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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 7180 of 9051 papers

TitleStatusHype
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture ModelingCode3
Sequential Modeling Enables Scalable Learning for Large Vision ModelsCode3
Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language ModelCode3
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masksCode3
CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous DrivingCode3
Objaverse-XL: A Universe of 10M+ 3D ObjectsCode3
SVIT: Scaling up Visual Instruction TuningCode3
Self-QA: Unsupervised Knowledge Guided Language Model AlignmentCode3
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human SupervisionCode3
Anything-3D: Towards Single-view Anything Reconstruction in the WildCode3
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