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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 3826–3850 of 9051 papers

TitleStatusHype
Layer-diverse Negative Sampling for Graph Neural Networks—0
X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment—0
InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions—0
Just Add $100 More: Augmenting NeRF-based Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem—0
Audio-Visual Segmentation via Unlabeled Frame Exploitation—0
FORCE: Physics-aware Human-object Interaction—0
Simple 2D Convolutional Neural Network-based Approach for COVID-19 DetectionCode0
THOR: Text to Human-Object Interaction Diffusion via Relation Intervention—0
An upper bound of the mutation probability in the genetic algorithm for general 0-1 knapsack problem—0
Phasic Diversity Optimization for Population-Based Reinforcement Learning—0
Just Say the Name: Online Continual Learning with Category Names Only via Data Generation—0
Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization—0
AI-enhanced Collective IntelligenceCode0
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification—0
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model—0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation—0
Pantypes: Diverse Representatives for Self-Explainable ModelsCode0
SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different LanguagesCode0
Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookupCode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation—0
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models—0
Federated Data Model—0
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