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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 2501–2525 of 9051 papers

TitleStatusHype
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language RepresentationCode0
Coreset Selection for Object Detection—0
Understanding the Role of Temperature in Diverse Question Generation by GPT-4—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
Fault Detection in Mobile Networks Using Diffusion Models—0
The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation—0
Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning—0
MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter ExpertsCode5
Improved Paraphrase Generation via Controllable Latent DiffusionCode0
Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping—0
RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning—0
OmniSat: Self-Supervised Modality Fusion for Earth ObservationCode2
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic SegmentationCode0
Direct May Not Be the Best: An Incremental Evolution View of Pose GenerationCode0
Analyzing and Overcoming Local Optima in Complex Multi-Objective Optimization by Decomposition-Based Evolutionary Algorithms—0
Generalization Gap in Data Augmentation: Insights from Illumination—0
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization—0
WaveMo: Learning Wavefront Modulations to See Through Scattering—0
MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceCode0
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context LearningCode0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
Achieving Tight O(4^k) Runtime Bounds on Jump_k by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity—0
Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices—0
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial NetworksCode0
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
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