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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 3451–3475 of 9051 papers

TitleStatusHype
Can pre-trained models assist in dataset distillation?Code1
Unbiased estimation of sampling variance for Simpson's diversity indexCode0
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries—0
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization—0
Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data—0
MUNCH: Modelling Unique 'N Controllable Heads—0
Multimodal Question Answering for Unified Information ExtractionCode1
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and BeyondCode1
scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain—0
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning—0
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality—0
Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks—0
Prompting Audios Using Acoustic Properties For Emotion Representation—0
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks—0
Hierarchical Generation of Human-Object Interactions with Diffusion Probabilistic Models—0
Sieve: Multimodal Dataset Pruning Using Image Captioning ModelsCode1
Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks—0
Exploring Model Learning Heterogeneity for Boosting Ensemble RobustnessCode0
Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis—0
Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-IdentificationCode1
GenSim: Generating Robotic Simulation Tasks via Large Language ModelsCode2
No Offense Taken: Eliciting Offensiveness from Language ModelsCode0
LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts—0
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