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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 1926–1950 of 9051 papers

TitleStatusHype
CREA: A Collaborative Multi-Agent Framework for Creative Content Generation with Diffusion Models—0
Creating a Basic Language Resource Kit for Faroese—0
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue—0
Creating and Repairing Robot Programs in Open-World Domains—0
Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction—0
AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI—0
Alibaba Submission to the WMT20 Parallel Corpus Filtering Task—0
Creative Preference Optimization—0
Creativity Has Left the Chat: The Price of Debiasing Language Models—0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model—0
Conformity bias in the cultural transmission of music sampling traditions—0
Audio-Visual Segmentation via Unlabeled Frame Exploitation—0
Configuring Antenna System to Enhance the Downlink Performance of High Velocity Users in 5G MU-MIMO Networks—0
Cross-Cutting Political Awareness through Diverse News Recommendations—0
Cross-Dataset Generalization in Deep Learning—0
Assessing fish abundance from underwater video using deep neural networks—0
ConfigTron: Tackling network diversity with heterogeneous configurations—0
Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification—0
Assessing Distractors in Multiple-Choice Tests—0
Auditing Source Diversity Bias in Video Search Results Using Virtual Agents—0
A blindspot of AI ethics: anti-fragility in statistical prediction—0
Defining and Counting Phonological Classes in Cross-linguistic Segment Databases—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
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