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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 1231–1240 of 9051 papers

TitleStatusHype
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Calliar: An Online Handwritten Dataset for Arabic CalligraphyCode1
Building a Conversational Agent Overnight with Dialogue Self-PlayCode1
Generating Object StampsCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
Large-Vocabulary 3D Diffusion Model with TransformerCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
Generative Adversarial Graph Convolutional Networks for Human Action SynthesisCode1
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