Remove AI Research Remove Computer Vision Remove Natural Language Processing
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A Comprehensive Guide on i-Transformer

Analytics Vidhya

Introduction Transformers have revolutionized various domains of machine learning, notably in natural language processing (NLP) and computer vision. Their ability to capture long-range dependencies and handle sequential data effectively has made them a staple in every AI researcher and practitioner’s toolbox.

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20 GitHub Repositories to Master Natural Language Processing (NLP)

Marktechpost

Natural Language Processing (NLP) is a rapidly growing field that deals with the interaction between computers and human language. Transformers is a state-of-the-art library developed by Hugging Face that provides pre-trained models and tools for a wide range of natural language processing (NLP) tasks.

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Microsoft Research Introduces Florence-2: A Novel Vision Foundation Model with a Unified Prompt-based Representation for a Variety of Computer Vision and Vision-Language Tasks

Marktechpost

Natural language processing (NLP) is a good example of this tendency since sophisticated models demonstrate flexibility with thorough knowledge covering several domains and tasks with straightforward instructions. The popularity of NLP encourages a complementary strategy in computer vision.

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Rethinking Reproducibility As the New Frontier in AI Research

Unite.AI

In particular, the instances of irreproducible findings, such as in a review of 62 studies diagnosing COVID-19 with AI , emphasize the necessity to reevaluate practices and highlight the significance of transparency. Multiple factors contribute to the reproducibility crisis in AI research.

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A New AI Research Proposes VanillaNet: A Novel Neural Network Architecture Emphasizing the Elegance and Simplicity of Design while Retaining Remarkable Performance in Computer Vision Tasks

Marktechpost

These networks may carry out a range of human-like activities, including face recognition, speech recognition, object identification, natural language processing, and content synthesis, which include several layers and a lot of neurons or transformer blocks.

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AI trends in 2023: Graph Neural Networks

AssemblyAI

What is the current role of GNNs in the broader AI research landscape? Let’s take a look at some numbers revealing how GNNs have seen a spectacular rise within the research community. We find that the term Graph Neural Network consistently ranked in the top 3 keywords year over year.

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Can Computer Vision Systems Infer Your Muscle Activity from Video? Meet Muscles in Action (MIA): A New Dataset to Learn to Incorporate Muscle Activity into Human Motion Representations

Marktechpost

Be it the human-imitating Large Language Model like GPT 3.5 based on Natural Language Processing and Natural Language Understanding or the text-to-image model called DALL-E based on Computer vision, AI is paving its way toward success.