arXiv:2606. 16379v1 Announce Type: new Abstract: Evaluating representation similarity is fundamental to representation learning.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2608. 01039v1 Announce Type: cross Abstract: Trajectory similarity learning is fundamental to efficient trajectory retrieval under complex distance measures.
arXiv:2602. 06205v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces.
arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.
arXiv:2607. 05464v1 Announce Type: cross Abstract: The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects.
arXiv:2607. 22766v1 Announce Type: cross Abstract: The alignment of Large Language Models (LLMs) is increasingly bottlenecked by data quality.
arXiv:2605. 24782v2 Announce Type: replace Abstract: While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invariants, making even perception-based out-of-distribution accuracy a poor proxy for scientific utility.