Multi-step domain adaptation by adversarial attack to HΔH\mathcal{H} \Delta \mathcal{H}-divergence

Adversarial examples are transferable between different models. In our paper, we propose to use this property for multi-step domain adaptation. In unsupervised domain adaptation settings, we demonstrate that replacing the source domain with adversarial examples to $\mathcal{H} \Delta \mathcal{H}$-divergence can improve source classifier accuracy on the target domain. Our method can be connected to most domain adaptation techniques. We conducted a range of experiments and achieved improvement in accuracy on Digits and Office-Home datasets

Easy Batch Normalization

It was shown that adversarial examples improve object recognition. But what about their opposite side, easy examples? Easy examples are samples that the machine learning model classifies correctly with high confidence. In our paper, we are making the first step toward exploring the potential benefits of using easy examples in the training procedure of neural networks. We propose to use an auxiliary batch normalization for easy examples for the standard and robust accuracy improvement

Toward Never Ending Language Learning for Morphologically Rich Languages

Peer reviewe

Search for the Radiative Capture d+d->^4He+\gamma Reaction from the dd\mu Muonic Molecule State

A search for the muon catalyzed fusion reaction dd --> ^4He +\gamma in the dd\mu muonic molecule was performed using the experimental \mu CF installation TRITON and NaI(Tl) detectors for \gamma-quanta. The high pressure target filled with deuterium at temperatures from 85 K to 800 K was exposed to the negative muon beam of the JINR phasotron to detect \gamma-quanta with energy 23.8 MeV. The first experimental estimation for the yield of the radiative deuteron capture from the dd\mu state J=1 was obtained at the level n_{\gamma}\leq 2\times 10^{-5} per one fusion.Comment: 9 pages, 3 Postscript figures, submitted to Phys. At. Nuc