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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 2951–2975 of 9051 papers

TitleStatusHype
Orthogonal Finetuning for Direct Preference Optimization—0
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment BenchmarkingCode0
MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations—0
The Impact of Generative Artificial Intelligence on Ideation and the performance of Innovation Teams (Preprint)—0
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning—0
LDPC Codes in Cooperative Communication—0
MANTA -- Model Adapter Native generations that's Affordable—0
RobotFingerPrint: Unified Gripper Coordinate Space for Multi-Gripper Grasp Synthesis and Transfer—0
Reliable and diverse evaluation of LLM medical knowledge mastery—0
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data CollectionCode0
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder—0
SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval—0
PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion CaptureCode0
N-Version Assessment and Enhancement of Generative AI—0
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank—0
Present and Future Generalization of Synthetic Image DetectorsCode0
SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation—0
ControlMath: Controllable Data Generation Promotes Math Generalist Models—0
Exploring the Performance-Reproducibility Trade-off in Quality-DiversityCode0
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models—0
Imagine yourself: Tuning-Free Personalized Image Generation—0
Higher-Order Message Passing for Glycan Representation LearningCode0
Adaptive Mixture Importance Sampling for Automated Ads Auction Tuning—0
Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC—0
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models—0
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