Reward learning is a fundamental problem in robotics to have robots that operate in alignment with what their human user wants. Many preference-based learning algorithms and active querying techniques have been proposed as a solution to this problem. In this paper, we present APReL, a library for active preference-based reward learning algorithms, which enable researchers and practitioners to experiment with the existing techniques and easily develop their own algorithms for various modules of the problem.

This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations using a top-level Chinese restaurant process. Within each cluster of relations, a Dirichlet process mixture is then used to partition the domain entities and model the probability distribution of relation values. The HIRM generalizes the standard infinite relational model and can be used for a variety of data analysis tasks including dependence detection, clustering, and density estimation. We present new algorithms for fully Bayesian posterior inference via Gibbs sampling. We illustrate the efficacy of the method on a density estimation benchmark of twenty object-attribute datasets with up to 18 million cells and use it to discover relational structure in real-world datasets from politics and genomics.

Data analysis has become a necessity in the modern era of cricket. Everything from effective team management to match win predictions use some form of analytics. Meaningful data representations are necessary for efficient analysis of data. In this study we investigate the use of adaptive (learnable) embeddings to represent inter-related features (such as players, teams, etc). The data used for this study is collected from a classical T20 tournament IPL (Indian Premier League). To naturally facilitate the learning of meaningful representations of features for accurate data analysis, we formulate a deep representation learning framework which jointly learns a custom set of embeddings (which represents our features of interest) through the minimization of a contrastive loss. We base our objective on a set of classes obtained as a result of hierarchical clustering on the overall run rate of an innings. It's been assessed that the framework ensures greater generality in the obtained embeddings, on top of which a task based analysis of overall run rate prediction was done to show the reliability of the framework.

Automated segmentation in medical image analysis is a challenging task that requires a large amount of manually labeled data. However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation. In addition, most existing semi-supervised approaches are usually not robust compared with the supervised counterparts, and also lack explicit modeling of geometric structure and semantic information, both of which limit the segmentation accuracy. In this work, we present SimCVD, a simple contrastive distillation framework that significantly advances state-of-the-art voxel-wise representation learning. We first describe an unsupervised training strategy, which takes two views of an input volume and predicts their signed distance maps of object boundaries in a contrastive objective, with only two independent dropout as mask. This simple approach works surprisingly well, performing on the same level as previous fully supervised methods with much less labeled data. We hypothesize that dropout can be viewed as a minimal form of data augmentation and makes the network robust to representation collapse. Then, we propose to perform structural distillation by distilling pair-wise similarities. We evaluate SimCVD on two popular datasets: the Left Atrial Segmentation Challenge (LA) and the NIH pancreas CT dataset. The results on the LA dataset demonstrate that, in two types of labeled ratios (i.e., 20% and 10%), SimCVD achieves an average Dice score of 90.85% and 89.03% respectively, a 0.91% and 2.22% improvement compared to previous best results. Our method can be trained in an end-to-end fashion, showing the promise of utilizing SimCVD as a general framework for downstream tasks, such as medical image synthesis and registration.

This method is also known as Holt's method, after Charles C. Holt and his paper from 1957. It's called double exponential smoothing because it's based on two smoothing parameters -- Alpha (for level) and Beta (for trend). The algorithm solves the primary issue of simple exponential smoothing, as now the forecasts can account for the trend in historical data. The ExponentialSmoothing class from the Statsmodels library accepts a trend parameter, with the possible values being add and mul. If you can't decide which type of trend is present in your data, test both and see which results in the lowest error.

All the references are taken from the book -- Hands On Machine Learning with Scikit-learn, Keras & Tensorflow by Aurelien Geron. Notebook for this article can be found here. Support Vector Machines can be used for linear or non-linear classification, regression and even outlier detection. It is well suited for complex-small or medium-sized datasets. SVMs are also sensitive to feature scaling, if the feature are standardized it will generalize better.

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis. The book has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian and Vietnamese.

It is a method of machine learning where the model learns from the supervisory signal of the data unlike supervised learning where separate labels are specified for each observation. It is also known as Representation Learning. Note, the model's learned representation is used for downstream tasks like BERT, where language models are used for downstream tasks like text classification. Here, we can use Linear classifiers along with a learned self-supervised model for prediction. Recently, self-supervised learning has seen a great surge in the number of papers getting published for few obvious reasons including the availability of unlabelled data.

What does this paper achieve? It introduces an algorithm to learn connections between blocks in deep learning networks. A connection between blocks i and j (i precedes j in the network) indicates that output from i is added to the input to j (which may be getting input from other blocks). How does this help us? This algorithm can be used to determine connections between blocks in existing CNNs that would improve performance, as the authors have demonstrated for ResNet and ResNext in the paper.

Learn how to solve real life problem using the Machine learning techniques Machine Learning models such as Linear Regression, Logistic Regression, KNN etc. Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc. Understanding of basics of statistics and concepts of Machine Learning How to do basic statistical operations and run ML models in Python Indepth knowledge of data collection and data preprocessing for Machine Learning problem How to convert business problem into a Machine learning problem