AI Modeling Data Scarcity Deep Learning 
Pickl AI
FEBRUARY 1, 2023
Transfer Learning in Deep Learning: A Brief Overview Collecting large volumes of data, filtering it and then interpreting is a challenging task. What if we say that you have the option of using a pre-trained model that works as a framework for data training? Yes, Transfer Learning is the answer to it.
Marktechpost
DECEMBER 31, 2023
He highlighted the necessity for effective data use by stressing the significant amount of data many AI systems consume. Another researcher highlighted the challenge of considering AI model-free due to market data scarcity for training, particularly in realistic derivative markets.
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AssemblyAI
SEPTEMBER 22, 2023
In August – Meta released a tool for AI-generated audio named AudioCraft and open-sourced all of its underlying models, including MusicGen. Last week – StabilityAI launched StableAudio , a subscription-based platform for creating music with AI models.
Topbots
DECEMBER 19, 2023
The findings indicate that alleged emergent abilities might evaporate under different metrics or more robust statistical methods, suggesting that such abilities may not be fundamental properties of scaling AI models. The paper also explores alternative strategies to mitigate data scarcity.
Pickl AI
MAY 29, 2024
Multi-Task Learning Deep Learning is a towering pillar in the vast landscape of artificial intelligence, revolutionising various domains with remarkable capabilities. Deep Learning algorithms have become integral to modern technology, from image recognition to Natural Language Processing.
Viso.ai
DECEMBER 18, 2023
Instead of relying on organic events, we generate this data through computer simulations or generative models. Synthetic data can augment existing datasets, create new datasets, or simulate unique scenarios. Specifically, it solves two key problems: data scarcity and privacy concerns. Technique No.
John Snow Labs
AUGUST 7, 2024
This breakthrough enabled the generation of data and images that have since played a crucial role in training medical professionals and developing diagnostic tools while maintaining patient privacy. They simulate trials predict responses and generate synthetic biological data to accelerate research while ensuring safety and effectiveness.
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