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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 1751–1775 of 9051 papers

TitleStatusHype
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation—0
The Role of Diversity in In-Context Learning for Large Language Models—0
We Need to Measure Data Diversity in NLP -- Better and Broader—0
The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages—0
Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning—0
EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition—0
Holes in Latent Space: Topological Signatures Under Adversarial Influence—0
ReDDiT: Rehashing Noise for Discrete Visual Generation—0
Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals—0
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity—0
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving—0
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks—0
Token-Importance Guided Direct Preference Optimization—0
VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data SynthesisCode0
Less is More: Efficient Point Cloud Reconstruction via Multi-Head Decoders—0
SpokenNativQA: Multilingual Everyday Spoken Queries for LLMs—0
MGD^3: Mode-Guided Dataset Distillation using Diffusion Models—0
PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders—0
Pan-tropical plant functional trait variation from space—0
Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions—0
The Price of Format: Diversity Collapse in LLMsCode0
MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images—0
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