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Found 8 papers, 6 papers with code
Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation
Contrastive learning is a model pre-training technique by first creating similar views of the original data, and then encouraging the data and its corresponding views to be close in the embedding space.
Conformal Prediction Sets Improve Human Decision Making
In response to everyday queries, humans explicitly signal uncertainty and offer alternative answers when they are unsure.
Equirectangular image construction method for standard CNNs for Semantic Segmentation
In addition, there are few publicly dataset of equirectangular images with labels, which presents a challenge for standard CNNs models to process equirectangular images effectively.
Self-supervised Representation Learning From Random Data Projectors
Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations.
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Comparing to 17 modern metrics for evaluating the overall performance, fidelity, diversity, rarity, and memorization of generative models, we find that the state-of-the-art perceptual realism of diffusion models as judged by humans is not reflected in commonly reported metrics such as FID.
Find Your Friends: Personalized Federated Learning with the Right Collaborators
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model.
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
Explaining the influence of training data on deep neural network predictions is a critical tool for debugging models through data curation.
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction
We additionally introduce a novel Frobenius norm-based contrastive learning objective to improve latent representational generalization. Empirically, we validate MAPSED on two publicly accessible urban crime datasets for spatiotemporal sparse event prediction, where MAPSED outperforms both classical and state-of-the-art deep learning models.
