arXiv:2603. 29488v2 Announce Type: replace Abstract: Cosine similarity is often used to measure the similarity of vector representations of neural network models.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2509. 12760v5 Announce Type: replace Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax activation function, adding Similarity (i.
arXiv:2511. 14117v2 Announce Type: replace Abstract: Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotators into a majority vote.
arXiv:2607. 09405v1 Announce Type: new Abstract: Similarity search is a primary application of embedding models trained by contrastive learning.
arXiv:2504. 16318v3 Announce Type: replace Abstract: Cosine similarity is a standard comparison rule for learned representations in information retrieval, natural language processing, computer vision, and multimodal learning.
arXiv:2604. 11613v4 Announce Type: replace-cross Abstract: Transformers can perform in-context classification from a few labeled examples, yet the inference-time algorithm remains opaque.
arXiv:2608. 08489v1 Announce Type: new Abstract: Neural network classifiers trained by cross-entropy minimization are highly sensitive to label noise and adversarial contamination.