Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially problematic for image-based RL, where a change in just one variable, such as the background colour, can change many pixels in the image. The changed pixels can lead to drastic changes in the agent's latent representation of the image, causing the learned policy to fail. To learn more robust representations, we introduce TEmporal Disentanglement (TED), a self-supervised auxiliary task that leads to disentangled image representations exploiting the sequential nature of RL observations. We find empirically that RL algorithms utilising TED as an auxiliary task adapt more quickly to changes in environment variables with continued training compared to state-of-the-art representation learning methods. Since TED enforces a disentangled structure of the representation, our experiments also show that policies trained with TED generalise better to unseen values of variables irrelevant to the task (e.g. background colour) as well as unseen values of variables that affect the optimal policy (e.g. goal positions).

We've finished the Data Preprocessing part and now it's time to start making Machine Learning Models. We're are going to start with the Simple Linear Regression Model and I will show you how to do it in R. To Learn how to do Simple Linear Regressions in Python, go Here. Before we begin, we need to understand our data and the problem we are trying to solve. I have prepared the dataset that we are going to be using in this tutorial. However, feel free to use any dataset that you may have, and see if you'll get similar results.

Create a regressor and call it, 'poly_reg'. Assign the regressor to the lm() function as we did in linear regression. The function takes two arguments. The formula and the data, same way we did in linear regression. To transform this from a linear regression to a polynomial regression model, we need to add some polynomial features.

We are going to learn how to implement a Multiple Linear Regression model in R. This is a bit more complex than Simple Linear Regression but it's going to be so practical and fun. Multiple Linear Regression is a data science technique that uses several explanatory variables to predict the outcome of a response variable. A Multiple linear regression model attempts to model the relationship between two or more explanatory variables (independent variables) and a response variable (dependent variable), by fitting a linear equation to observed data. Every value of the independent variable x is associated with a value of the dependent variable y.

We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic information. We propose a conceptually simple approach that allows for an analytic solution similar to standard PCA and can be kernelized. Our methods have the same complexity as standard PCA, or kernel PCA, and run much faster than existing methods for fair PCA based on semidefinite programming or manifold optimization, while achieving similar results.

In this Python tutorial, we learn about How to Use Activation Functions in Neural Networks. Activation functions play an integral role in neural networks by introducing nonlinearity. This nonlinearity allows neural networks to develop complex representations and functions based on the inputs that would not be possible with a simple linear regression model. Many different nonlinear activation functions have been proposed throughout the history of neural networks. In this post, you will explore three popular ones: sigmoid, tanh, and ReLU.

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. As there are various pattern mining algorithms, this study aimed to validate which algorithm discovers the best set of movement patterns for player movement profiling in professional rugby league and the similarity in extracted movement patterns between the algorithms. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Machine learning classification algorithms were used to identify which algorithm gives the best set of movement patterns to separate playing positions with Jaccard similarity score identifying the extent of similarity between algorithms' movement patterns. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant similarity with LCCspm and LCS patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive movement patterns for separating groups of players.

Ultrasound is progressing toward becoming an affordable and versatile solution to medical imaging. With the advent of COVID-19 global pandemic, there is a need to fully automate ultrasound imaging as it requires trained operators in close proximity to patients for a long period of time, therefore increasing risk of infection. In this work, we investigate the important yet seldom-studied problem of scan target localization, under the setting of lung ultrasound imaging. We propose a purely vision-based, data driven method that incorporates learning-based computer vision techniques. We combine a human pose estimation model with a specially designed regression model to predict the lung ultrasound scan targets, and deploy multiview stereo vision to enhance the consistency of 3D target localization. While related works mostly focus on phantom experiments, we collect data from 30 human subjects for testing. Our method attains an accuracy level of 16.00(9.79) mm for probe positioning and 4.44(3.75) degree for probe orientation, with a success rate above 80% under an error threshold of 25mm for all scan targets. Moreover, our approach can serve as a general solution to other types of ultrasound modalities. The code for implementation has been released.

When implementing the gradient descent method in low precision, the employment of stochastic rounding schemes helps to prevent stagnation of convergence caused by the vanishing gradient effect. Unbiased stochastic rounding yields zero bias by preserving small updates with probabilities proportional to their relative magnitudes. This study provides a theoretical explanation for the stagnation of the gradient descent method in low-precision computation. Additionally, we propose two new stochastic rounding schemes that trade the zero bias property with a larger probability to preserve small gradients. Our methods yield a constant rounding bias that, on average, lies in a descent direction. For convex problems, we prove that the proposed rounding methods typically have a beneficial effect on the convergence rate of gradient descent. We validate our theoretical analysis by comparing the performances of various rounding schemes when optimizing a multinomial logistic regression model and when training a simple neural network with an 8-bit floating-point format.

Tree-based supervised learning algorithms, such as the Classification and Regression Tree (CART) (Breiman et al., 1984), Random Forests (Breiman, 2001), and XGBoost (Chen and Guestrin, 2016) are popular in practice due to their ability to learn complex nonlinear functions efficiently. Bayesian Additive Regression Trees (BART, Chipman et al. (2010)) is the most popular model-based regression tree method; it has been demonstrated empirically to provide accurate out-of-sample prediction (without covariate shift), and its Bayesian uncertainty intervals often out-perform alternatives in terms of frequentist coverage (see Chipman et al. (2010); Kapelner and Bleich (2013)). XBART (He and Hahn, 2021) is a stochastic tree ensemble method that can be used to approximate BART models in a fraction of the run-time. Throughout the paper, we will refer to BART models but will use the XBART fitting algorithm. While tree-based methods frequently provide accurate out-of-sample predictions, their ability to extrapolate is fundamentally limited by their intrinsic, piecewise constant structure.