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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 301–350 of 9051 papers

TitleStatusHype
SCAMPS: Synthetics for Camera Measurement of Physiological SignalsCode2
Guiding Generative Protein Language Models with Reinforcement LearningCode2
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based OptimizationCode1
Deep Ordinal Regression with Label DiversityCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
Adapting Precomputed Features for Efficient Graph CondensationCode1
Deep generative selection models of T and B cell receptor repertoires with soNNiaCode1
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task GenerationCode1
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery DetectionCode1
Deep Image Harmonization with Learnable AugmentationCode1
Deep Sketch-Based Modeling: Tips and TricksCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Deep Color Transfer using Histogram AnalogyCode1
Adaptable Agent Populations via a Generative Model of PoliciesCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Dataset Factorization for CondensationCode1
Dataset GrowthCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
A Case for Rejection in Low Resource ML DeploymentCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Dan: Deep attention neural network for news recommendationCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Active Teacher for Semi-Supervised Object DetectionCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Curriculum-guided Hindsight Experience ReplayCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
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