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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 1901–1925 of 9051 papers

TitleStatusHype
Are Large Language Models Capable of Generating Human-Level Narratives?Code1
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance—0
Conditional Quantile Estimation for Uncertain Watch Time in Short-Video RecommendationCode0
The Fabrication of Reality and Fantasy: Scene Generation with LLM-Assisted Prompt Interpretation—0
Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review—0
On Diversity in Discriminative Neural Networks—0
VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification BenchmarkCode5
RIS-Assisted High Resolution Radar Sensing—0
Learning Semantic Latent Directions for Accurate and Controllable Human Motion PredictionCode1
Better RAG using Relevant Information GainCode0
AU-vMAE: Knowledge-Guide Action Units Detection via Video Masked Autoencoder—0
Repurformer: Transformers for Repurposing-Aware Molecule Generation—0
CIC-BART-SSA: Controllable Image Captioning with Structured Semantic AugmentationCode0
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging—0
Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development—0
Don't Throw Away Data: Better Sequence Knowledge Distillation—0
Omni-Dimensional Frequency Learner for General Time Series Analysis—0
DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion ModelsCode1
Domain Generalization for 6D Pose Estimation Through NeRF-based Image Synthesis—0
Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method—0
Visual Prompt Selection for In-Context Learning SegmentationCode1
Research Experience of an Undergraduate Student in Computer Vision and Robotics—0
Popular News Always Compete for the User's Attention! POPK: Mitigating Popularity Bias via a Temporal-Counterfactual—0
Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues—0
MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-ExpertsCode0
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