Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks

Architecture optimization, which is a technique for finding an efficient neural network that meets certain requirements, generally reduces to a set of multiple-choice selection problems among alternative sub-structures or parameters. The discrete nature of the selection problem, however, makes this optimization difficult. To tackle this problem we introduce a novel concept of a trainable gate function. The trainable gate function, which confers a differentiable property to discretevalued variables, allows us to directly optimize loss functions that include non-differentiable discrete values such as 0-1 selection. The proposed trainable gate can be applied to pruning. Pruning can be carried out simply by appending the proposed trainable gate functions to each intermediate output tensor followed by fine-tuning the overall model, using any gradient-based training methods. So the proposed method can jointly optimize the selection of the pruned channels while fine-tuning the weights of the pruned model at the same time. Our experimental results demonstrate that the proposed method efficiently optimizes arbitrary neural networks in various tasks such as image classification, style transfer, optical flow estimation, and neural machine translation.Comment: Accepted to AAAI 2020 (Poster

Can a reduction in credit card processing fees offset the effect of a hike in the minimum wage?

The objective of this study is to assess whether a reduction in credit card processing fees can offset the effect of a hike in the minimum wage by examining the unique case of South Korea. To do so, this study introduces a theoretical model with money and credit as the explicit means of payment. In particular, it develops a general equilibrium model with micro-foundations for dealing with the relationship between minimum wage increases and job automation, and takes a long-run approach in the quantitative analysis. Contrary to the existing literature, the study shows that a minimum wage hike negatively and significantly affects overall employment. The calibrated results show that a 13.6% hike in the minimum wage causes a 16.46% reduction in the demand for simple labor earning the minimum wage, and also decreases the demand for non-simple labor by 0.157%. In contrast, if a policy of reducing credit card processing fees is adopted to ease the negative effect of a hike in minimum wage on employment, a 0.65% reduction in these fees (derived by shifting the burden of interest on credit card debt from seller to buyer) results in a 0.09% decrease in the labor demand

Graduate Recital, Flute

Andre Jolivet is an iconic figure in contemporary French music. Many of his composition have a unique configuration of high character, maturity, combined with a sense of mysticism, religious influence, and the concept of returning to its origin give the foundation for Jolivet\u27s music. Jolivet\u27s composition, Chant De Linos, was composed during his mid-life stage (2nd stage) for the Paris Conservatory as a competition piece for the Morceau de Concours. Chant de Linos is a literal translation Linos\u27 Song and depicts a chant during an ancient Greek funeral. Jolivet combined the Greek funeral chant with his unique compositional style. Mourning , Cries , and Dance are the reoccurring themes throughout the piece. Primitive and exotic element of the flute and piano can be expressed through modern compositional style, stable technique, and rhythmic repetition, which all come together to create the magical effect of Andre Jolivet\u27s Chant de Linos. Throughout this analysis, an in-depth analysis will be done about Jolivet\u27s past, the importance and development of 20th century French contemporary music and it\u27s the use of woodwind instruments, and finally the compositional techniques used by Andre Jolivet in composing the Chant de Linos

Can a reduction in credit card processing fees offset the effect of a hike in the minimum wage?

The objective of this study is to assess whether a reduction in credit card processing fees can offset the effect of a hike in the minimum wage by examining the unique case of South Korea. To do so, this study introduces a theoretical model with money and credit as the explicit means of payment. In particular, it develops a general equilibrium model with micro-foundations for dealing with the relationship between minimum wage increases and job automation, and takes a long-run approach in the quantitative analysis. Contrary to the existing literature, the study shows that a minimum wage hike negatively and significantly affects overall employment. The calibrated results show that a 13.6% hike in the minimum wage causes a 16.46% reduction in the demand for simple labor earning the minimum wage, and also decreases the demand for non-simple labor by 0.157%. In contrast, if a policy of reducing credit card processing fees is adopted to ease the negative effect of a hike in minimum wage on employment, a 0.65% reduction in these fees (derived by shifting the burden of interest on credit card debt from seller to buyer) results in a 0.09% decrease in the labor demand

The Role of Socially-Mediated Alignment in the Development of Second Language Grammar and Vocabulary: Comparing Face-to-Face and Synchronous Mobile-Mediated Communication

Decades of research has shown that speakers mutually adapt to each other’s linguistic behaviors at different levels of language during dialogue. Recent second language (L2) research has suggested that alignment occurring while L2 learners carry out collaborative activities may lead to L2 development, highlighting the benefits of using alignment activities for L2 learning. However, despite the notion that speakers linguistically align in interactions happening in socially-situated contexts, little is known about the role of social factors in the magnitude and learning outcomes of alignment occurring in L2 interaction. The purpose of the study was to examine the pedagogical benefits of alignment activities for the development of L2 grammar and vocabulary during peer interaction across two different interactional contexts: Face-to-Face (FTF) and synchronous mobile-mediated communication (SMMC; mobile text-chat). The target vocabulary items included 32 words and the target structure was a stranded preposition construction embedded in an English relative clause. Furthermore, this study investigated whether social factors (i.e., L2 learners’ perceptions of their interlocutor’s proficiency, comprehensibility of the interlocutor’s language production, and task experience with the interlocutor) and cognitive factors (i.e., individual differences in language aptitude, cognitive style, and proficiency) would modulate alignment effects. Ninety-eight Korean university students were assigned to either the FTF or SMMC group. They completed two alignment activities in pairs, three measurement tests (pre-, post-, and delayed post-test), various cognitive ability tests, and perception questionnaires over four weeks. Results indicated that alignment occurred at the structural and lexical levels in FTF and SMMC modes, but also that structural alignment was facilitated significantly more in the SMMC mode when compared to FTF. However, there was no significant modality effect on the degree of lexical alignment. Findings also demonstrated beneficial role of alignment activities in L2 grammar and vocabulary learning, irrespective of the modality. Furthermore, results suggested that language proficiency and explicit language aptitude were significantly associated with structural alignment driven learning. Learners’ perceptions did not show a significant impact on the degree of alignment and learning outcomes. Implications for the benefits of interactive alignment activities for L2 development and the effect of modality, social factors, and cognitive factors are discussed

Comparing Sample-wise Learnability Across Deep Neural Network Models

Estimating the relative importance of each sample in a training set has important practical and theoretical value, such as in importance sampling or curriculum learning. This kind of focus on individual samples invokes the concept of sample-wise learnability: How easy is it to correctly learn each sample (cf. PAC learnability)? In this paper, we approach the sample-wise learnability problem within a deep learning context. We propose a measure of the learnability of a sample with a given deep neural network (DNN) model. The basic idea is to train the given model on the training set, and for each sample, aggregate the hits and misses over the entire training epochs. Our experiments show that the sample-wise learnability measure collected this way is highly linearly correlated across different DNN models (ResNet-20, VGG-16, and MobileNet), suggesting that such a measure can provide deep general insights on the data's properties. We expect our method to help develop better curricula for training, and help us better understand the data itself.Comment: Accepted to AAAI 2019 Student Abstrac