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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 1351–1375 of 9051 papers

TitleStatusHype
Building a Conversational Agent Overnight with Dialogue Self-PlayCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single SampleCode1
Diverse Semantic Image Synthesis via Probability Distribution ModelingCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Diverse Video Generation using a Gaussian Process TriggerCode1
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3DCode1
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph ExpertsCode1
ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid MotionsCode1
Diversified Adversarial Attacks based on Conjugate Gradient MethodCode1
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot FillingCode1
C^2: Scalable Auto-Feedback for LLM-based Chart GenerationCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Diversified Batch Selection for Training AccelerationCode1
Diversifying Dialog Generation via Adaptive Label SmoothingCode1
Nutrition5k: Towards Automatic Nutritional Understanding of Generic FoodCode1
Oasis: One Image is All You Need for Multimodal Instruction Data SynthesisCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Harvesting Event Schemas from Large Language ModelsCode1
Hierarchical Quality-Diversity for Online Damage RecoveryCode1
GUARD: A Safe Reinforcement Learning BenchmarkCode1
Group-wise Inhibition based Feature Regularization for Robust ClassificationCode1
GS-Blur: A 3D Scene-Based Dataset for Realistic Image DeblurringCode1
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