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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

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Showing 4061–4070 of 9051 papers

TitleStatusHype
Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas system—0
Creative Discovery using QD SearchCode0
Reinforcement Learning for Topic ModelsCode0
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
Adaptive Learning Path Navigation Based on Knowledge Tracing and Reinforcement Learning—0
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized DiversityCode1
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer—0
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model—0
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human SupervisionCode3
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