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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 5901–5950 of 9051 papers

TitleStatusHype
Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation—0
Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction—0
Training on test data: Removing near duplicates in Fashion-MNIST—0
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data—0
Training Task Experts through Retrieval Based Distillation—0
Estimation of functional diversity and species traits from ecological monitoring data—0
Trajectory First: A Curriculum for Discovering Diverse Policies—0
Transaction Fraud Detection via an Adaptive Graph Neural Network—0
Transferable Query Selection for Active Domain Adaptation—0
Transfer Dynamics in Emergent Evolutionary Curricula—0
Transfer Learning by Modeling a Distribution over Policies—0
Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI—0
Transfer learning for ensembles: reducing computation time and keeping the diversity—0
Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy—0
Transforming Multi-Concept Attention into Video Summarization—0
Transformers are Minimax Optimal Nonparametric In-Context Learners—0
Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning—0
Transmitter Diversity with Beam Steering—0
Trap of Feature Diversity in the Learning of MLPs—0
Bugs as Features (Part I): Concepts and Foundations for the Compositional Data Analysis of the Microbiome-Gut-Brain Axis—0
Tree balance in phylogenetic models—0
Tree of Reviews: A Tree-based Dynamic Iterative Retrieval Framework for Multi-hop Question Answering—0
Tree-Structured Reinforcement Learning for Sequential Object Localization—0
TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning—0
Trend-responsive User Segmentation Enabling Traceable Publishing Insights. A Case Study of a Real-world Large-scale News Recommendation System—0
TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi—0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
TroubleLLM: Align to Red Team Expert—0
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness—0
TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population—0
TRUNet: Transformer-Recurrent-U Network for Multi-channel Reverberant Sound Source Separation—0
TrustAL: Trustworthy Active Learning using Knowledge Distillation—0
TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics—0
Trying AGAIN instead of Trying Longer: Prior Learning for Automatic Curriculum Learning—0
TSMD: A Database for Static Color Mesh Quality Assessment Study—0
TSO: Self-Training with Scaled Preference Optimization—0
Tuning-Free Amodal Segmentation via the Occlusion-Free Bias of Inpainting Models—0
Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion—0
Turbo-ICL: In-Context Learning-Based Turbo Equalization—0
Turbo-Sim: a generalised generative model with a physical latent space—0
Turning the Tide: Repository-based Code Reflection—0
Position: AI/ML Influencers Have a Place in the Academic Process—0
Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions—0
Twitter Bot Detection using Diversity Measures—0
Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization—0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Two Kinds of Recall—0
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems—0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
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