Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks

Deep generative models provide a powerful set of tools to understand real-world data. But as these models improve, they increase in size and complexity, so their computational cost in memory and execution time grows. Using binary weights in neural networks is one method which has shown promise in reducing this cost. However, whether binary neural networks can be used in generative models is an open problem. In this work we show, for the first time, that we can successfully train generative models which utilize binary neural networks. This reduces the computational cost of the models massively. We develop a new class of binary weight normalization, and provide insights for architecture designs of these binarized generative models. We demonstrate that two state-of-the-art deep generative models, the ResNet VAE and Flow++ models, can be binarized effectively using these techniques. We train binary models that achieve loss values close to those of the regular models but are 90%-94% smaller in size, and also allow significant speed-ups in execution time

Explaining Latent Factor Models for Recommendation with Influence Functions

Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation. Despite the high recommendation accuracy of LFMs, a critical issue to be resolved is the lack of explainability. Extensive efforts have been made in the literature to incorporate explainability into LFMs. However, they either rely on auxiliary information which may not be available in practice, or fail to provide easy-to-understand explanations. In this paper, we propose a fast influence analysis method named FIA, which successfully enforces explicit neighbor-style explanations to LFMs with the technique of influence functions stemmed from robust statistics. We first describe how to employ influence functions to LFMs to deliver neighbor-style explanations. Then we develop a novel influence computation algorithm for matrix factorization with high efficiency. We further extend it to the more general neural collaborative filtering and introduce an approximation algorithm to accelerate influence analysis over neural network models. Experimental results on real datasets demonstrate the correctness, efficiency and usefulness of our proposed method

GPCSIM : an instrument simulator of polymer analysis by size exclusion chromatography for demonstration and training purposes

A computer simulation has been developed with the purpose of demonstrating and visualizing a multitude of effects in the molecular characterization of synthetic polymer mixtures by size exclusion (gel permeation) chromatography. The chromatographic results and their interpretation are influenced by numerous parameters originating from sample, column and instrumentation used (injection, detection etc). The target audience for the software tool consists of polymers scientists, teachers of separation science and students. Especially for the latter audience it is important to stress that the software enables intentional creation of mistakes and learning from these mistakes. What the user can do ranges from visualization (quantitatively) all retention and dispersion effects, validation of experimental setup, checking sensitivity for certain operating conditions, extrapolation current instrument specifications, and in general performing hypothetical experiments. Several examples, such as column choice, band broadening, detection comparison and possible artifacts in the calculation of distributions are presented. This simulator is part of a family of similar tools for gas chromatography, high performance liquid chromatography, micellar electrokinetic chromatography and capillary electrophoresis. They have proved their effectiveness in education of separation science topics at several European universities

Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

SchNet - a deep learning architecture for molecules and materials

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics

Welvaartsfuncties in de landbouw : ontwikkeling en toepassing

An individual's well-being (also referred to as welfare or utility) is intimately linked to the extent to which the desires and wishes of that individual are fulfilled, and pain and distress avoided. These desires can relate to various aspects of daily life. Some pertain to economics, others are more sociological in nature. The economic aspect that has received the most attention is income. In this respect income has a very broad meaning: command over commodities. It covers elements such as income distribution, wealth and its distribution, poverty, etc. Closely related to income and income distribution is the division of time between labour and leisure (in some instances also time spent in the household). To a large extent the distribution of time between labour (paid time) and leisure (unpaid time) determines the amount of income an individual can earn, In addition, the choice of a particular job (at least if one has the option of choosing) also has implications regarding the amount of income. Some aspects of life usually considered as belonging to the field of sociology such as health, education, safety, social relations and feelings of fear, have nonetheless received an increasing interest of economists (Becker, 1965; Rosen, 1986).This research concerns income and labour in agriculture. Literature on agricultural economics devides income research into three general categories:-income formation and income distribution in agriculture-comparison of incomes earned inside and outside agriculture - research on the incidence of poverty in rural areas.Research on labour is mainly concerned with:-the quality of jobs: working conditions, education, on-the job-training, etc.-the amount of work done: for example, how many hours a day does a farmer work.In this study a special kind of utility function (Van Praag, 1968) is used to describe farm families' preferences with respect to income and labour. This socalled individual welfare function is a cardinally measurable utility function. This function was originally derived as a lognormal distribution function. Up to now most applications of this special utility function have been aimed at deriving the welfare function of income.The univariate lognormal distribution function is determined by two parameters, μand σ. In order to measure the welfare function of income, that is, to estimate the parameters μand σthe 'Income Evaluation Question' was developed. Under the assumption that utility can be measured directly, respondents to surveys have to specify the amounts of income they associate with verbal qualifications such as 'very bad', 'bad', etc. The parameters of the welfare function can be estimated using those evaluations, making some special assumption about the way those surveyed provide these evaluations (the so-called 'equal interval' assumption). Differences in estimated parameter values can be explained by the differences in economic and demographic background among the people being surveyed.The welfare function of labour is measured in a way that is analogous to the welfare function of income. In the labour evaluation, respondents express their feelings about two different, yet related aspects of labour. First, working on the farm provides 'psychic income' for the workers involved. Therefore, to a certain extent, labour will be evaluated positively. Second, the provision of labour reduces the amount of leisure time available. This aspect of labour provision is evaluated negatively. According to the theory of the welfare function, the evaluation of both aspects of labour can be approximated by a bivariate lognormal distribution function (Van Praag, 1968; Van de Stadt, 1983). The four parameters of this specification are measured similarly to the welfare function of income. Survey respondents are asked to answer the 'Labour Evaluation Question', which indicates a range of hypothetical working hours. The respondent is supposed to give a (numerical) evaluation for the various working hours relevant to him or her. As the bivariate lognormal distribution function is rather demanding as far as the number of parameters to be estimated is concerned, additional specifications such as a gamma and a beta specification were also estimated. Just as with the welfare function of income, differences in parameters can partly be attributed to differences in the respondents' economic and demographic circumstances.Both the welfare function of income and that of labour should be considered partial welfare functions. When evaluating working hours, income is assumed to be constant, whereas the amount of working hours is constant when evaluating different amounts of income. According to the theory of the welfare function, the simultaneous evaluation of income and labour should be specified as the product of the partial welfare functions (at least whenever the aspects evaluated, in this case income and labour, are independent). It will probably be very difficult for a respondent to evaluate an amount of income that is very high (or very low), compared with that respondent's usual income, without (even if implicitly) changing the amount of labour involved. The same is true when evaluating labour. Respondents might relate different amounts of working time to changes in income. This means that any simultaneous evaluation of income and labour based on such partial evaluations can only be valid when conducted within the scope of respondents' own actual labourlincome combinations. In addition to the (theoretically correct) specification as a product of partial welfare functions, the simultaneous evaluation of income and labour has also been specified as the (convex) weighted sum of the partial evaluations.The parameters of the welfare function are estimated using survey results. The answers to the specific evaluation questions (the 'Income Evaluation Question' and the 'Labour Evaluation Question') are particularly relevant in the estimation procedure. For the purpose of estimating the welfare function of farm families in the Netherlands, a small sample of farmers (arable, dairy, intensivelivestock, and others) was chosen from among the Dutch farm population. In addition to answering the evaluation questions, respondents were also requested to provide information about the technical characteristics of their farms and the demographic characteristics of their farm families. Contrary to most previous applications, the evaluation questions were put to both the farmers and their wifes (if present). Although the sample