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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 3426–3450 of 9051 papers

TitleStatusHype
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental SegmentationCode1
Understanding the Effects of RLHF on LLM Generalisation and DiversityCode1
Hexa: Self-Improving for Knowledge-Grounded Dialogue System—0
Diversity from Human Feedback—0
Growing ecosystem of deep learning methods for modeling proteinx2013protein interactions—0
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQACode0
Affine Frequency Division Multiplexing With Index Modulation—0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
Increasing Entropy to Boost Policy Gradient Performance on Personalization TasksCode0
SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory PredictionCode1
ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot CoordinationCode2
Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions—0
Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction—0
IPMix: Label-Preserving Data Augmentation Method for Training Robust ClassifiersCode1
QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied ContextsCode0
FM Tone Transfer with Envelope Learning—0
A Holistic Evaluation of Piano Sound Quality—0
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIsCode0
A Process for Topic Modelling Via Word Embeddings—0
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping DatasetsCode1
Knolling Bot: Learning Robotic Object Arrangement from Tidy Demonstrations—0
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
Unbiased estimation of sampling variance for Simpson's diversity indexCode0
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