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Significance of Video Annotation in Training Computer Vision Models
22 Aug

Significance of Video Annotation in Training Computer Vision Models

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In today’s dynamic digital landscape, data is pivotal in fueling innovations in artificial intelligence (AI). Video annotation is critical for advancements in AI through computer vision. Moreover, the training data must be customized per the AI model’s training requirements for developing AI and machine learning (ML) applications.    Video annotation aids ML models in detecting objects and memorizing particular patterns to make predictions. It boosts machine-learning algorithms in interpreting complex visuals using descriptive markers.    It also significantly trains computer vision models in fields such as autonomous vehicles, intelligent retail checkouts, drones, and more.    Read the blog below to…
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Databricks Data Warehouse Brickbuilder Migration Solutions Help Businesses Democratize Data and Analytics
22 Aug

Databricks Data Warehouse Brickbuilder Migration Solutions Help Businesses Democratize Data and Analytics

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Today, we're excited to announce the launch of Data Warehouse Brickbuilder Migration Solutions. This is an expansion to the Brickbuilder Program, which packages together the experience and knowledge of the Databricks partner ecosystem along with pre-built code, modular frameworks, and custom services to help organizations unlock the full potential of the Databricks Data Intelligence Platform. As organizations increasingly rely on data to boost productivity and fuel innovation, migrating to Databricks enables them to achieve effective data management, utilization and access to data and AI by consolidating the best features of data lakes and data warehouses into one platform.The Brickbuilder Program,…
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Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps
22 Aug

Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps

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arXiv:2408.11112v1 Announce Type: new Abstract: In recent years, Data Science has become increasingly relevant as a support tool for industry, significantly enhancing decision-making in a way never seen before. In this context, the MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models, ranging from experimentation to monitoring in productive environments. Research results shows MLOps is a constantly evolving discipline, with challenges and solutions for integrating development and production environments, publishing models in production environments, and monitoring models throughout the end to end development lifecycle. This paper contributes to the understanding of MLOps techniques and…
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Event Stream based Sign Language Translation: A High-Definition Benchmark Dataset and A New Algorithm
22 Aug

Event Stream based Sign Language Translation: A High-Definition Benchmark Dataset and A New Algorithm

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arXiv:2408.10488v1 Announce Type: new Abstract: Sign Language Translation (SLT) is a core task in the field of AI-assisted disability. Unlike traditional SLT based on visible light videos, which is easily affected by factors such as lighting, rapid hand movements, and privacy breaches, this paper proposes the use of high-definition Event streams for SLT, effectively mitigating the aforementioned issues. This is primarily because Event streams have a high dynamic range and dense temporal signals, which can withstand low illumination and motion blur well. Additionally, due to their sparsity in space, they effectively protect the privacy of the target person. More specifically,…
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Tabular Transfer Learning via Prompting LLMs
22 Aug

Tabular Transfer Learning via Prompting LLMs

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arXiv:2408.11063v1 Announce Type: new Abstract: Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with the scarcity of labeled data, transfer learning is a conventional approach; it suggests to learn a transferable knowledge by training a neural network from multiple other sources. In this paper, we investigate transfer learning of tabular tasks, which has been less studied and successful in the literature, compared to other domains, e.g., vision and language. This is because tables are inherently heterogeneous, i.e., they contain different…
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ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks
22 Aug

ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks

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arXiv:2408.11104v1 Announce Type: new Abstract: The loss functions of many learning problems contain multiple additive terms that can disagree and yield conflicting update directions. For Physics-Informed Neural Networks (PINNs), loss terms on initial/boundary conditions and physics equations are particularly interesting as they are well-established as highly difficult tasks. To improve learning the challenging multi-objective task posed by PINNs, we propose the ConFIG method, which provides conflict-free updates by ensuring a positive dot product between the final update and each loss-specific gradient. It also maintains consistent optimization rates for all loss terms and dynamically adjusts gradient magnitudes based on conflict levels.…
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MambaEVT: Event Stream based Visual Object Tracking using State Space Model
22 Aug

MambaEVT: Event Stream based Visual Object Tracking using State Space Model

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arXiv:2408.10487v1 Announce Type: new Abstract: Event camera-based visual tracking has drawn more and more attention in recent years due to the unique imaging principle and advantages of low energy consumption, high dynamic range, and dense temporal resolution. Current event-based tracking algorithms are gradually hitting their performance bottlenecks, due to the utilization of vision Transformer and the static template for target object localization. In this paper, we propose a novel Mamba-based visual tracking framework that adopts the state space model with linear complexity as a backbone network. The search regions and target template are fed into the vision Mamba network for…
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Interactive-T2S: Multi-Turn Interactions for Text-to-SQL with Large Language Models
22 Aug

Interactive-T2S: Multi-Turn Interactions for Text-to-SQL with Large Language 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
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Solving Oscillator ODEs via Soft-constrained Physics-informed Neural Network with Small Data
22 Aug

Solving Oscillator ODEs via Soft-constrained Physics-informed Neural Network with Small Data

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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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