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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 6176–6200 of 9051 papers

TitleStatusHype
We Can't Understand AI Using our Existing Vocabulary—0
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation—0
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks—0
Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery—0
Weighted Ensemble Self-Supervised Learning—0
Weighted Spectral Cluster Ensemble—0
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization—0
Weight Ensembling Improves Reasoning in Language Models—0
Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification—0
WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data—0
Well-temperate phage: optimal bet-hedging against local environmental collapses—0
We Need to Measure Data Diversity in NLP -- Better and Broader—0
We Need to Talk About Classification Evaluation Metrics in NLP—0
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)—0
What are Public Concerns about ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You—0
What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders—0
What are you optimizing for? Aligning Recommender Systems with Human Values—0
What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices—0
What leads to generalization of object proposals?—0
What Makes for Good Representations for Contrastive Learning—0
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations—0
What Matters in Learning from Large-Scale Datasets for Robot Manipulation—0
What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning—0
"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models—0
What the %PCSA? Addressing Diversity in Lower-Limb Musculoskeletal Models: Age- and Sex-related Differences in PCSA and Muscle Mass—0
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