Remove 2015 Remove Clustering Remove Natural Language Processing
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Evaluating Long-Context Question & Answer Systems

Eugene Yan

Loong evaluates a model’s ability to locate, compare, cluster, and reason on evidence spread across multiple documents, typically ranging from 10,000 to over 250,000 tokens. Clustering : Aggregating and grouping relevant information from multiple sources based on specific criteria. © Eugene Yan 2015 - 2025 • Feedback • RSS

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Announcing the ICDAR 2023 Competition on Hierarchical Text Detection and Recognition

Google Research AI blog

books, magazines, newspapers, forms, street signs, restaurant menus) so that they can be indexed, searched, translated, and further processed by state-of-the-art natural language processing techniques. Middle: Illustration of line clustering. Right: Illustration paragraph clustering. HierText identifies 103.8

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Top 6 Kubernetes use cases

IBM Journey to AI blog

Nodes run the pods and are usually grouped in a Kubernetes cluster, abstracting the underlying physical hardware resources. Kubernetes’s declarative, API -driven infrastructure has helped free up DevOps and other teams from manually driven processes so they can work more independently and efficiently to achieve their goals.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

Our high-level training procedure is as follows: for our training environment, we use a multi-instance cluster managed by the SLURM system for distributed training and scheduling under the NeMo framework. From 2015–2018, he worked as a program director at the US NSF in charge of its big data program. Youngsuk Park is a Sr.

AWS 128
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Robustness of a Markov Blanket Discovery Approach to Adversarial Attack in Image Segmentation: An…

Mlearning.ai

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., 2015; Huang et al., 2019) or by using input pre-processing techniques to remove adversarial perturbations (Xie et al., 2012; Otsu, 1979; Long et al.,

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more.

ML 97
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Comparative Analysis: PyTorch vs TensorFlow vs Keras

Pickl AI

In industry, it powers applications in computer vision, natural language processing, and reinforcement learning. This allows users to change the network architecture on-the-fly, which is particularly useful for tasks that require variable input sizes, such as natural language processing and reinforcement learning.