arXiv:2606. 06458v1 Announce Type: new Abstract: Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2607. 14703v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology.
arXiv:2607. 02718v1 Announce Type: cross Abstract: Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes.
arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).
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:2605. 24417v2 Announce Type: replace Abstract: Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings.
arXiv:2510. 06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data.