Marktechpost

MARCH 11, 2024

Deep Neural Networks (DNNs) excel in enhancing surgical precision through semantic segmentation and accurately identifying robotic instruments and tissues. However, they face catastrophic forgetting and a rapid decline in performance on previous tasks when learning new ones, posing challenges in scenarios with limited data.

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The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continual learning?

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Unite.AI

JULY 25, 2023

Known as “catastrophic forgetting” in AI terms, this phenomenon severely impedes the progress of machine learning , mimicking the elusive nature of human memories. This insight is pivotal in understanding how continual learning can be optimized in machines to closely resemble the cognitive capabilities of humans.

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Pickl AI

APRIL 19, 2024

Select the right learning path tailored to your goals and preferences. Continuous learning is critical to becoming an AI expert, so stay updated with online courses, research papers, and workshops. Specialise in domains like machine learning or natural language processing to deepen expertise.

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Viso.ai

APRIL 29, 2024

STNs are used to “teach” neural networks how to perform spatial transformations on input data to improve spatial invariance. Commonly Used Technologies and Frameworks For Spatial Transformer Networks When it comes to implementation, the usual suspects, TensorFlow and PyTorch , are the go-to backbone for STNs.

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Unite.AI

AUGUST 28, 2023

Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience. Deep learning techniques further enhanced this, enabling sophisticated image and speech recognition. ” BabyAGI responded with a well-thought-out plan.

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The Stanford AI Lab Blog

APRIL 25, 2022

Manning, Jure Leskovec Contact : xikunz2@cs.stanford.edu Award nominations: Spotlight Links: Paper | Website Keywords : knowledge graph, question answering, language model, commonsense reasoning, graph neural networks, biomedical qa Fast Model Editing at Scale Authors : Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D.

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