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Fine-Tuning NVIDIA NV-Embed-v1 on Amazon Polarity Dataset Using LoRA and PEFT: A Memory-Efficient Approach with Transformers and Hugging Face

Marktechpost

Whether you’re working on product review classification, AI-driven recommendation systems, or domain-specific search engines, this method allows you to fine-tune large-scale models on a budget efficiently. Here is the Colab Notebook for the above project. Here is the Colab Notebook for the above project.

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UC Berkeley Researchers Propose CRATE: A Novel White-Box Transformer for Efficient Data Compression and Sparsification in Deep Learning

Marktechpost

Such a representation makes many subsequent tasks, including those involving vision, classification, recognition and segmentation, and generation, easier. Therefore, encoders, decoders, and auto-encoders can all be implemented using a roughly identical crate design. All credit for this research goes to the researchers of this project.

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Snorkel AI researchers present 18 papers at NeurIPS 2023

Snorkel AI

Benchmarks, domain-specific datasets, and models Benchmarking drives progress in AI research. Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification Guha et al. A case for reframing automated medical image classification as segmentation Hooper et al.

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This AI Paper Unveils X-Raydar: A Groundbreaking Open-Source Deep Neural Networks for Chest X-Ray Abnormality Detection

Marktechpost

The AI algorithms were evaluated on three retrospective datasets, demonstrating similar performance to historical clinical radiologist reporters for various clinically important findings. The X-Raydar achieved a mean AUC of 0.919 on the auto-labeled set, 0.864 on the consensus set, and 0.842 on the MIMIC-CXR test.

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Snorkel AI researchers present 18 papers at NeurIPS 2023

Snorkel AI

Benchmarks, domain-specific datasets, and models Benchmarking drives progress in AI research. Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification Guha et al. A case for reframing automated medical image classification as segmentation Hooper et al.

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Breaking Boundaries in 3D Instance Segmentation: An Open-World Approach with Improved Pseudo-Labeling and Realistic Scenarios

Marktechpost

By providing object instance-level classification and semantic labeling, 3D semantic instance segmentation tries to identify items in a given 3D scene represented by a point cloud or mesh. They use an auto-labeling approach to distinguish between known and unknowable class labels to produce pseudo-labels during training.

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DeepMind AI Supercharges YouTube Shorts Exposure by Auto-Generating Descriptions for Millions of Videos

Marktechpost

” This generated text is stored as metadata, enabling more efficient video classification and facilitating search engine accessibility. The impact of Flamingo has already been felt, as hundreds of thousands of newly uploaded Shorts videos have benefited from AI-generated descriptions.