In this episode, we discuss Giraffe: Adventures in Expanding Context Lengths in LLMs by Arka Pal, Deep Karkhanis, Manley Roberts, Samuel Dooley, Arvind Sundararajan, Siddartha Naidu. The paper reviews techniques for overcoming the fixed context length limitation in large language models like LLaMA or LLaMA 2 by modifying positional encodings and introduces a new truncation strategy. It presents three novel tasks for evaluation, finding that linear scaling of contexts at evaluation time improves model performance, especially with a truncated positional basis. The researchers release new models named Giraffe with extended context lengths, along with datasets and code on HuggingFace to encourage further exploration in context length extrapolation.
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25K AI + DC Community | AI Data Centers Brand Ambassador | On a Mission Building Next Gen Digital Infrastructure | AI-Ready Data Centers | AI Compute | GPU Cloud | AI Cloud Infra Leader | Hyperscalers| AI/HPC Solutions
Vision -Language Modeling
New from FAIR: An Introduction to Vision-Language Modeling. Paper ➡️ https://go.fb.me/ncjj6t This guide covers how VLMs work, how to train them and approaches to evaluation — while it primarily covers mapping image to language, it also discusses how to extend VLMs to videos. FAIR is releasing this guide together with a set of collaborators to enable a greater understanding of mechanics behind mapping vision to language.
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When managing large datasets, vision language models are a great option. When it comes to simultaneously processing and analyzing massive amounts of textual and visual information, they provide a number of major advantages. You can gain insightful knowledge and improve your comprehension of big data management and utilization by investigating their capabilities and applications. For your studies or projects requiring extensive data analysis, this subject may be especially helpful. 💡 💡
New from FAIR: An Introduction to Vision-Language Modeling. Paper ➡️ https://go.fb.me/ncjj6t This guide covers how VLMs work, how to train them and approaches to evaluation — while it primarily covers mapping image to language, it also discusses how to extend VLMs to videos. FAIR is releasing this guide together with a set of collaborators to enable a greater understanding of mechanics behind mapping vision to language.
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New from FAIR: An Introduction to Vision-Language Modeling. Paper ➡️ https://go.fb.me/ncjj6t This guide covers how VLMs work, how to train them and approaches to evaluation — while it primarily covers mapping image to language, it also discusses how to extend VLMs to videos. FAIR is releasing this guide together with a set of collaborators to enable a greater understanding of mechanics behind mapping vision to language.
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LLMs are trained on written language, VLM is extension of LLMs with Vision language.
New from FAIR: An Introduction to Vision-Language Modeling. Paper ➡️ https://go.fb.me/ncjj6t This guide covers how VLMs work, how to train them and approaches to evaluation — while it primarily covers mapping image to language, it also discusses how to extend VLMs to videos. FAIR is releasing this guide together with a set of collaborators to enable a greater understanding of mechanics behind mapping vision to language.
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An introduction to Vision-Language Models (VLMs).
New from FAIR: An Introduction to Vision-Language Modeling. Paper ➡️ https://go.fb.me/ncjj6t This guide covers how VLMs work, how to train them and approaches to evaluation — while it primarily covers mapping image to language, it also discusses how to extend VLMs to videos. FAIR is releasing this guide together with a set of collaborators to enable a greater understanding of mechanics behind mapping vision to language.
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I’m putting this at the top of my reading list. If you’ve ever been curious about the technical details behind multimodal vision/text models and their applications, this looks like a great place to start! #artificialintelligence #computervision
New from FAIR: An Introduction to Vision-Language Modeling. Paper ➡️ https://go.fb.me/ncjj6t This guide covers how VLMs work, how to train them and approaches to evaluation — while it primarily covers mapping image to language, it also discusses how to extend VLMs to videos. FAIR is releasing this guide together with a set of collaborators to enable a greater understanding of mechanics behind mapping vision to language.
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enabling digital services for Student Loan related activities while maintaining the highest security standard, the most compliant personal data protection and customer-centric data-driven innovation.
🌟 Excited to share our latest blog post "Rotation Averaging: A Primal-Dual Method and Closed-Forms in Cycle Graphs" on arXiv! This work addresses the optimization problem in rotation averaging, a crucial aspect of geometric reconstruction and visual simultaneous localization and mapping. Our novel primal-dual method and insights from spectral graph theory offer valuable advancements in this field. Check out the full article here: https://bit.ly/3L4az24 #rotationaveraging #geometricreconstruction #arXivpublication
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In this episode, we discuss Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation by Jaemin Cho, Yushi Hu, Roopal Garg, Peter Anderson, Ranjay Krishna, Jason Baldridge, Mohit Bansal, Jordi Pont-Tuset, Su Wang. The abstract discusses the evaluation of text-to-image models, focusing on ensuring the accuracy between text prompts and generated images through a question generation and answering system. It introduces the Davidsonian Scene Graph (DSG), a strategy intended to improve question quality and answer consistency by creating a structured set of unique, semantic questions. Extensive testing and human assessments have shown DSG's effectiveness, and the release of DSG-1k provides a benchmark for wider usage and evaluation in the field.
arxiv preprint - Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation
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🌟 Day 12 of 100 Days Challenge: Exploring Subarray Counts 🌟 Today, I delved deep into arrays and subarrays, encountering an intriguing problem: counting subarrays with a specific property. #CodingExploration #AlgorithmicThinking #100DaysChallenge
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Martti Kirkko-Jaakkola is one of our brilliant mathematicians whose task is to provide clients with answers from the data produced by measuring equipment, together with the team. But how do albatrosses’ flight paths and indoor navigation relate to the work of this academic? Get to know Martti here: https://lnkd.in/dt5HCuy6 Curious about joining Nordic Inertial team? Visit our Career-page or contact Jussi Collin. #Team #Mathematics #Engineering #Research #Interial #Algorithms #StaffIntroduction #MotionSensing
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