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Found 12 papers, 6 papers with code
Craft: Cross-modal Aligned Features Improve Robustness of Prompt Tuning
The experiment on four different prompt tuning structures consistently shows the improvement of our method, with increases of up to $6. 1\%$ in the Base-to-Novel generalization task, $5. 8\%$ in the group robustness task, and $2. 7\%$ in the out-of-distribution tasks.
ParallelEdits: Efficient Multi-Aspect Text-Driven Image Editing with Attention Grouping
This dataset is a benchmark for evaluating text-driven image editing methods in multifaceted scenarios.
Pooling Image Datasets With Multiple Covariate Shift and Imbalance
Further, we discuss how this style of formulation offers a unified perspective on at least 5+ distinct problem settings, from self-supervised learning to matching problems in 3D reconstruction.
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets
Pooling multiple neuroimaging datasets across institutions often enables improvements in statistical power when evaluating associations (e. g., between risk factors and disease outcomes) that may otherwise be too weak to detect.
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
This is directly related to the structure of the computation graph, which can grow linearly as a function of the number of MC samples needed.
Towards Group Robustness in the presence of Partial Group Labels
Such a requirement is impractical in situations where the data labeling efforts for minority or rare groups are significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
Invariant Learning with Partial Group Labels
Such a requirement is impractical in situations where the data labelling efforts for minority or rare groups is significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
Learning Invariant Representations using Inverse Contrastive Loss
Learning invariant representations is a critical first step in a number of machine learning tasks.
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret
We provide a detailed technical analysis and present experiments demonstrating that various fairness measures from the literature can be reliably imposed on a number of training tasks in vision in a manner that is interpretable.
Active Learning with Importance Sampling
We consider an active learning setting where the algorithm has access to a large pool of unlabeled data and a small pool of labeled data.
Accelerating Column Generation via Flexible Dual Optimal Inequalities with Application to Entity Resolution
We tackle optimization of weighted set packing by relaxing integrality in our ILP formulation.
Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations
Rectified Linear Units (ReLUs) are among the most widely used activation function in a broad variety of tasks in vision.
