SOTAVerified

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 7901–7925 of 9051 papers

TitleStatusHype
Recent Advances in Diversified Recommendation—0
A Simple Dual-decoder Model for Generating Response with Sentiment—0
A deep-learning-based approach for fast and robust steel surface defects classification—0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
Atom Responding Machine for Dialog Generation—0
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds—0
A novel statistical metric learning for hyperspectral image classification—0
Deep Sky Modeling for Single Image Outdoor Lighting Estimation—0
SISUA: Semi-Supervised Generative Autoencoder for Single Cell DataCode0
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks—0
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving—0
DRIT++: Diverse Image-to-Image Translation via Disentangled RepresentationsCode1
Argument Identification in Public Comments from eRulemaking—0
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling—0
The Lexical Gap: An Improved Measure of Automated Image Description Quality—0
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm—0
Learning Diverse Generations using Determinantal Point Processes—0
Diversity and Depth in Per-Example Routing Models—0
RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY—0
Consistency-based anomaly detection with adaptive multiple-hypotheses predictionsCode0
MixFeat: Mix Feature in Latent Space Learns Discriminative Space—0
Modulating transfer between tasks in gradient-based meta-learning—0
Diverse Machine Translation with a Single Multinomial Latent Variable—0
RelGAN: Relational Generative Adversarial Networks for Text GenerationCode0
Model Compression with Generative Adversarial Networks—0
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