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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 701–710 of 9051 papers

TitleStatusHype
Understanding the Effects of RLHF on LLM Generalisation and DiversityCode1
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental SegmentationCode1
SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory PredictionCode1
IPMix: Label-Preserving Data Augmentation Method for Training Robust ClassifiersCode1
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping DatasetsCode1
On the Embedding Collapse when Scaling up Recommendation ModelsCode1
Toward a Plug-and-Play Vision-Based Grasping Module for RoboticsCode1
Amortizing intractable inference in large language modelsCode1
Can pre-trained models assist in dataset distillation?Code1
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and BeyondCode1
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