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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 7126–7150 of 9051 papers

TitleStatusHype
Offensive Language Detection on Video Live Streaming Chat—0
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
Offline Learning of Controllable Diverse Behaviors—0
Can Offline Metrics Measure Explanation Goals? A Comparative Survey Analysis of Offline Explanation Metrics in Recommender Systems—0
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management—0
Offline Reinforcement Learning Hands-On—0
Omega: An Architecture for AI Unification—0
Omega: An Architecture for AI Unification—0
Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets—0
Omni-Dimensional Frequency Learner for General Time Series Analysis—0
OmniEval: A Benchmark for Evaluating Omni-modal Models with Visual, Auditory, and Textual Inputs—0
OmniMotionGPT: Animal Motion Generation with Limited Data—0
OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB—0
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning—0
On Accurate Evaluation of GANs for Language Generation—0
On a Scale-Invariant Approach to Bundle Recommendations in Candy Crush Saga—0
On Benefits of Selection Diversity via Bilevel Exclusive Sparsity—0
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models—0
On Decoding Strategies for Neural Text Generators—0
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks—0
On Diverse Asynchronous Activity Anticipation—0
On Diversity in Discriminative Neural Networks—0
On Domain-Specific Post-Training for Multimodal Large Language Models—0
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition—0
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment—0
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