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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 2501–2525 of 9051 papers

TitleStatusHype
Chaotic turnover of rare and abundant species in a strongly interacting model community—0
A Path to Simpler Models Starts With Noise—0
Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes—0
Channel Self-Supervision for Online Knowledge Distillation—0
Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system—0
A Compressive Memory-based Retrieval Approach for Event Argument Extraction—0
Channel Estimation for MIMO Space Time Coded OTFS under Doubly Selective Channels—0
A Parameterized Family of Meta-Submodular Functions—0
Adversarial Text-to-Image Synthesis: A Review—0
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
Chameleon: On the Scene Diversity and Domain Variety of AI-Generated Videos Detection—0
A Paradigm Shift in Mouza Map Vectorization: A Human-Machine Collaboration Approach—0
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining—0
Does the dataset meet your expectations? Explaining sample representation in image data—0
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation—0
Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation—0
Challenges in the Knowledge Base Population Slot Filling Task—0
Challenges Encountered in Turkish Natural Language Processing Studies—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Challenges and Strategies in Cross-Cultural NLP—0
Challenges and Solutions in AI for All—0
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research—0
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning—0
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method—0
DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization—0
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