In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different speakers interleave in the time domain. This RNN is naturally integrated with a distance-dependent Chinese restaurant process (ddCRP) to accommodate an unknown number of speakers. Our system is fully supervised and is able to learn from examples where time-stamped speaker labels are annotated. We achieved a 7.6% diarization error rate on NIST SRE 2000 CALLHOME, which is better than the state-of-the-art method using spectral clustering. Moreover, our method decodes in an online fashion while most state-of-the-art systems rely on offline clustering.

ABSTRACT In order to save the memory, we propose a miniaturization method for neural network to reduce the parameter quantity existed in remote sensing (RS) image semantic segmentation model. The compact convolution optimization method is first used for standard U-Net to reduce the weights quantity. With the purpose of decreasing model performance loss caused by miniaturization and based on the characteristics of remote sensing image, fewer down-samplings and improved cascade atrous convolution are then used to improve the performance of the miniaturized U-Net. Compared with U-Net, our proposed Micro-Net not only achieves 29.26 times model compression, but also basically maintains the performance unchanged on the public dataset. Index Terms--semantic segmentation, compact convolution, atrous convolution, deep learning 1. INTRODUCTION As the major data source in mapping [1], earth observation [2], ground target recognition [3], RS images have important research value.

Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an algorithm, spot-checking is intended to evaluate a diverse set of algorithms rapidly and provide a rough first-cut result. This first cut result may be used to get an idea if a problem or problem representation is indeed predictable, and if so, the types of algorithms that may be worth investigating further for the problem. Spot-checking is an approach to help overcome the "hard problem" of applied machine learning and encourage you to clearly think about the higher-order search problem being performed in any machine learning project. In this tutorial, you will discover the usefulness of spot-checking algorithms on a new predictive modeling problem and how to develop a standard framework for spot-checking algorithms in python for classification and regression problems. How to Develop a Reusable Framework for Spot-Check Algorithms in Python Photo by Jeff Turner, some rights reserved. We cannot know beforehand what algorithms will perform well on a given predictive modeling problem.

When big data is really small Machine Learning for all, what does it mean to democratize AI? A simple example Resources 5 7. 7 The last 40 years have witnessed massive adoption of the relational model It's hard to find any examples today of enterprises whose data isn't in a relational database Millions of human hours invested in building relational models and populating them with data Relational databases are rich with knowledge of the underlying domains that they model The availability and accuracy of large amounts of curated data has made it possible for humans (BI) and machines (AI) to learn from the past and to predict the future The relational model dominates data management 8. When big data is small 9. 9 What would a database do? Features Entities 2. Feature extraction query s: Aggregates (statistics) generated from model spec and feature extraction query 3. Model specification (e.g., "degree 2 ridge regression") 1. Database ID x 1 x 2 x 3 ... y 10. 1 0 Supported methods include Linear regression Polynomial regression Factorization machines Decision trees Linear SVM K-Means & K-Median clustering Principal component analysis Deep sum-product networks (with more on the way) Does it work for all model classes or methods?

Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this tutorial, you'll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text. Reading the mood from text with machine learning is called sentiment analysis, and it is one of the prominent use cases in text classification. This falls into the very active research field of natural language processing (NLP). Other common use cases of text classification include detection of spam, auto tagging of customer queries, and categorization of text into defined topics. So how can you do this? Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you'll need to take your Python skills to the next level. Before we start, let's take a look at what data we have. Go ahead and download the data set from the Sentiment Labelled Sentences Data Set from the UCI Machine Learning Repository. By the way, this repository is a wonderful source for machine learning data sets when you want to try out some algorithms. This data set includes labeled reviews from IMDb, Amazon, and Yelp. Each review is marked with a score of 0 for a negative sentiment or 1 for a positive sentiment. With this data set, you are able to train a model to predict the sentiment of a sentence. Take a quick moment to think about how you would go about predicting the data.

The Expectation-Maximization (EM) algorithm for mixture models often results in slow or invalid convergence. The popular convergence proof affirms that the likelihood increases with Q; Q is increasing in the M -step and non-decreasing in the E-step. The author found that (1) Q may and should decrease in some E-steps; (2) The Shannon channel from the E-step is improper and hence the expectation is improper. The author proposed the CM-EM algorithm (CM means Channel's Matching), which adds a step to optimize the mixture ratios for the proper Shannon channel and maximizes G, average log-normalized-likelihood, in the M-step. Neal and Hinton's Maximization-Maximization (MM) algorithm use F instead of Q to speed the convergence. Maximizing G is similar to maximizing F. The new convergence proof is similar to Beal's proof with the variational method. It first proves that the minimum relative entropy equals the minimum R-G (R is mutual information), then uses variational and iterative methods that Shannon et al. use for rate-distortion functions to prove the global convergence. Some examples show that Q and F should and may decrease in some E-steps. For the same example, the EM, MM, and CM-EM algorithms need about 36, 18, and 9 iterations respectively.

As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data. In this work, we describe a minimax framework for statistical learning with ambiguity sets given by balls in Wasserstein space. In particular, we prove generalization bounds that involve the covering number properties of the original ERM problem. As an illustrative example, we provide generalization guarantees for transport-based domain adaptation problems where the Wasserstein distance between the source and target domain distributions can be reliably estimated from unlabeled samples.

Ensemble methods are among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the complexity of each learner typically grows with the size of the dataset. This phenomenon results in an increasing demand for storage space, which may be very costly. This problem mostly manifests in a subscriber based environment, where a user-specific ensemble needs to be stored on a personal device with strict storage limitations (such as a cellular device). In this work we introduce a novel method for lossless compression of tree-based ensemble methods, focusing on random forests. Our suggested method is based on probabilistic modeling of the ensemble's trees, followed by model clustering via Bregman divergence. This allows us to find a minimal set of models that provides an accurate description of the trees, and at the same time is small enough to store and maintain. Our compression scheme demonstrates high compression rates on a variety of modern datasets. Importantly, our scheme enables predictions from the compressed format and a perfect reconstruction of the original ensemble. In addition, we introduce a theoretically sound lossy compression scheme, which allows us to control the trade-off between the distortion and the coding rate.

Recent progress on deep learning has made it possible to automatically transform the screenshot of Graphic User Interface (GUI) into code by using the encoder-decoder framework. While the commonly adopted image encoder (e.g., CNN network), might be capable of extracting image features to the desired level, interpreting these abstract image features into hundreds of tokens of code puts a particular challenge on the decoding power of the RNN-based code generator. Considering the code used for describing GUI is usually hierarchically structured, we propose a new attention-based hierarchical code generation model, which can describe GUI images in a finer level of details, while also being able to generate hierarchically structured code in consistency with the hierarchical layout of the graphic elements in the GUI. Our model follows the encoder-decoder framework, all the components of which can be trained jointly in an end-to-end manner. The experimental results show that our method outperforms other current state-of-the-art methods on both a publicly available GUI-code dataset as well as a dataset established by our own.

Online learning makes sequence of decisions with partial data arrival where next movement of data is unknown. In this paper, we have presented a new idea as multiple times weight updating that update the weight iteratively for same instance. The proposed technique analyzed with popular algorithms from literature and experimented using established tool. The results indicates that mistake rate reduces to zero or close to zero for various datasets and algorithms. The overhead running cost is not too expensive and achieving mistake rate close to zero further strengthen the proposed technique. The proposed technique could be helpful to meet real life challenges.