Search Results for author:
Found 5 papers, 3 papers with code
Towards Foundation Time Series Model: To Synthesize Or Not To Synthesize?
In this work, we consider the essential question if it is advantageous to train a foundation model on synthetic data or it is better to utilize only a limited number of real-life examples.
Sparse and Transferable Universal Singular Vectors Attack
The research in the field of adversarial attacks and models' vulnerability is one of the fundamental directions in modern machine learning.
General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing
Randomized smoothing is the state-of-the-art approach to construct image classifiers that are provably robust against additive adversarial perturbations of bounded magnitude.
Translate your gibberish: black-box adversarial attack on machine translation systems
Neural networks are deployed widely in natural language processing tasks on the industrial scale, and perhaps the most often they are used as compounds of automatic machine translation systems.
NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer
Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms.
