Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned information when learning new data from a new domain. In this paper, we propose a less-forgetful learning method for the domain expansion scenario. While existing domain adaptation techniques solely focused on adapting to new domains, the proposed technique focuses on working well with both old and new domains without needing to know whether the input is from the old or new domain. First, we present two naive approaches which will be problematic, then we provide a new method using two proposed properties for less-forgetful learning. Finally, we prove the effectiveness of our method through experiments on image classification tasks. All datasets used in the paper, will be released on our website for someone's follow-up study.

Understanding politics is challenging because the politics take the influence from everything. Even we limit ourselves to the political context in the legislative processes; we need a better understanding of latent factors, such as legislators, bills, their ideal points, and their relations. From the modeling perspective, this is difficult 1) because these observations lie in a high dimension that requires learning on low dimensional representations, and 2) because these observations require complex probabilistic modeling with latent variables to reflect the causalities. This paper presents a new model to reflect and understand this political setting, NIPEN, including factors mentioned above in the legislation. We propose two versions of NIPEN: one is a hybrid model of deep learning and probabilistic graphical model, and the other model is a neural tensor model. Our result indicates that NIPEN successfully learns the manifold of the legislative bill's text, and NIPEN utilizes the learned low-dimensional latent variables to increase the prediction performance of legislators' votings. Additionally, by virtue of being a domain-rich probabilistic model, NIPEN shows the hidden strength of the legislators' trust network and their various characteristics on casting votes.

The Approximate Nearest Neighbor (ANN) search problem is important in applications such as information retrieval. Several hashing-based search methods that provide effective solutions to the ANN search problem have been proposed. However, most of these focus on similarity preservation and coding error minimization, and pay little attention to optimizing the precision-recall curve or receiver operating characteristic curve. In this paper, we propose a novel projection-based hashing method that attempts to maximize the precision and recall. We first introduce an uncorrelated component analysis (UCA) by examining the precision and recall, and then propose a UCA-based hashing method. The proposed method is evaluated with a variety of datasets. The results show that UCA-based hashing outperforms state-of-the-art methods, and has computationally efficient training and encoding processes.

Most of the previous work on brain-computer interfaces (BCIs) exploiting the P300 in electroencephalography (EEG) has focused on low-level signal processing algorithms such as feature extraction and classification methods. Although a significant improvement has been made in the past, the accuracy of detecting P300 is limited by the inherently low signal-to-noise ratio in EEGs. In this paper, we present a systematic approach to optimize the interface using partially observable Markov decision processes (POMDPs). Through experiments involving human subjects, we show the P300 speller system that is optimized using the POMDP achieves a significant performance improvement in terms of the communication bandwidth in the interaction.