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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., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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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. Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms.

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Demand forecasting at Getir built with Amazon Forecast

AWS Machine Learning Blog

Getir was founded in 2015 and operates in Turkey, the UK, the Netherlands, Germany, France, Spain, Italy, Portugal, and the United States. Algorithm Selection Amazon Forecast has six built-in algorithms ( ARIMA , ETS , NPTS , Prophet , DeepAR+ , CNN-QR ), which are clustered into two groups: statististical and deep/neural network.

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We still have so much to learn from nature

Dataconomy

The Kilobot platform provides researchers with a practical means to study and experiment with swarm robotics algorithms and concepts. Swarm intelligence algorithms are typically decentralized, meaning that they do not require a central controller. In 2015, a swarm of robots was used to search for survivors after the Nepal earthquake.

Algorithm 172
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Best Machine Learning Frameworks for ML Experts in 2023

Pickl AI

People don’t even need the in-depth knowledge of the various machine learning algorithms as it contains pre-built libraries. PyTorch PyTorch is a popular, open-source, and lightweight machine learning and deep learning framework built on the Lua-based scientific computing framework for machine learning and deep learning algorithms.

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

So for example, in 2015, fidget spinners were all the rage. Now the key insight that we had in solving this is that we noticed that unseen concepts are actually well clustered by pre-trained deep learning models or foundation models. So this might come up if we’re a social media site and we’re trying to do a recommendation.

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

So for example, in 2015, fidget spinners were all the rage. Now the key insight that we had in solving this is that we noticed that unseen concepts are actually well clustered by pre-trained deep learning models or foundation models. So this might come up if we’re a social media site and we’re trying to do a recommendation.