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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 1331–1340 of 9051 papers

TitleStatusHype
Deep Generative Models for 3D Medical Image Synthesis—0
Differentially Private Learning Needs Better Model Initialization and Self-DistillationCode0
R-CoT: Reverse Chain-of-Thought Problem Generation for Geometric Reasoning in Large Multimodal ModelsCode5
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion AugmentationCode0
Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models—0
AutoRNet: Automatically Optimizing Heuristics for Robust Network Design via Large Language Models—0
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
Annotation-Free MIDI-to-Audio Synthesis via Concatenative Synthesis and Generative Refinement—0
Audio-to-Score Conversion Model Based on Whisper methodology—0
MiniPLM: Knowledge Distillation for Pre-Training Language ModelsCode2
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