Stochastic variance-reduced gradient (SVRG) is a classical optimization method. Although it is theoretically proved to have better convergence performance than stochastic gradient descent (SGD), the generalization performance of SVRG remains open. In this paper we investigate the effects of some training techniques, mini-batching and learning rate decay, on the generalization performance of SVRG, and verify the generalization performance of Batch-SVRG (B-SVRG). In terms of the relationship between optimization and generalization, we believe that the average norm of gradients on each training sample as well as the norm of average gradient indicate how flat the landscape is and how well the model generalizes. Based on empirical observations of such metrics, we perform a sign switch on B-SVRG and derive a practical algorithm, BatchPlus-SVRG (BP-SVRG), which is numerically shown to enjoy better generalization performance than B-SVRG, even SGD in some scenarios of deep neural networks.

Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for efficient allocation of wireless resources. Obtaining Down-Link (DL) CSI in Frequency Division Duplexing (FDD) systems from Up-Link (UL) CSI is not as straightforward as in TDD systems, and so usually users feedback the DL-CSI to the transmitter. To remove the need for feedback (and thus having less signaling overhead), several methods have been studied to estimate DL-CSI from UL-CSI. In this paper, we propose a scheme to infer DL-CSI by observing UL-CSI in which we use two recent deep neural network structures: a) Convolutional Neural networks and b) Generative Adversarial Networks. The proposed deep network structures first learn a latent model of the environment from the training data. Then, the result latent model is used to predict the DL-CSI from the UL-CSI. We have simulated the proposed scheme and evaluated its performance in a few network settings. Simulation results (for different multipath environments) demonstrate efficiency of both direct and generative approaches for UL2DL prediction. One key feature of new generation of cellular networks is their efficient use of frequency bands and energy. To achieve this goal, they use various techniques such as water-filling, appropriate precoding and beamforming. In Time Division Duplexing (TDD) systems, Up-Link (UL) and Down-Link (DL) frequencies are equal, so we can use channel reciprocity and simply infer the DL channel by observing the UL channel.

Abstract--Mining causality from text is a complex and crucial natural language understanding task. Most of the early attempts at its solution can group into two categories: 1) utilizing co-occurrence frequency and world knowledge for causality detection; 2) extracting cause-effect pairs by using connectives and syntax patterns directly. However, because causality has various linguistic expressions, the noisy data and ignoring implicit expressions problems induced by these methods cannot be avoided. In this paper, we present a neural causality detection model, namely Multilevel Causality Detection Network (MCDN), to address this problem. Specifically, we adopt multi-head self-attention to acquire semantic feature at word level and integrate a novel Relation Network to infer causality at segment level. To the best of our knowledge, in touch with the causality tasks, this is the first time that the Relation Network is applied. The experimental results on the AltLex dataset, demonstrate that: a) MCDN is highly effective for the ambiguous and implicit causality inference; b) comparing with the regular text classification task, causality detection requires stronger inference capability; c) the proposed approach achieved state-of- the-art performance. I. Introduction Automatic text causality mining is a critical but difficult task because causality is thought to play an essential role in human cognition when making decisions [1]. Thus, automatic text causality has been studied extensively in a wide range of areas, such as industry [2], physics [3] and healthcare [4], etc. A tool to automatically scour the plethora of textual content on the web and extract meaningful causal relations could help us construct causal chains to unveil previously unknown relationships between events [5] and accelerates the discovery of the intrinsic logic of the events [6]. Many research efforts have been made to mine causality from text corpus with complex sentence structures in the books or newspapers. In Causal-TimeBank [7] authors introduced "CLINK" and "C-SIGNAL" tag to mark events causal relation and causal signals respectively based on specific templates (e.g., "A happened because of B").

YY, ZZZZ 1 Music Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions Eita Nakamura, Kazuyoshi Y oshii, Member, IEEE Abstract --Most work on models for music transcription has focused on describing local sequential dependence of notes in musical scores and failed to capture their global repetitive structure, which can be a useful guide for transcribing music. Focusing on the rhythm, we formulate several classes of Bayesian Markov models of musical scores that describe repetitions indirectly by sparse transition probabilities of notes or note patterns. This enables us to construct piece-specific models for unseen scores with unfixed repetitive structure and to derive tractable inference algorithms. Moreover, to describe approximate repetitions, we explicitly incorporate a process of modifying the repeated notes/note patterns. We apply these models as a prior music language model for rhythm transcription, where piece-specific score models are inferred from performed MIDI data by unsupervised learning, in contrast to the conventional supervised construction of score models. Evaluations using vocal melodies of popular music showed that the Bayesian models improved the transcription accuracy for most of the tested model types, indicating the universal efficacy of the proposed approach. I NTRODUCTION Music transcription is an actively studied but yet unsolved problem in music information processing [1], [2]. One of the goals of music transcription is to convert a music performance signal into a human-readable symbolic musical score. While recent studies have achieved highly accurate pitch detection [3]-[7], it is also necessary to transcribe rhythms in order to obtain symbolic music representation [8]-[18]. Since there are many logically possible representations of rhythms (including meaningless one for humans) for a given performance [11], using a score model that describes prior knowledge about musical scores is a key to solve this problem. A common approach for music transcription is to integrate a musical score (language) model and a performance/acoustic model to obtain a proper transcription that best fits an input performance signal, similarly to the method of statistical speech recognition. More recently, end-to-end approaches have also been attempted [19]-[21], which have been of limited success so far. Manuscript received XX, YY; revised XX, YY . This work was supported partially by JSPS KAKENHI (Nos. The work of EN was supported by the JSPS research fellowship (PD).

Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy transfer. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster.

Although we have focused on the "hot" area of A Vs, our discussion and the novel CBI theorems are more generally applicable. We see them as especially useful now for MLbased systems with critical applications, although not with extreme requirements, since assurance in these systems must rely on combinations of statistical evidence with other verification methods that are, as yet, not well-established. A PPENDIX A. Statement And Proof of CBI Theorem 1 Problem: Consider the set D of all probability distributions defined over the unit interval, each distribution representing a potential prior distribution of pfm values for an A V . For 0 p l null null 1, we seek a prior distribution that minimises the posterior confidence in a reliability bound p [ p l, 1], given k fatalities have occurred over n miles driven and subject to constraints on some quantiles of the prior distribution. That is, for θ (0, 1], we solve minimise D Pr ( X null p k & n) subject to Pr ( X null null) θ, Pr (X null p l) 1 Solution: There is a prior in D that minimises the posterior confidence: the 2-point distribution Pr ( X x) θ 1 x x 1 (1 θ)1 x x 3 where p l null x 1 null null x 3, and the values of x 1 and x 3 both depend on the model parameters (i.e.

Existing conversational systems tend to generate generic responses. Recently, Background Based Conversations (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs are able to generate more informative responses, they either cannot generate natural responses or have difficulty in locating the right background information. In this paper, we propose a Reference-aware Network (RefNet) to address the two issues. Unlike existing methods that generate responses token by token, RefNet incorporates a novel reference decoder that provides an alternative way to learn to directly cite a semantic unit (e.g., a span containing complete semantic information) from the background. Experimental results show that RefNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that RefNet can generate more appropriate and human-like responses.

--eSports is the rapidly developing multidisciplinary domain. However, research and experimentation in eSports are in the infancy. In this work, we propose a smart chair platform - an unobtrusive approach to the collection of data on the eSports athletes and data further processing with machine learning methods. The use case scenario involves three groups of players: cyber athletes (Monolith team), semiprofessional players and newbies all playing CS:GO discipline. In particular, we collect data from the accelerometer and gyroscope integrated in the chair and apply machine learning algorithms for the data analysis. Our results demonstrate that the professional athletes can be identified by their behaviour on the chair while playing the game.

--T oday's competition between the professional eSports teams is so strong that in-depth analysis of players' performance literally crucial for creating a powerful team. There are two main approaches to such an estimation: obtaining features and metrics directly from the in-game data or collecting detailed information about the player including data on his/her physical training. While the correlation between the player's skill and in-game data has already been covered in many papers, there are very few works related to analysis of eSports athlete's skill through his/her physical behavior . We propose the smart chair platform which is to collect data on the person's behavior on the chair using an integrated accelerometer, a gyroscope and a magnetometer . We extract the important game events to define the players' physical reactions to them. The obtained data are used for training machine learning models in order to distinguish between the low-skilled and high-skilled players. We extract and figure out the key features during the game and discuss the results. I NTRODUCTION Nowadays eSports is a rapidly growing industry with more than billion players involved worldwide.

Many airports hope to start using biometric scanners in lieu of passports to identify travelers. Buzz60's Tony Spitz has the details. The next time you go to the airport you might notice something different as part of the security process: A machine scanning your face to verify your identity. U.S. Customs and Border Protection (CBP) has been working with airlines to implement biometric face scanners in domestic airports to better streamline security. But how does the process work?