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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 5101–5125 of 9051 papers

TitleStatusHype
Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity—0
Saliency Diversified Deep Ensemble for Robustness to Adversaries—0
Salient Slices: Improved Neural Network Training and Performance with Image Entropy—0
Sammaan@LT-EDI-ACL2022: Ensembled Transformers Against Homophobia and Transphobia—0
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks—0
Sample efficient Quality Diversity for neural continuous control—0
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek—0
SampleFix: Learning to Generate Functionally Diverse Fixes—0
SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity—0
Sampling Approach Matters: Active Learning for Robotic Language Acquisition—0
Sampling from Probabilistic Submodular Models—0
Contextually Plausible and Diverse 3D Human Motion Prediction—0
Experiences from Creating a Benchmark for Sentiment Classification for Varieties of English—0
Sampling Strategies for GAN Synthetic Data—0
Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets—0
Efficient and Training-Free Control of Language Generation—0
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.—0
SANE: Specialization-Aware Neural Network Ensemble—0
SAOR: Single-View Articulated Object Reconstruction—0
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection—0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping—0
SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models—0
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation—0
Says Who ? Identification of Expert versus Layman Critics' Reviews of Documentary Films—0
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