arXiv research

Researchers create a flickering attack to fool video recognition networks.

Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines. We propose a novel deep-learning method to learn stable and temporally coherent feature spaces for points clouds that change over time. We identify a set of inherent problem…

DPFRL uses particle filters for decision making with complex visual observations.

Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architec…

Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose d…

The principal aim of this work is the evidence on empirical way that catastrophic bifurcation breakdowns or transitions, proceeded by flickering phenomenon, are present on notoriously significant and unpredictable financial markets. Overall, in this work we developed various metrics associated with catastrophic bifurca…

Humans and animals show remarkable flexibility in adjusting their behaviour when their goals, or rewards in the environment change. While such flexibility is a hallmark of intelligent behaviour, these multi-task scenarios remain an important challenge for machine learning algorithms and neurobiological models alike. We…

Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods f…

This paper presents a new approach, called perturb-max, for high-dimensional statistical inference that is based on applying random perturbations followed by optimization. This framework injects randomness to maximum a-posteriori (MAP) predictors by randomly perturbing the potential function for the input. A classic re…

The paper tackles extrapolation of gene knockouts effects on RNA counts.

Novel geometry-informed irreversible perturbation accelerates Langevin dynamics convergence.

Study linear perturbations in Schwarzschild black hole spacetime.

New research evaluates various perturbation methods for improving neural network robustness.

Convolutional neural networks (CNN) have become one of the most popular machine learning tools and are being applied in various tasks, however, CNN models are vulnerable to universal perturbations, which are usually human-imperceptible but can cause natural images to be misclassified with high probability. One of the s…

We study the transfer of adversarial robustness of deep neural networks between different perturbation types. While most work on adversarial examples has focused on $L_\infty$ and $L_2$-bounded perturbations, these do not capture all types of perturbations available to an adversary. The present work evaluates 32 attack…

EVILL uses randomised perturbations to improve exploration in bandit problems.

New bifurcation found in perturbations of non-generic closed self-shrinkers.

Adversarial perturbations fool deepfake detectors with high accuracy.

Advances FTPL results for bandit problems with unbounded perturbations.

Generative Intervention Models predict perturbation effects without knowing the underlying mechanisms.

Identifies all perturbative vacua in bosonic string theory.

Develops new methods to create imperceptible image changes that fool classifiers.

Charge measurements for instantons and gravitational perturbations.

Eigenvalues of Steklov eigenproblems change predictably with boundary tweaks.

Study linear perturbations of Spin(7) metrics, finding only rank one nilpotent matrices.

Simple perturbation of Vafa-Witten equations leads to transversality.

We developed a perturbation model for affine gravity theories.

Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial training improves the robustness of image classifiers against such adversarial perturbations, it leaves them sensitive to perturbations on a non-ne…

DBPA assesses LLM perturbations using frequentist hypothesis testing.

The paper learns perturbation sets from data to improve robustness in machine learning.

Adversarial training, in which a network is trained on both adversarial and clean examples, is one of the most trusted defense methods against adversarial attacks. However, there are three major practical difficulties in implementing and deploying this method - expensive in terms of extra memory and computation costs; …

A fast method computes class-specific adversarial perturbations for deep networks.

Study metric perturbations to make degenerate harmonic forms non-degenerate.

In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection. We employ adversarial perturbations for obfuscating certain variables in raw data while preserving the rest. Current adversarial perturbation methods are used for dat…

Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural text to be misclassified with high probability. Unlike images on which a single fixed-size adversarial perturbation can be found, text is of …

The paper proves new theorems about specific types of operator perturbations.

The study reveals how adversarial perturbations can include class features for generalization.

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we propose the first quantitative analysis of the robustness of classifiers to universal perturba…

Improved online Lasso reduces regret in sparse linear contextual bandits.

New method $μP^2$ improves neural network training by scaling perturbations layerwise.

Paper proposes a method to generate adversarial perturbations for black-box attacks without accessing inner states.

Deep neural networks are susceptible to adversarial manipulations in the input domain. The extent of vulnerability has been explored intensively in cases of $\ell_p$-bounded and $\ell_p$-minimal adversarial perturbations. However, the vulnerability of DNNs to adversarial perturbations with specific statistical properti…

New metric scores perturbations across populations, not cells, improving model comparison.

In this paper, we propose a perturbation framework to measure the robustness of graph properties. Although there are already perturbation methods proposed to tackle this problem, they are limited by the fact that the strength of the perturbation cannot be well controlled. We firstly provide a perturbation framework on …

This work improves explanation quality for time series predictions by learning perturbations.

The paper studies perturbations of de Rham Hodge operators on manifolds with or without boundaries.

This paper establishes transversality for perturbed Vafa-Witten moduli spaces on 4-manifolds.

New adversarial training methods generate multiplicative perturbations for robust DNN training.