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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 501–525 of 9051 papers

TitleStatusHype
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding PerspectiveCode1
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language ModelsCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language ModelCode1
Improve Student's Reasoning Generalizability through Cascading Decomposed CoTs DistillationCode1
SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified FlowCode1
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source DataCode1
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing ConstraintsCode1
Dataset GrowthCode1
Learning diverse attacks on large language models for robust red-teaming and safety tuningCode1
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word ExclusionCode1
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI DataCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time SeriesCode1
Graph Neural PDE Solvers with Conservation and Similarity-EquivarianceCode1
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign UsersCode1
Learning to Transform Dynamically for Better Adversarial TransferabilityCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction TuningCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
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