Performance Analysis
Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts
We generate a set of diverse expert models via hypernetworks to cover all possible distribution scenarios, and optimize the model ensemble to adapt to any test distribution. Crucially, in any distribution scenario, we can flexibly output a dedicated model solution that matches the user's preference.
BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays Y ang Zhou 1, T an Li Hui Faith
Vision-language pretraining involves training models on large datasets of images and text to learn the relationships between visual and textual data.
Cell ontology guided transcriptome foundation model
Cells are basic units of all living organisms.
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
Current P AC-Bayes generalisation bounds are restricted to scalar metrics of performance, such as the loss or error rate. However, one ideally wants more information-rich certificates that control the entire distribution of possible outcomes, such as the distribution of the test loss in regression, or the probabilities of different mis-classifications.
Multi-Label Learning with Stronger Consistency Guarantees
Furthermore, this loss function fails to account for label correlations.
