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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 7101–7150 of 9051 papers

TitleStatusHype
Non-saturating GAN training as divergence minimization—0
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation—0
No-Regret Caching via Online Mirror Descent—0
NOSE Augment: Fast and Effective Data Augmentation Without Searching—0
Not even wrong: Reply to Wagg et al—0
Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas—0
Novel Methods for Analyzing Cellular Interactions in Deep Learning-Based Image Cytometry: Spatial Interaction Potential and Co-Localization Index—0
NoveltyBench: Evaluating Language Models for Humanlike Diversity—0
NSOAMT -- New Search Only Approach to Machine Translation—0
NU:BRIEF -- A Privacy-aware Newsletter Personalization Engine for Publishers—0
NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation—0
NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimation—0
N-Version Assessment and Enhancement of Generative AI—0
Nyström landmark sampling and regularized Christoffel functions—0
Objaverse: A Universe of Annotated 3D Objects—0
Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding—0
Object detection and tracking benchmark in industry based on improved correlation filter—0
Object Detection as a Positive-Unlabeled Problem—0
Object Detection for Understanding Assembly Instruction Using Context-aware Data Augmentation and Cascade Mask R-CNN—0
Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance—0
Object Scene Representation Transformer—0
Observing the Population Dynamics in GE by means of the Intrinsic Dimension—0
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction—0
Occupational segregation in a Roy model with composition preferences—0
ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in—0
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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