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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 7851–7900 of 9051 papers

TitleStatusHype
SetGAN: Improving the stability and diversity of generative models through a permutation invariant architecture—0
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation—0
Conformity bias in the cultural transmission of music sampling traditions—0
Reducing Popularity Bias in Recommendation Over Time—0
Re-ranking Based Diversification: A Unifying View—0
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems—0
SampleFix: Learning to Generate Functionally Diverse Fixes—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
DAL: Dual Adversarial Learning for Dialogue Generation—0
Harnessing behavioral diversity to understand circuits for cognition—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI ModelsCode0
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
Bayesian Optimisation over Multiple Continuous and Categorical InputsCode0
5G framework concepts for the next generation networks—0
Submodular Batch Selection for Training Deep Neural NetworksCode0
GAIT: A Geometric Approach to Information TheoryCode0
Training on test data: Removing near duplicates in Fashion-MNIST—0
Batch Active Learning Using Determinantal Point ProcessesCode0
Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables—0
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles—0
A concise guide to existing and emerging vehicle routing problem variants—0
Neural Response Generation with Meta-Words—0
Comparison of Diverse Decoding Methods from Conditional Language ModelsCode0
Flexible Modeling of Diversity with Strongly Log-Concave DistributionsCode0
A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble—0
Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Simultaneously Learning Architectures and Features of Deep Neural Networks—0
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task—0
Improving Neural Language Modeling via Adversarial TrainingCode0
Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms—0
Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data—0
Patch Transformer for Multi-tagging Whole Slide Histopathology Images—0
Argument Generation with Retrieval, Planning, and Realization—0
Transfer Learning by Modeling a Distribution over Policies—0
Ensemble Pruning via Margin Maximization—0
Enhanced Optimization with Composite Objectives and Novelty Pulsation—0
Conversing by Reading: Contentful Neural Conversation with On-demand Machine ReadingCode0
A database for face presentation attack using wax figure faces—0
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in VideoCode0
SParC: Cross-Domain Semantic Parsing in ContextCode0
Attributed Graph Clustering via Adaptive Graph ConvolutionCode0
Balanced Ranking with Diversity Constraints—0
Lattice-Based Transformer Encoder for Neural Machine Translation—0
Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasksCode0
Masked Non-Autoregressive Image Captioning—0
Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems—0
``Caption'' as a Coherence Relation: Evidence and Implications—0
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