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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 1801–1850 of 9051 papers

TitleStatusHype
Information Density Principle for MLLM BenchmarksCode0
A Simple Method for Commonsense ReasoningCode0
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic ManuscriptsCode0
A Hierarchical Deep Learning Approach for Minority Instrument DetectionCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
LAD: Language Models as Data for Zero-Shot DialogCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
A Simple, Fast Diverse Decoding Algorithm for Neural GenerationCode0
IndicEval-XL: Bridging Linguistic Diversity in Code Generation Across Indic LanguagesCode0
Computational detection of antigen specific B cell receptors following immunizationCode0
Incubating Text Classifiers Following User Instruction with Nothing but LLMCode0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
Interactive Neural Style Transfer with ArtistsCode0
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence PairsCode0
A Guide for Practical Use of ADMG Causal Data AugmentationCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
A simple and effective hybrid genetic search for the job sequencing and tool switching problemCode0
Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class PriorsCode0
Large-Scale Auto-Regressive Modeling Of Street NetworksCode0
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region FittingCode0
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender DiversityCode0
Improving Screening Processes via Calibrated Subset SelectionCode0
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion ModelingCode0
A Grid-Based Evolutionary Algorithm for Many-Objective OptimizationCode0
AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question AnsweringCode0
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationCode0
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy LossCode0
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddingsCode0
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation SystemsCode0
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract DescriptionsCode0
LawDNet: Enhanced Audio-Driven Lip Synthesis via Local Affine Warping DeformationCode0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point ProcessesCode0
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-TuningCode0
Improving Language Generation with Sentence Coherence ObjectiveCode0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
A Benchmark Database of Phonetic Alignments in Historical Linguistics and DialectologyCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Improving Generalization with Domain Convex GameCode0
Improving Neural Language Modeling via Adversarial TrainingCode0
A Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationCode0
Controllable Motion Generation via Diffusion Modal CouplingCode0
Improving Diversity of Commonsense Generation by Large Language Models via In-Context LearningCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Complex Locomotion Skill Learning via Differentiable PhysicsCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
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