Statistical Learning
PartiallyView-alignedClustering
To solve this practical and challenging problem, we propose a novel multi-view clustering method termed partially view-aligned clustering (PVC).
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models George Stein Jesse C. Cresswell Rasa Hosseinzadeh Yi Sui Brendan Leigh Ross
We address these flaws through a study of alternative self-supervised feature extractors, find that the semantic information encoded by individual networks strongly depends on their training procedure, and show that DINOv2-ViT -L/14 allows for much richer evaluation of generative models.
1e04b969bf040acd252e1faafb51f829-Paper.pdf
Updating onlythese task-specific modules thenallowsthe model to be adapted to low-data tasks for as many steps as necessary without risking overfitting. Unfortunately, existing meta-learning methods either do not scale to long adaptation or else rely on handcrafted task-specific architectures. Here, we propose ameta-learning approach that obviates the need for this often sub-optimal hand-selection.
End-to-endLearnableClustering forIntentLearninginRecommendation
Intent learning, which aims to learn users' intents for user understanding and itemrecommendation, hasbecome ahotresearch spotinrecent years.
