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Found 6 papers, 1 papers with code
Meta Co-Training: Two Views are Better than One
We show that in the common case when independent views are not available we can construct such views inexpensively using pre-trained models.
Fine-Grained Image Classification
Semi-Supervised Image Classification
Lower Bounds for Adversarially Robust PAC Learning
In this work, we initiate a formal study of probably approximately correct (PAC) learning under evasion attacks, where the adversary's goal is to \emph{misclassify} the adversarially perturbed sample point $\widetilde{x}$, i. e., $h(\widetilde{x})\neq c(\widetilde{x})$, where $c$ is the ground truth concept and $h$ is the learned hypothesis.
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
We study both "inherent" bounds that apply to any problem and any classifier for such a problem as well as bounds that apply to specific problems and specific hypothesis classes.
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure
We show that if the metric probability space of the test instance is concentrated, any classifier with some initial constant error is inherently vulnerable to adversarial perturbations.
Learning under $p$-Tampering Attacks
They obtained $p$-tampering attacks that increase the error probability in the so called targeted poisoning model in which the adversary's goal is to increase the loss of the trained hypothesis over a particular test example.
Commonsense Reasoning and Large Network Analysis: A Computational Study of ConceptNet 4
Part III investigates non-overlapping, as well as overlapping communities that are found in ConceptNet 4.
