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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 7801–7825 of 9051 papers

TitleStatusHype
Neural Network Verification for the Masses (of AI graduates)—0
T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story CompletionCode0
An Automated Ensemble Learning Framework Using Genetic Programming for Image ClassificationCode0
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity—0
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction—0
Generating Responses with a Specific Emotion in Dialog—0
Learning to Abstract for Memory-augmented Conversational Response GenerationCode0
Boosting Dialog Response Generation—0
Generating Diverse Translations with Sentence Codes—0
Bandit Learning for Diversified Interactive Recommendation—0
One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems—0
SetGAN: Improving the stability and diversity of generative models through a permutation invariant architecture—0
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation—0
Conformity bias in the cultural transmission of music sampling traditions—0
Reducing Popularity Bias in Recommendation Over Time—0
Re-ranking Based Diversification: A Unifying View—0
SampleFix: Learning to Generate Functionally Diverse Fixes—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems—0
Harnessing behavioral diversity to understand circuits for cognition—0
DAL: Dual Adversarial Learning for Dialogue Generation—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI ModelsCode0
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
Reinforcement Learning with Convex ConstraintsCode1
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