Sub-sampling for Efficient Non-Parametric Bandit Exploration

In this paper we propose the first multi-armed bandit algorithm based on re-sampling that achieves asymptotically optimal regret simultaneously for different families of arms (namely Bernoulli, Gaussian and Poisson distributions). Unlike Thompson Sampling which requires to specify a different prior to be optimal in each case, our proposal RB-SDA does not need any distribution-dependent tuning… RB-SDA belongs to the family of Sub-sampling Duelling Algorithms (SDA) which combines the sub-sampling idea first used by the BESA [1] and SSMC [2] algorithms […]

October 28, 2020 Machine Learning

Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning

Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities… Multi-temporal satellite images and publicly available webcam streams are among the viable data sources to monitor lake ice. In this work we investigate machine learning-based image […]

October 28, 2020 Machine Learning

Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning

Self-supervised visual pretraining has shown significant progress recently. Among those methods, SimCLR greatly advanced the state of the art in self-supervised and semi-supervised learning on ImageNet… The input feature representations for speech and visual tasks are both continuous, so it is natural to consider applying similar objective on speech representation learning. In this paper, we propose Speech SimCLR, a new self-supervised objective for speech representation learning. During training, Speech SimCLR applies augmentation on raw speech and its spectrogram. Its objective […]

October 28, 2020 Python

What Does if name == “main”: Do in Python?

Introduction It’s common to see if __name__ == “__main__” in Python scripts we find online, or one of the many we write ourselves. Why do we use that if-statement when running our Python programs? In this article, we explain the mechanics behind its usage, the advantages, and where it can be used. The __name__ Attribute and the __main__ Scope The __name__ attribute comes by default as one of the names in the current local scope. The Python interpreter automatically adds […]

October 28, 2020 Python

Seaborn Scatter Plot – Tutorial and Examples

Introduction Seaborn is one of the most widely used data visualization libraries in Python, as an extension to Matplotlib. It offers a simple, intuitive, yet highly customizable API for data visualization. In this tutorial, we’ll take a look at how to plot a scatter plot in Seaborn. We’ll cover simple scatter plots, multiple scatter plots with FacetGrid as well as 3D scatter plots. Import Data We’ll use the World Happiness dataset, and compare the Happiness Score against varying features to […]

October 28, 2020 Python

How to Set Axis Range (xlim, ylim) in Matplotlib

Introduction Matplotlib is one of the most widely used data visualization libraries in Python. Much of Matplotlib’s popularity comes from its customization options – you can tweak just about any element from its hierarchy of objects. In this tutorial, we’ll take a look at how to set the axis range (xlim, ylim) in Matplotlib, to truncate or expand the view to specific limits. Creating a Plot Let’s first create a simple plot: import matplotlib.pyplot as plt import numpy as np […]

October 28, 2020 Artificial intelligence Leave a comment

Enabling interaction between mixed reality and robots via cloud-based localization

You are here. We see some representation of this every day—a red pin, a pulsating blue dot, a small graphic of an airplane. Without a point of reference on which to anchor it, though, here doesn’t help us make our next move or coordinate with others. But in the context of an office building, street, or U.S. map, “here” becomes a location that we can understand in relation to other points. We’re near the lobby; at the intersection of Broadway […]

October 28, 2020 Machine Learning

Error-Correcting Output Codes (ECOC) for Machine Learning

Machine learning algorithms, like logistic regression and support vector machines, are designed for two-class (binary) classification problems. As such, these algorithms must either be modified for multi-class (more than two) classification problems or not used at all. The Error-Correcting Output Codes method is a technique that allows a multi-class classification problem to be reframed as multiple binary classification problems, allowing the use of native binary classification models to be used directly. Unlike one-vs-rest and one-vs-one methods that offer a similar […]

October 26, 2020 Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 56: Beam Search and Models’ Surprisal

Last year an EMNLP paper “On NMT Search Errors and Model Errors: Cat Got Your Tongue?” (that I discussed in MT Weekly 20) showed a mindblowing property of neural machine translation models that the most probable target sentence is not necessarily the best target sentence. In NMT, we model the target sentence probably that is factorized using the chain rule into conditional token probabilities. We can imagine the target sentence generation like this: The model estimates the probability of the […]

October 26, 2020 Machine Learning

Why Use Ensemble Learning?

What are the Benefits of Ensemble Methods for Machine Learning? Ensembles are predictive models that combine predictions from two or more other models. Ensemble learning methods are popular and the go-to technique when the best performance on a predictive modeling project is the most important outcome. Nevertheless, they are not always the most appropriate technique to use and beginners the field of applied machine learning have the expectation that ensembles or a specific ensemble method are always the best method […]

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