used is certainly not a perfect reflection of the Dutch farm population, the farms selected seem to have provided a reasonable picture of farm type subgroups within the population.As far as the preferences regarding income are concerned, the results broadly correspond to those for the Dutch population. (For results concerning the population at large, see, e.g. Kapteyn, 1977). It generally takes a higher income to satisfy a farmer than it does to satisfy his spouse. This fact has been explained in Kapteyn et al., 1986.Past incomes do affect perceived satisfaction with certain amounts of income in the present. This is the so-called 'preference drift'. Because of the highly fluctuating incomes of farmers, this effect is less pronounced for the farm population than for the average Dutch citizen. Using some kind of permanent income measure that fluctuates less than actual farm incomes, substantially reduces the differences between farmers and nonfarmers.The number of persons in a family can be considered an indication of the costs involved in feeding and clothing the family. Although family size does have a comparable effect on the farmer's income preferences, as in studies covering the Dutch population, family size has a much greater effect on the preferences of the farmer's wife. This is due to the fact that previous research only addressed the income evaluation question to the head of the family, in most cases a man.The welfare function of income can be used to calculate parity incomes for different groups of farmers. Group A earns a parity income compared with group B when both groups are equally satisfied with their incomes. Based upon the (small) sample, intensive-livestock farmers seem to be the most satisfied with their incomes, whereas arable farmers are the least satisfied. In general, farmers seem to be less satisfied with their incomes than the average Dutch citizen. It should be kept in mind however, that this observation is related to the period of the survey. In different circumstances the comparison result could very well be reversed.The evaluation of labour is not monotonically decreasing in time, as is almost always assumed in neoclassical economics. If only a small number of hours is worked on the farm, the perceived sense of well-being increases. If the amount of labour exceeds a certain number of hours, perceived welfare decreases. The increase in perceived welfare is the result of 'psychic income' derived by farmers working their farms. The welfare perception eventually starts decreasing as the amount of leisure time is reduced due to increasing labour time. Various specifications of the welfare function of labour have been estimated. The lognormal specification fit the data best.As with the parameters of the welfare function of income, habit formation determines to some degree the estimated values of the parameters of the welfare function of labour.The combination of the welfare function of income and the welfare function of labour results in the welfare function of income and labour. If both partial evaluations are independent, then the combined welfare function is the product of the partial welfare functions. The cardinality of the welfare function makes it possible to determine the unique welfare levels of the indifference curves.The shape of the indifference curves is different than in the neoclassical case because of the 'psychic income' derived from working, whether it is on the farm or elsewhere.The amount of income needed to persuade a farmer to work an extra hour (the so- called marginal rate of transformation) can be calculated from the estimated welfare function of income and labour. The interpretation of the calculated values is rather difficult. Compared with the values in other research (based on neoclassical assumptions), the calculated rates of transformation based on the welfare- function are very high.Given that farmers would have the same preferences regarding on-farm and off-farm labour, calculations based on neoclassical assumptions show that the on-farm amount of labour for individual farmers is not compatible with utilitymaximizing behaviour. Because farmers are working too many hours, their wage rate, measured by the value of the marginal product, is low. Nonfarming wage rates in 'comparable' jobs are higher. Here farmers could earn a higher income with presumably less labour. One reason why farmers do not leave agriculture is that in the above comparison an incorrect value for the opportunity cost of farm labour is used in calculating off-farm income opportunities. Nonagricultural wage rates for current farmers would be even lower than their agricultural wage rates. There is also the chance that the right kind of jobs (part-time, close to the farm) are not available. Another explanation is that the income measure is too restricted. In addition to providing money, farming also supplies other kinds of income (e.g. housing, home- grown goods, etc.). From this point of view, farmers' incomes would not be (relatively) low as long as all components were adequately assessed. Another explanation takes preferences towards labour into account. It could be that farmers have such preferences (with respect to farm labour), that they are satisfied with a relatively low reward.The results based on the welfare function of income do not confirm explanations concerning income, as such. Farmers are less satisfied with their money income than workers outside agriculture. Obviously non-money income does not fully compensate for the low money reward. When preferences regarding labour and income are simultaneously taken into account, in order to justify an additional hour of work, the wage rate should be substantially higher than the marginal wage rate calculated in previous (neoclassical) research. So, even when preferences towards farm labour are taken into account, it is still impossible to explain the low wage rates in agriculture.The high values for farmers' transformation rates, based on their preferences regarding income and labour, do not fit with the incidence of low agricultural wage rates. One explanation could be that the welfare function should contain other arguments besides income and labour. Or perhaps attention should be paid to nonagricultural circumstances in order to discover why farmers are so reluctant to leave agriculture. Despite the fact that circumstances in agriculture do not concur with farmers' preferences, farmers seem to be trapped in agriculture without any opportunities for leaving and taking a job elsewhere. This inquiry into the preferences of farm families cannot, by itself, provide the ultimate answer to the question of why farmers remain farmers.</p

Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure