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Deep Learning Techniques for Autonomous Driving: An Overview

Marktechpost

Conclusion: In the realm of autonomous driving, several open challenges persist, all of which can be addressed with the help of Deep Learning and AI: Perception: Deep learning enhances object detection and recognition accuracy, but future systems should aim for increased detail recognition and improved camera and LiDAR data integration.

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Amazon AI Research Introduces BioBRIDGE: A Parameter-Efficient Machine Learning Framework to Bridge Independently Trained Unimodal Foundation Models to Establish Multimodal Behavior

Marktechpost

By aligning the embedding space of unimodal FMs through cross-modal transformation models utilizing KG triplets, BioBRIDGE maintains data sufficiency and efficiency and navigates the challenges posed by computational costs and data scarcity that hinder the scalability of multimodal approaches. Check out the Paper.

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Innovation in Synthetic Data Generation: Building Foundation Models for Specific Languages

Unite.AI

However, generating synthetic data for NLP is non-trivial, demanding high linguistic knowledge, creativity, and diversity. Different methods, such as rule-based and data-driven approaches, have been proposed to generate synthetic data.

NLP 173
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Deep Learning for Medical Image Analysis: Current Trends and Future Directions

Heartbeat

Data Scarcity and Quality Issues in Medical Imaging One significant challenge in medical image analysis is the need for labeled data, especially for rare diseases or specific patient populations.