

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[Submitted on 9 Aug 2024] View a PDF of the paper titled Kolmogorov-Arnold Network for Online Reinforcement Learning, by Victor Augusto Kich and 5 other authors View PDF HTML (experimental) Abstract:Kolmogorov-Arnold Networks (KANs) have shown potential as an alternative to Multi-Layer Perceptrons (MLPs) in neural networks, providing universal function approximation with fewer parameters and reduced memory usage. In this paper, we explore the use of KANs as function approximators within the Proximal Policy Optimization (PPO) algorithm. We evaluate this approach by comparing its performance to the original MLP-based PPO using the DeepMind Control Proprio Robotics benchmark. Our results indicate that…
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On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey
12 Aug

On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey

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arXiv:2408.04879v1 Announce Type: new Abstract: Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains via learning generalized knowledge from limited data in the seen domain. The gist for ZSIR is to execute element-wise representation and reasoning from the input visual space to the target semantic space, which is a bottom-up modeling paradigm inspired by the process by which humans observe the world, i.e., capturing new concepts by learning and combining the basic components or shared characteristics. In recent years, element-wise learning techniques have seen significant progress in ZSIR as well as widespread application. However,…
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Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding
12 Aug

Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding

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arXiv:2408.04651v1 Announce Type: new Abstract: This project investigates the efficacy of Large Language Models (LLMs) in understanding and extracting scientific knowledge across specific domains and to create a deep learning framework: Knowledge AI. As a part of this framework, we employ pre-trained models and fine-tune them on datasets in the scientific domain. The models are adapted for four key Natural Language Processing (NLP) tasks: summarization, text generation, question answering, and named entity recognition. Our results indicate that domain-specific fine-tuning significantly enhances model performance in each of these tasks, thereby improving their applicability for scientific contexts. This adaptation enables non-experts to…
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Supernovas, Black Holes and Streaming Data
12 Aug

Supernovas, Black Holes and Streaming Data

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OverviewThis blog post is a follow-up to the session From Supernovas to LLMs at Data + AI Summit 2024, where I demonstrated how anyone can consume and process publicly available NASA satellite data from Apache Kafka.Unlike most Kafka demos, which are not easily reproducible or rely on simulated data, I will show how to analyze a live data stream from NASA's publicly accessible Gamma-ray Coordinates Network (GCN) which integrates data from supernovas and black holes coming from various satellites.While it's possible to craft a solution using only open source Apache Spark™ and Apache Kafka, I will show the significant advantages…
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Adversarially Robust Industrial Anomaly Detection Through Diffusion Model
12 Aug

Adversarially Robust Industrial Anomaly Detection Through Diffusion Model

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arXiv:2408.04839v1 Announce Type: new Abstract: Deep learning-based industrial anomaly detection models have achieved remarkably high accuracy on commonly used benchmark datasets. However, the robustness of those models may not be satisfactory due to the existence of adversarial examples, which pose significant threats to the practical deployment of deep anomaly detectors. Recently, it has been shown that diffusion models can be used to purify the adversarial noises and thus build a robust classifier against adversarial attacks. Unfortunately, we found that naively applying this strategy in anomaly detection (i.e., placing a purifier before an anomaly detector) will suffer from a high anomaly…
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ChatGPT Meets Iris Biometrics
12 Aug

ChatGPT Meets Iris Biometrics

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[Submitted on 9 Aug 2024] View a PDF of the paper titled ChatGPT Meets Iris Biometrics, by Parisa Farmanifard and Arun Ross View PDF HTML (experimental) Abstract:This study utilizes the advanced capabilities of the GPT-4 multimodal Large Language Model (LLM) to explore its potential in iris recognition - a field less common and more specialized than face recognition. By focusing on this niche yet crucial area, we investigate how well AI tools like ChatGPT can understand and analyze iris images. Through a series of meticulously designed experiments employing a zero-shot learning approach, the capabilities of ChatGPT-4 was assessed across various…
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