Search Results for author:
Found 15 papers, 8 papers with code
Robust NAS under adversarial training: benchmark, theory, and beyond
Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data.
Unsupervised Open-Vocabulary Object Localization in Videos
In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization.
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.
Bridging the Gap to Real-World Object-Centric Learning
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world.
Targeted Separation and Convergence with Kernel Discrepancies
Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown central to a wide range of applications, including hypothesis testing, sampler selection, distribution approximation, and variational inference.
Assaying Out-Of-Distribution Generalization in Transfer Learning
Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e. g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations.
Adversarial Robustness
Out-of-Distribution Generalization
+1
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers
Most current classifiers are vulnerable to adversarial examples, small input perturbations that change the classification output.
Metrizing Weak Convergence with Maximum Mean Discrepancies
More precisely, we prove that, on a locally compact, non-compact, Hausdorff space, the MMD of a bounded continuous Borel measurable kernel k, whose reproducing kernel Hilbert space (RKHS) functions vanish at infinity, metrizes the weak convergence of probability measures if and only if k is continuous and integrally strictly positive definite (i. s. p. d.)
Adversarial Vulnerability of Neural Networks Increases with Input Dimension
Over the past four years, neural networks have been proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions.
First-order Adversarial Vulnerability of Neural Networks and Input Dimension
Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions.
From optimal transport to generative modeling: the VEGAN cookbook
We study unsupervised generative modeling in terms of the optimal transport (OT) problem between true (but unknown) data distribution $P_X$ and the latent variable model distribution $P_G$.
AdaGAN: Boosting Generative Models
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images.
Consistent Kernel Mean Estimation for Functions of Random Variables
We provide a theoretical foundation for non-parametric estimation of functions of random variables using kernel mean embeddings.
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions
The RKHS distance of two mapped measures is a semi-metric $d_k$ over $M$.
Removing systematic errors for exoplanet search via latent causes
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest.
