February 5, 2020 Abstract In the past decade, interest in generative models has grown tremendously. However, their training performance can be highly affected by contamination, where outliers are encoded in the representation of the model. In this paper, we introduce a weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs). This formulation is used to fine-tune the latent space of generative RKMs using a weighting function based on the Minimum Covariance Determinant, which is a highly robust estimator of multivariate location and scatter. Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data. We further show that the robust method also preserves uncorrelated feature learning through qualitative and quantitative experiments on standard datasets. Keywords-- Machine Learning, Generative Models, Robustness, Kernel Methods, Restricted Kernel Machines 1 Introduction Generative modeling is an important direction within machine learning, finding applications in image generation [1], anomaly detection [2], denoising [3], collaborative filtering [4] and many more. A popular choice for generation are latent variable models like V ariational Auto-Encoders (V AEs) [5] and Restricted Boltzmann Machines (RBMs) [6, 7].

Steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI) provides reliable responses leading to high accuracy and information throughput. But achieving high accuracy typically requires a relatively long time window of one second or more. Various methods were proposed to improve sub-second response accuracy through subject-specific training and calibration. Substantial performance improvements were achieved with tedious calibration and subject-specific training; resulting in the user's discomfort. So, we propose a training-free method by combining spatial-filtering and temporal alignment (CSTA) to recognize SSVEP responses in sub-second response time. CSTA exploits linear correlation and non-linear similarity between steady-state responses and stimulus templates with complementary fusion to achieve desirable performance improvements. We evaluated the performance of CSTA in terms of accuracy and Information Transfer Rate (ITR) in comparison with both training-based and training-free methods using two SSVEP data-sets. We observed that CSTA achieves the maximum mean accuracy of 97.43$\pm$2.26 % and 85.71$\pm$13.41 % with four-class and forty-class SSVEP data-sets respectively in sub-second response time in offline analysis. CSTA yields significantly higher mean performance (p<0.001) than the training-free method on both data-sets. Compared with training-based methods, CSTA shows 29.33$\pm$19.65 % higher mean accuracy with statistically significant differences in time window less than 0.5 s. In longer time windows, CSTA exhibits either better or comparable performance though not statistically significantly better than training-based methods. We show that the proposed method brings advantages of subject-independent SSVEP classification without requiring training while enabling high target recognition performance in sub-second response time.

The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision. This paper investigates how to preserve and extract the abundant information from graph-structured data into embedding space in an unsupervised manner. To this end, we propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. GMI generalizes the idea of conventional mutual information computations from vector space to the graph domain where measuring mutual information from two aspects of node features and topological structure is indispensable. GMI exhibits several benefits: First, it is invariant to the isomorphic transformation of input graphs---an inevitable constraint in many existing graph representation learning algorithms; Besides, it can be efficiently estimated and maximized by current mutual information estimation methods such as MINE; Finally, our theoretical analysis confirms its correctness and rationality. With the aid of GMI, we develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder. Considerable experiments on transductive as well as inductive node classification and link prediction demonstrate that our method outperforms state-of-the-art unsupervised counterparts, and even sometimes exceeds the performance of supervised ones.

This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs. Traditionally, real data are taken as positive while generated data are negative. This positive-negative classification criterion was kept fixed all through the learning process of the discriminator without considering the gradually improved quality of generated data, even if they could be more realistic than real data at times. In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality. The discriminator is thus a classifier for this positive and unlabeled classification problem, and we derive a new Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality the proposed model will achieve and the equivalent optimization goal. Empirically, we find that PUGAN can achieve comparable or even better performance than those sophisticated discriminator stabilization methods.

Over the past decade, several approaches have been introduced for short - term traffic prediction. However, providing fine - grained traffic prediction for large - scale transportation networks where numerous detectors are geographically deployed to collect traf fic data is still an open issue. To address this issue, in this paper, we formulate the problem of customizing an LSTM model for a single detector into a finite Markov decision process and then introduce an A utomatic L STM C ustomization (ALC) algorithm to a utomatically customize an LSTM model for a single detector such that the corresponding prediction accuracy can be as satisfactory as possible and the time consumption can be as low as possible. Based on the ALC algorithm, we introduce a distributed approac h called D istributed A utomatic L STM C ustomization (DALC) to customize an LSTM model for every detector in large - scale transportation networks. Our experiment demonstrate s that the DALC provides higher prediction accuracy than several approaches provided by Apache Spark MLlib.

We propose a novel quantum model for the restricted Boltzmann machine (RBM), in which the visible units remain classical whereas the hidden units are quantized as noninteracting fermions. The free motion of the fermions is parametrically coupled to the classical signal of the visible units. This model possesses a quantum behaviour such as coherences between the hidden units. Numerical experiments show that this fact makes it more powerful than the classical RBM with the same number of hidden units. At the same time, a significant advantage of the proposed model over the other approaches to the Quantum Boltzmann Machine (QBM) is that it is exactly solvable and efficiently trainable on a classical computer: there is a closed expression for the log-likelihood gradient with respect to its parameters. This fact makes it interesting not only as a model of a hypothetical quantum simulator, but also as a quantum-inspired classical machine-learning algorithm.

Welcome to Machine Learning Up-To-Date (ML UTD) 1! The LifeWithData blog separates the signal from the noise in today's hectic front lines of software engineering and machine learning. LifeWithData aims to consistently deliver curated machine learning newsletters that point the reader to key developments without massive amounts of backstory for each. This enables frequent, concise updates across the industry without overloading readers with information. Machine Learning Up-To-Date 1 brings innovations in the fields of edge computing, deep learning, ML standardization. Let's continue moving away from bulky cloud server costs with Pytorch mobile and SwiftUI.

IMAGE: Modern sleep diagnostics is based on wearable, non-intrusive methods. A new deep learning model developed by researchers at the University of Eastern Finland can identify sleep stages as accurately as an experienced physician. This opens up new avenues for the diagnostics and treatment of sleep disorders, including obstructive sleep apnoea. Obstructive sleep apnoea (OSA) is a nocturnal breathing disorder that causes a major burden on public health care systems and national economies. It is estimated that up to one billion people worldwide suffer from obstructive sleep apnoea, and the number is expected to grow due to population ageing and increased prevalence of obesity.

How long can a person go without sleep? The world record is apparently 11 days. However, when Randy Gardner set that record in 1965, he may have been awake during the time, but he was basically'cognitively dysfunctional'. If perhaps Gardner went beyond two weeks he would likely have died. Some animals appear to be awake all the time. Cetaceans) need to remain awake because they need to periodically come up to the surface to breathe oxygen.

This may come as a surprise because computers seem like they can understand time series data. After all, aren't self-driving cars, AlphaStar and recurrent neural networks all evidence that today's ML can handle time series data? Self-driving cars use a hybrid of ML and procedural programming. ML (statistical programming) handles the low-level stuff like recognizing pedestrians. Procedural (nonstatistical) programming handles high-level stuff like navigation.