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Found 16 papers, 12 papers with code
Multi-Resolution Continuous Normalizing Flows
In this work we introduce a Multi-Resolution variant of such models (MRCNF), by characterizing the conditional distribution over the additional information required to generate a fine image that is consistent with the coarse image.
Ranked #6 on
Image Generation
on ImageNet 64x64
(Bits per dim metric)
Improving Continuous Normalizing Flows using a Multi-Resolution Framework
Recent work has shown that Continuous Normalizing Flows (CNFs) can serve as generative models of images with exact likelihood calculation and invertible generation/density estimation.
Adversarial Boot Camp: label free certified robustness in one epoch
Machine learning models are vulnerable to adversarial attacks.
Climate & BCG: Effects on COVID-19 Death Growth Rates
Multiple studies have suggested the spread of COVID-19 is affected by factors such as climate, BCG vaccinations, pollution and blood type.
Deterministic Gaussian Averaged Neural Networks
We present a deterministic method to compute the Gaussian average of neural networks used in regression and classification.
Learning normalizing flows from Entropy-Kantorovich potentials
We approach the problem of learning continuous normalizing flows from a dual perspective motivated by entropy-regularized optimal transport, in which continuous normalizing flows are cast as gradients of scalar potential functions.
Deep Learning improves identification of Radio Frequency Interference
Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.
Instrumentation and Methods for Astrophysics
How to train your neural ODE: the world of Jacobian and kinetic regularization
Training neural ODEs on large datasets has not been tractable due to the necessity of allowing the adaptive numerical ODE solver to refine its step size to very small values.
Ranked #1 on
Density Estimation
on CelebA-HQ 256x256
Farkas layers: don't shift the data, fix the geometry
Successfully training deep neural networks often requires either batch normalization, appropriate weight initialization, both of which come with their own challenges.
Empirical confidence estimates for classification by deep neural networks
It is well-known that the softmax values of the network are not estimates of the probabilities of class labels.
A principled approach for generating adversarial images under non-smooth dissimilarity metrics
This includes, but is not limited to, $\ell_1, \ell_2$, and $\ell_\infty$ perturbations; the $\ell_0$ counting "norm" (i. e. true sparseness); and the total variation seminorm, which is a (non-$\ell_p$) convolutional dissimilarity measuring local pixel changes.
Scaleable input gradient regularization for adversarial robustness
In this work we revisit gradient regularization for adversarial robustness with some new ingredients.
The LogBarrier adversarial attack: making effective use of decision boundary information
Adversarial attacks formally correspond to an optimization problem: find a minimum norm image perturbation, constrained to cause misclassification.
Calibrated Top-1 Uncertainty estimates for classification by score based models
While the accuracy of modern deep learning models has significantly improved in recent years, the ability of these models to generate uncertainty estimates has not progressed to the same degree.
Improved robustness to adversarial examples using Lipschitz regularization of the loss
We augment adversarial training (AT) with worst case adversarial training (WCAT) which improves adversarial robustness by 11% over the current state-of-the-art result in the $\ell_2$ norm on CIFAR-10.
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust
In this work we study input gradient regularization of deep neural networks, and demonstrate that such regularization leads to generalization proofs and improved adversarial robustness.
