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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 8851–8875 of 9051 papers

TitleStatusHype
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?Code0
exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment ProblemCode0
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment ApproachCode0
Style Variable and Irrelevant Learning for Generalizable Person Re-identificationCode0
Automatic Generation of Fashion Images using Prompting in Generative Machine Learning ModelsCode0
Detecting Visual Relationships with Deep Relational NetworksCode0
Revisiting Few-Shot Learning from a Causal PerspectiveCode0
DESTEIN: Navigating Detoxification of Language Models via Universal Steering Pairs and Head-wise Activation FusionCode0
Revisiting Knowledge Distillation under Distribution ShiftCode0
Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerceCode0
Exact Fusion via Feature Distribution Matching for Few-shot Image GenerationCode0
Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge DistillationCode0
Sub-Architecture Ensemble Pruning in Neural Architecture SearchCode0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
Can Generative Models Improve Self-Supervised Representation Learning?Code0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity AlgorithmCode0
Describing like humans: on diversity in image captioningCode0
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup IdentificationCode0
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta LearningCode0
Evolutionary Generative Adversarial NetworksCode0
DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology DiagnosisCode0
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parametersCode0
DELINE8K: A Synthetic Data Pipeline for the Semantic Segmentation of Historical DocumentsCode0
Adder Attention for Vision TransformerCode0
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