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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 626–650 of 9051 papers

TitleStatusHype
Diverse Video Generation using a Gaussian Process TriggerCode1
Diverse Image-to-Image Translation via Disentangled RepresentationsCode1
Diversified Batch Selection for Training AccelerationCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
A Diverse Corpus for Evaluating and Developing English Math Word Problem SolversCode1
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
Diverse Policy Optimization for Structured Action SpaceCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
Diversity-Aware Meta Visual PromptingCode1
Diversity-based Trajectory and Goal Selection with Hindsight Experience ReplayCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
DLow: Diversifying Latent Flows for Diverse Human Motion PredictionCode1
Bias Loss for Mobile Neural NetworksCode1
An Extensible Benchmark Suite for Learning to Simulate Physical SystemsCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
Biological Sequence Design with GFlowNetsCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
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