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Found 6 papers, 2 papers with code
ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity
Deep learning has shown great potential in assisting radiologists in reading chest X-ray (CXR) images, but its need for expensive annotations for improving performance prevents widespread clinical application.
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images.
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity
Deformable DETR uses the multiscale feature to ameliorate performance, however, the number of encoder tokens increases by 20x compared to DETR, and the computation cost of the encoder attention remains a bottleneck.
Online Hyperparameter Meta-Learning with Hypergradient Distillation
Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters.
Large-Scale Meta-Learning with Continual Trajectory Shifting
Meta-learning of shared initialization parameters has shown to be highly effective in solving few-shot learning tasks.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize to heterogeneous tasks and architectures.
