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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 1376–1400 of 9051 papers

TitleStatusHype
Improving the Estimation of Attenuation in Q/V Band Systems with a Kalman-Based Scintillation Filter—0
PUMA: Empowering Unified MLLM with Multi-granular Visual GenerationCode2
Instruction-Driven Game Engine: A Poker Case Study—0
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language ModelsCode2
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-GuidanceCode1
The Disparate Benefits of Deep EnsemblesCode0
Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language—0
360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote SensingCode1
ERVQ: Enhanced Residual Vector Quantization with Intra-and-Inter-Codebook Optimization for Neural Audio Codecs—0
Characterizing Model Collapse in Large Language Models Using Semantic Networks and Next-Token Probability—0
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data DiversityCode1
Multi-trait User Simulation with Adaptive Decoding for Conversational Task AssistantsCode0
Expanding Chatbot Knowledge in Customer Service: Context-Aware Similar Question Generation Using Large Language Models—0
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph—0
Using Protected Attributes to Consider Fairness in Multi-Agent Systems—0
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved Personalization—0
Preference Optimization with Multi-Sample Comparisons—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
Reading Yule in light of the history and present of macroevolution—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
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