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Found 10 papers, 8 papers with code
Adversarial Support Alignment
Furthermore, we show that our approach can be viewed as a limit of existing notions of alignment by increasing transportation assignment tolerance.
The Benefits of Pairwise Discriminators for Adversarial Training
We provide sufficient conditions for local convergence; characterize the capacity balance that should guide the discriminator and generator choices; and construct examples of minimally sufficient discriminators.
Towards understanding the true loss surface of deep neural networks using random matrix theory and iterative spectral methods
This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.
Deep Curvature Suite
We present MLRG Deep Curvature suite, a PyTorch-based, open-source package for analysis and visualisation of neural network curvature and loss landscape.
Subspace Inference for Bayesian Deep Learning
Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty.
A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.
Averaging Weights Leads to Wider Optima and Better Generalization
Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence.
Ranked #78 on
Image Classification
on CIFAR-100
(using extra training data)
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
The loss functions of deep neural networks are complex and their geometric properties are not well understood.
Bayesian Incremental Learning for Deep Neural Networks
In industrial machine learning pipelines, data often arrive in parts.
Ultimate tensorization: compressing convolutional and FC layers alike
Convolutional neural networks excel in image recognition tasks, but this comes at the cost of high computational and memory complexity.
