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28 May

Efficient Mitigation of Bus Bunching through Setter-Based Curriculum Learning

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arXiv:2405.15824v1 Announce Type: new Abstract: Curriculum learning has been growing in the domain of reinforcement learning as a method of improving training efficiency for various tasks. It involves modifying the difficulty (lessons) of the environment as the agent learns, in order to encourage more optimal agent behavior and higher reward states. However, most curriculum learning methods currently involve discrete transitions of the curriculum or predefined steps by the programmer or using automatic curriculum learning on only a small subset training such as only on an adversary. In this paper, we propose a novel approach to curriculum learning that uses a…

28 May

Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier

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[Submitted on 17 Apr 2024] Authors:Aristeidis Tsaris, Chengming Zhang, Xiao Wang, Junqi Yin, Siyan Liu, Moetasim Ashfaq, Ming Fan, Jong Youl Choi, Mohamed Wahib, Dan Lu, Prasanna Balaprakash, Feiyi Wang View a PDF of the paper titled Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier, by Aristeidis Tsaris and 11 other authors View PDF Abstract:Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence lengths. While transformers for text has inspired scaling sequence lengths in ViTs, yet adapting these for ViTs introduces unique challenges.…

28 May

DuanzAI: Slang-Enhanced LLM with Prompt for Humor Understanding

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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

28 May

Executive Overview: The Rise of Open Foundational Models

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Moving generative AI applications from the proof of concept stage into production requires control, reliability and data governance. Organizations are turning to open source foundation models in search of that control and the ability to better influence outputs by more tightly managing both the models and the data they are trained on.Databricks has assisted thousands of customers in evaluating use cases for generative AI and determining the most appropriate architecture for their organization.Our customers have shared with us the challenge of building and deploying production-quality AI models, which is often difficult and expensive. As a result, most CIOs are not…

28 May

Fast Inference Using Automatic Differentiation and Neural Transport in Astroparticle Physics

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arXiv:2405.14932v1 Announce Type: new Abstract: Multi-dimensional parameter spaces are commonly encountered in astroparticle physics theories that attempt to capture novel phenomena. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to this community. Effectively sampling these spaces is crucial to bridge the gap between experiment and theory. Several recent innovations, which are only beginning to make their way into this field, have made navigating such complex posteriors possible. These include GPU acceleration, automatic differentiation, and neural-network-guided reparameterization. We apply these advancements to astroparticle physics experimental results in the context of novel neutrino physics and…

28 May

LookUp3D: Data-Driven 3D Scanning

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arXiv:2405.14882v1 Announce Type: new Abstract: We introduce a novel calibration and reconstruction procedure for structured light scanning that foregoes explicit point triangulation in favor of a data-driven lookup procedure. The key idea is to sweep a calibration checkerboard over the entire scanning volume with a linear stage and acquire a dense stack of images to build a per-pixel lookup table from colors to depths. Imperfections in the setup, lens distortion, and sensor defects are baked into the calibration data, leading to a more reliable and accurate reconstruction. Existing structured light scanners can be reused without modifications while enjoying the superior…

27 May

Aya 23: Open Weight Releases to Further Multilingual Progress

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arXiv:2405.15032v1 Announce Type: new Abstract: This technical report introduces Aya 23, a family of multilingual language models. Aya 23 builds on the recent release of the Aya model ("Ust"un et al., 2024), focusing on pairing a highly performant pre-trained model with the recently released Aya collection (Singh et al., 2024). The result is a powerful multilingual large language model serving 23 languages, expanding state-of-art language modeling capabilities to approximately half of the world's population. The Aya model covered 101 languages whereas Aya 23 is an experiment in depth vs breadth, exploring the impact of allocating more capacity to fewer languages…

27 May

The Vital Role of Data Governance in Communications, Media and Entertainment

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Data, analytics and AI governance is perhaps the most important yet challenging aspect of any data and AI democratization effort. For your data, analytics and AI needs, you've likely deployed two different systems — data warehouses for business intelligence and data lakes for AI. And now you've created data silos with data movement across two systems, each with a different governance model.But data isn't limited to files or tables. You also have assets like dashboards, ML models and notebooks, each with their own permission models, making it difficult to manage access permissions for all these assets consistently. The problem gets…

27 May

How Does Bayes Error Limit Probabilistic Robust Accuracy

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arXiv:2405.14923v1 Announce Type: new Abstract: Adversarial examples pose a security threat to many critical systems built on neural networks. Given that deterministic robustness often comes with significantly reduced accuracy, probabilistic robustness (i.e., the probability of having the same label with a vicinity is $ge 1-kappa$) has been proposed as a promising way of achieving robustness whilst maintaining accuracy. However, existing training methods for probabilistic robustness still experience non-trivial accuracy loss. It is unclear whether there is an upper bound on the accuracy when optimising towards probabilistic robustness, and whether there is a certain relationship between $kappa$ and this bound. This…

27 May

DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models

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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