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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

Showing 14261450 of 9051 papers

TitleStatusHype
Metric Space Magnitude for Evaluating the Diversity of Latent RepresentationsCode1
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-TuningCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM ImagesCode1
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error CorrectionCode1
MLGO: a Machine Learning Guided Compiler Optimizations FrameworkCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment DatabaseCode1
MMRel: A Relation Understanding Benchmark in the MLLM EraCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Answering Ambiguous Questions via Iterative PromptingCode1
New Protocols and Negative Results for Textual Entailment Data CollectionCode1
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language BenchmarkCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Monocular Human-Object Reconstruction in the WildCode1
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story GenerationCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Color Space Learning for Cross-Color Person Re-IdentificationCode1
Multi-head Attention-based Deep Multiple Instance LearningCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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