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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 3526–3550 of 9051 papers

TitleStatusHype
Benchmarking the Performance of Pre-trained LLMs across Urdu NLP Tasks—0
MallowsPO: Fine-Tune Your LLM with Preference Dispersions—0
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings—0
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and GeneralizationCode0
DIDI: Diffusion-Guided Diversity for Offline Behavioral GenerationCode0
Naturally Private Recommendations with Determinantal Point Processes—0
Towards Exploratory Quality Diversity Landscape Analysis—0
LookHere: Vision Transformers with Directed Attention Generalize and ExtrapolateCode0
Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning—0
Lessons to learn for better safeguarding of genetic resources during tree pandemics: the case of ash dieback in Europe—0
Traffic Scenario Logic: A Spatial-Temporal Logic for Modeling and Reasoning of Urban Traffic ScenariosCode0
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis—0
Visual Analysis of Prediction Uncertainty in Neural Networks for Deep Image Synthesis—0
No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models—0
Multiple Realizability and the Rise of Deep Learning—0
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in TextCode0
Orthogonally Initiated Particle Swarm Optimization with Advanced Mutation for Real-Parameter Optimization—0
Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking—0
A Workbench for Autograding Retrieve/Generate SystemsCode0
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question AnsweringCode0
CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models—0
Asymptotic theory of in-context learning by linear attentionCode0
Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual LearningCode0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—0
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