SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4751–4775 of 9051 papers

TitleStatusHype
Investigating the Effects of Large-Scale Pseudo-Stereo Data and Different Speech Foundation Model on Dialogue Generative Spoken Language Model—0
Investigating the Impact of Text Summarization on Topic Modeling—0
Towards Algorithmic Transparency: A Diversity Perspective—0
Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles—0
Investigating the Relationship between Multi-Party Linguistic Entrainment, Team Characteristics, and the Perception of Team Social Outcomes—0
Investors Embrace Gender Diversity, Not Female CEOs: The Role of Gender in Startup Fundraising—0
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection—0
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts—0
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset—0
Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN—0
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data—0
IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization—0
IRS-Assisted Massive MIMO-NOMA Networks with Polarization Diversity—0
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model—0
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble—0
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Towards a Probabilistic Fusion Approach for Robust Battery Prognostics—0
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)—0
Towards a Similarity-adjusted Surprisal Theory—0
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI—0
Towards assessing agricultural land suitability with causal machine learning—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
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering—0
Show:102550
← PrevPage 191 of 363Next →

No leaderboard results yet.