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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 401–425 of 9051 papers

TitleStatusHype
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-GuidanceCode1
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data DiversityCode1
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory PredictionCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism via Dual Diffusion Models and GPT PromptingCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic DataCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
Style-Specific Neurons for Steering LLMs in Text Style TransferCode1
MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal AssistantsCode1
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint DetectionCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn InteractionCode1
Content-aware Tile Generation using Exterior Boundary InpaintingCode1
Massively Multi-Person 3D Human Motion Forecasting with Scene ContextCode1
LOLA -- An Open-Source Massively Multilingual Large Language ModelCode1
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identificationCode1
UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV DetectionCode1
Planning In Natural Language Improves LLM Search For Code GenerationCode1
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