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Microsoft Solves the Problem of LLM Data Scarcity

Flipboard

Small models have shown promise over the last few months, and we are now finally getting to see what they are truly capable of thanks to Microsoft,

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

Marktechpost

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

Marktechpost

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.

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

Marktechpost

RL applications range from game playing to robotic control, making it essential for researchers to develop efficient and scalable learning methods. A major issue in RL is the data scarcity in embodied AI, where agents must interact with physical environments.

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Innovations in Analytics: Elevating Data Quality with GenAI

Towards AI

By leveraging GenAI, we can streamline and automate data-cleaning processes: Clean data to use AI? Clean data through GenAI! Three ways to use GenAI for better data Improving data quality can make it easier to apply machine learning and AI to analytics projects and answer business questions.

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This AI Paper Proposes FLORA: A Novel Machine Learning Approach that Leverages Federated Learning and Parameter-Efficient Adapters to Train Visual-Language Models VLMs

Marktechpost

Also, FLORA’s efficiency analysis shows that it uses much less memory and communication compared to baseline methods, which shows that it could be used in real-world federated learning situations. In conclusion, FLORA presents a promising solution to the challenge of training vision-language models in federated learning settings.

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Harnessing Machine Learning for Advanced Bioprocess Development: From Data-Driven Optimization to Real-Time Monitoring

Marktechpost

Application of Machine Learning in Bioprocess Development: ML has profoundly impacted bioprocess development, particularly in strain selection and engineering stages. ML techniques like support vector machine (SVM) regression and Gaussian process (GP) regression predict optimal conditions for enzymatic activities and media composition.