Mini-batch sub-sampling is likely here to stay, due to growing data demands, memory-limited computational resources such as graphical processing units (GPUs), and the dynamics of on-line learning. Sampling a new mini-batch at every loss evaluation brings a number of benefits, but also one significant drawback: The loss function becomes discontinuous. These discontinuities are generally not problematic when using fixed learning rates or learning rate schedules typical of subgradient methods. However, they hinder attempts to directly minimize the loss function by solving for critical points, since function minimizers find spurious minima induced by discontinuities, while critical points may not even exist. Therefore, finding function minimizers and critical points in stochastic optimization is ineffective. As a result, attention has been given to reducing the effect of these discontinuities by means such as gradient averaging or adaptive and dynamic sampling. This paper offers an alternative paradigm: Recasting the optimization problem to rather find Non-Negative Associated Gradient Projection Points (NN-GPPs). In this paper, we demonstrate the NN-GPP interpretation of gradient information is more robust than critical points or minimizers, being less susceptible to sub-sampling induced variance and eliminating spurious function minimizers. We conduct a visual investigation, where we compare function value and gradient information for a variety of popular activation functions as applied to a simple neural network training problem. Based on the improved description offered by NN-GPPs over minimizers to identify true optima, in particular when using smooth activation functions with high curvature characteristics, we postulate that locating NN-GPPs can contribute significantly to automating neural network training.

In this paper we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to make confident decisions because of the uncertainty in predictions arisen from insufficient quality and quantity of training data. Here, we show that Bayesian neural networks are useful to quantify the uncertainty of molecular property prediction with three numerical experiments. In particular, it enables us to decompose the predictive variance into the model- and data-driven uncertainties, which helps to elucidate the source of errors. In the logP predictions, we show that data noise affected the data-driven uncertainties more significantly than the model-driven ones. Based on this analysis, we were able to find unexpected errors in the Harvard Clean Energy Project dataset. Lastly, we show that the confidence of prediction is closely related to the predictive uncertainty by performing on bio-activity and toxicity classification problems.

The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data. We cover topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. We conclude by presenting a discussion of the reviewed literature and outlining the research gaps and remaining challenges for future work.

A classic setting of the stochastic K-armed bandit problem is considered in this note. In this problem it has been known that KL-UCB policy achieves the asymptotically optimal regret bound and KL-UCB policy empirically performs better than the KL-UCB policy although the regret bound for the original form of the KL-UCB policy has been unknown. This note demonstrates that a simple proof of the asymptotic optimality of the KL-UCB policy can be given by the same technique as those used for analyses of other known policies. In the problem of the stochastic bandit problems, it is known that there exists a (problem-dependent) regret lower bound [1][2]. It can be achieved by, for example, the DMED policy [3] for the model of nonparametric distributions over [0, 1]. One of the conference version [6] of [5] also proposed KL-UCB policy, which empirically performs better than KL-UCB but does not have a theoretical guarantee.

The Zone of Avoidance makes it difficult for astronomers to catalogue galaxies at low latitudes to our galactic plane due to high star densities and extinction. However, having a complete sky map of galaxies is important in a number of fields of research in astronomy. There are many unclassified sources of light in the Zone of Avoidance and it is therefore important that there exists an accurate automated system to identify and classify galaxies in this region. This study aims to evaluate the efficiency and accuracy of using an evolutionary algorithm to evolve the topology and configuration of Convolutional Neural Network (CNNs) to automatically identify galaxies in the Zone of Avoidance. A supervised learning method is used with data containing near-infrared images. Input image resolution and number of near-infrared passbands needed by the evolutionary algorithm is also analyzed while the accuracy of the best evolved CNN is compared to other CNN variants.

Tensor is a natural way to represent the high-dimensional data, thus it preserves more intrinsic information than matrix when dealing with high-order data [1, 2, 3]. In practice, parts of the tensor entries are missing during data acquisition and transformation, tensor completion estimates the missing entries based on the assumption that most elements are correlated [4]. This correlation can be modeled as low-rank data structures which can be used in a series of applications, including signal processing [2], machine learning [5], remote sensing [6], computer vision [7], etc. There are two main frameworks for tensor completion, namely, variational energy minimization as well as tensor rank minimization [8, 9], where the energy is usually a recovery error in the context of tensor completion and the definition of rank varies with diverse tensor decompositions. The first method is realized by means of the alternating least square (ALS), in which each core tensor is updated one by one while others are fixed [8]. The ALSbased method requires a predefined tensor rank, while the rank minimization does not. Common forms of tensor decompositions are summarized as follows.

Learning with less supervision is a major challenge in artificial intelligence. One sensible approach to decrease the amount of supervision is to leverage prior experience and transfer knowledge from tasks seen in the past. However, a necessary condition for a successful transfer is the ability to remember how to perform previous tasks. The Continual Learning (CL) setting, whereby an agent learns from a stream of tasks without seeing any example twice, is an ideal framework to investigate how to accrue such knowledge. In this work, we consider supervised learning tasks and methods that leverage a very small episodic memory for continual learning. Through an extensive empirical analysis across four benchmark datasets adapted to CL, we observe that a very simple baseline, which jointly trains on both examples from the current task as well as examples stored in the memory, outperforms state-of-the-art CL approaches with and without episodic memory. Surprisingly, repeated learning over tiny episodic memories does not harm generalization on past tasks, as joint training on data from subsequent tasks acts like a data dependent regularizer. We discuss and evaluate different approaches to write into the memory. Most notably, reservoir sampling works remarkably well across the board, except when the memory size is extremely small. In this case, writing strategies that guarantee an equal representation of all classes work better. Overall, these methods should be considered as a strong baseline candidate when benchmarking new CL approaches

Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy declines. This phenomenon is suggested to be an inherent trade-off. We propose a model that employs feature prioritization by a nonlinear attention module and $L_2$ feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training. The attention module encourages the model to rely heavily on robust features by assigning larger weights to them while suppressing non-robust features. The regularizer encourages the model to extract similar features for the natural and adversarial images, effectively ignoring the added perturbation. In addition to evaluating the robustness of our model, we provide justification for the attention module and propose a novel experimental strategy that quantitatively demonstrates that our model is almost ideally aligned with salient data characteristics. Additional experimental results illustrate the power of our model relative to the state of the art methods.

Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation. We approach this from two directions: First, we provide a general model of augmentation as a Markov process, and show that kernels appear naturally with respect to this model, even when we do not employ kernel classification. Next, we analyze more directly the effect of augmentation on kernel classifiers, showing that data augmentation can be approximated by first-order feature averaging and second-order variance regularization components. These frameworks both serve to illustrate the ways in which data augmentation affects the downstream learning model, and the resulting analyses provide novel connections between prior work in invariant kernels, tangent propagation, and robust optimization. Finally, we provide several proof-of-concept applications showing that our theory can be useful for accelerating machine learning workflows, such as reducing the amount of computation needed to train using augmented data, and predicting the utility of a transformation prior to training.