In speech area, it has been applied The use of deep networks to extract embeddings for speaker to speech enhancement [11, 12, 13], voice conversion [14], acoustic recognition has proven successfully. However, such embeddings model adaptation [15, 16, 17], noise-robust [18, 19], speakerinvariant are susceptible to performance degradation due to the mismatches [20, 21] automatic speech recognition, speaker model adaptation among the training, enrollment, and test conditions. In this work, we [22] and speech enhancement [23, 11, 24] using gradient reversal propose an adversarial speaker verification (ASV) scheme to learn layer (GRL) [25]. In these works, adversarial learning is the condition-invariant deep embedding via adversarial multi-task used to learn an intermediate representation in a DNN that is invariant training. In ASV, a speaker classification network and a condition to the shift among different conditions (e.g., environments, identification network are jointly optimized to minimize the speaker speakers, SNRs, etc.). To benefit from this, in this work, we propose classification loss and simultaneously mini-maximize the condition adversarial speaker verification (ASV) to suppress the effects loss. The target labels of the condition network can be categorical of condition variability in speaker modeling for robust SV.

Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function. In this work, we propose an attentive ADIT (AADIT) in which we advance the domain classifier with an attention mechanism to automatically weight the input deep features according to their importance in domain classification. With this attentive re-weighting, AADIT can focus on the domain normalization of phonetic components that are more susceptible to domain variability and generates deep features with improved domain-invariance and senone-discriminativity over ADIT. Most importantly, the attention block serves only as an external component to the DNN acoustic model and is not involved in ASR, so AADIT can be used to improve the acoustic modeling with any DNN architectures. More generally, the same methodology can improve any adversarial learning system with an auxiliary discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3% relative WER improvements, respectively, over a multi-conditional model and a strong ADIT baseline.

The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance. To overcome this problem, we propose a conditional T/S learning scheme, in which a "smart" student model selectively chooses to learn from either the teacher model or the ground truth labels conditioned on whether the teacher can correctly predict the ground truth. Unlike a naive linear combination of the two knowledge sources, the conditional learning is exclusively engaged with the teacher model when the teacher model's prediction is correct, and otherwise backs off to the ground truth. Thus, the student model is able to learn effectively from the teacher and even potentially surpass the teacher. We examine the proposed learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker adaptation on Microsoft short message dictation dataset. The proposed method achieves 9.8% and 12.8% relative word error rate reductions, respectively, over T/S learning for environment adaptation and speaker-independent model for speaker adaptation.

The Predictive Normalized Maximum Likelihood (pNML) scheme has been recently suggested for universal learning in the individual setting, where both the training and test samples are individual data. The goal of universal learning is to compete with a ``genie'' or reference learner that knows the data values, but is restricted to use a learner from a given model class. The pNML minimizes the associated regret for any possible value of the unknown label. Furthermore, its min-max regret can serve as a pointwise measure of learnability for the specific training and data sample. In this work we examine the pNML and its associated learnability measure for the Deep Neural Network (DNN) model class. As shown, the pNML outperforms the commonly used Empirical Risk Minimization (ERM) approach and provides robustness against adversarial attacks. Together with its learnability measure it can detect out of distribution test examples, be tolerant to noisy labels and serve as a confidence measure for the ERM. Finally, we extend the pNML to a ``twice universal'' solution, that provides universality for model class selection and generates a learner competing with the best one from all model classes.

Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -- ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

In current clinical practice, score-based mortality prediction systems, such as the series of the acute Predicting the risk of mortality for patients with acute physiology and chronic health evaluation (APACHE) scoring myocardial infarction (AMI) using electronic health records system, are widely used to help determine the treatment or (EHRs) data can help identify risky patients who might need medicine should be given to patients admitted into intensive more tailored care. In our previous work, we built care units (ICUs) [10]. Nevertheless, these scoring systems computational models to predict one-year mortality of patients have significant limitations, e.g., 1) they are often restricted to admitted to an intensive care unit (ICU) with AMI or post only few predictors; 2) they have poor generalizability and may myocardial infarction syndrome. Our prior work only used the be less precise when applied to specific subpopulations other structured clinical data from MIMIC-III, a publicly available than the original population used for the initial development; ICU clinical database. In this study, we enhanced our work by and 3) they need to be periodically recalibrated to reflect adding the word embedding features from free-text discharge changes in clinical practice and patient demographics [6].

We present RL-GAN-Net, where a reinforcement learning (RL) agent provides fast and robust control of a generative adversarial network (GAN). Our framework is applied to point cloud shape completion that converts noisy, partial point cloud data into a high-fidelity completed shape by controlling the GAN. While a GAN is unstable and hard to train, we circumvent the problem by (1) training the GAN on the latent space representation whose dimension is reduced compared to the raw point cloud input and (2) using an RL agent to find the correct input to the GAN to generate the latent space representation of the shape that best fits the current input of incomplete point cloud. The suggested pipeline robustly completes point cloud with large missing regions. To the best of our knowledge, this is the first attempt to train an RL agent to control the GAN, which effectively learns the highly nonlinear mapping from the input noise of the GAN to the latent space of point cloud. The RL agent replaces the need for complex optimization and consequently makes our technique real time. Additionally, we demonstrate that our pipelines can be used to enhance the classification accuracy of point cloud with missing data.

In this guide, I'll go into detail about how artificial intelligence is impacting marketing right now and how it will continue to impact it in the future. At the end of this post, you'll be excited about the possibilities of AI and probably a little nervous about the implications! And it's alright to be nervous because the role of marketers in organizations will change but…. Would you prefer to read this later? Artificial Intelligence in Marketing is real and now is the time to sit up and take notice. Artificial intelligence is accelerating marketing toward a more intelligently automated future in which smarter (i.e. AI-powered) solutions enable marketers to solve problems and achieve goals more efficiently. You can sit back and wait for the marketing world to get smarter and change around you, or you can embrace AI now and be proactive in creating a competitive advantage for yourself and your company. However, not all software companies really have AI that say they do. There's just so much hype surrounding AI Tech companies want to capitalize on it by saying their software is powered by AI and investors will give higher valuations to them because of the AI in their software. But there are many great software companies building true AI applications and this is set to grow massively over the next few years. MRFR research predicted the AI market to be worth 25 billion by 2025. If you're a marketer, it's time to get up to speed and understand the potential impact that AI will have on marketing. I'm pretty sure that this guide will help.

With the rapid development of knowledge bases(KBs),question answering(QA)based on KBs has become a hot research issue. In this paper,we propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus answering single-relation factoid question. In both of two frameworks,we study the effect of context information on the quality of QA,such as the entity's notable type,out-degree. In the end-to-end framework,we combine char-level encoding and self-attention mechanisms,using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition,we find that the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy and take much shorter time than them.

In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy that maps a set of available information processed by a recurrent neural network to ESSs' charging/discharging actions. Finally, we verify the effectiveness of our algorithm using real-time electricity prices from PJM.