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The “Zero-Shot” Mirage: How Data Scarcity Limits Multimodal AI

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Don’t Forget to join our 40k+ ML SubReddit The post The “Zero-Shot” Mirage: How Data Scarcity Limits Multimodal AI appeared first on MarkTechPost. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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Meet LP-MusicCaps: A Tag-to-Pseudo Caption Generation Approach with Large Language Models to Address the Data Scarcity Issue in Automatic Music Captioning

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The post Meet LP-MusicCaps: A Tag-to-Pseudo Caption Generation Approach with Large Language Models to Address the Data Scarcity Issue in Automatic Music Captioning appeared first on MarkTechPost.

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The AI resource challenge: It’s infrastructure & compute, not data scarcity

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Where would you look for a 2023 state of AI infrastructure analysis, if you really needed one? The answer should be obvious, of course, it’s Tel Aviv …

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CMU Researchers Release Pangea-7B: A Fully Open Multimodal Large Language Models MLLMs for 39 Languages

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The dataset was designed to address the major challenges of multilingual multimodal learning: data scarcity, cultural nuances, catastrophic forgetting, and evaluation complexity. Moreover, PANGEA matches or even outperforms proprietary models like Gemini-1.5-Pro

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Boosting Classification Accuracy: Integrating Transfer Learning and Data Augmentation for Enhanced Machine Learning Performance

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Together, these techniques mitigate the issues of limited target data, improving the model’s adaptability and accuracy. A recent paper published by a Chinese research team proposes a novel approach to combat data scarcity in classification tasks within target domains. Check out the Paper.

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Google DeepMind Researchers Introduce Diffusion Augmented Agents: A Machine Learning Framework for Efficient Exploration and Transfer Learning

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A major issue in RL is the data scarcity in embodied AI, where agents must interact with physical environments. This problem is exacerbated by the need for substantial reward-labeled data to train agents effectively.

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UC Berkeley Research Presents a Machine Learning System that Can Forecast at Near Human Levels

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However, judgmental forecasting has introduced a nuanced approach, leveraging human intuition, domain knowledge, and diverse information sources to predict future events under data scarcity and uncertainty. The challenge in predictive forecasting lies in its inherent complexity and the limitations of existing methodologies.