We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.

Several methods have been proposed to solve such derivative-free stochastic optimization problems, and we refer the reader to [3, 38] for surveys of these methods. A popular class of these methods estimate the gradients using function values and employ standard gradient-based optimization methods using these estimators. Quasi-Newton methods are recognized as one of the most powerful methods for solving deterministic optimization problems. These methods build quadratic models of the objective information using only gradient information. Recently, researchers have been adapting these methods for stochastic settings when the gradient information is available. The empirical results in [15] indicate that a careful implementation of these methods can be efficient compared with the popular stochastic gradient methods. We adapt these methods to make them suitable for situations where the gradients are estimated using function values. We propose finite-difference derivative-free stochastic quasi-Newton methods for solving (1) by exploiting common random number (CRN) evaluations of f.

In machine learning, differential privacy and federated learning concepts are gaining more and more importance in an increasingly interconnected world. While the former refers to the sharing of private data characterized by strict security rules to protect individual privacy, the latter refers to distributed learning techniques in which a central server exchanges information with different clients for machine learning purposes. In recent years, many studies have shown the possibility of bypassing the privacy shields of these systems and exploiting the vulnerabilities of machine learning models, making them leak the information with which they have been trained. In this work, we present the 3DGL framework, an alternative to the current federated learning paradigms. Its goal is to share generative models with high levels of $\varepsilon$-differential privacy. In addition, we propose DDP-$\beta$VAE, a deep generative model capable of generating synthetic data with high levels of utility and safety for the individual. We evaluate the 3DGL framework based on DDP-$\beta$VAE, showing how the overall system is resilient to the principal attacks in federated learning and improves the performance of distributed learning algorithms.

The micro-segmentation of customers in the finance sector is a non-trivial task and has been an atypical omission from recent scientific literature. Where traditional segmentation classifies customers based on coarse features such as demographics, micro-segmentation depicts more nuanced differences between individuals, bringing forth several advantages including the potential for improved personalization in financial services. AI and representation learning offer a unique opportunity to solve the problem of micro-segmentation. Although ubiquitous in many industries, the proliferation of AI in sensitive industries such as finance has become contingent on the imperatives of responsible AI. We had previously solved the micro-segmentation problem by extracting temporal features from the state space of a recurrent neural network (RNN). However, due to the inherent opacity of RNNs our solution lacked an explanation - one of the imperatives of responsible AI. In this study, we address this issue by extracting an explanation for and providing an interpretation of our temporal features. We investigate the state space of our RNN and through a linear regression model reconstruct the trajectories in the state space with high fidelity. We show that our linear regression coefficients have not only learned the rules used to create the RNN's output data but have also learned the relationships that were not directly evident in the raw data.

Graph neural networks have emerged as a powerful model for graph representation learning to undertake graph-level prediction tasks. Various graph pooling methods have been developed to coarsen an input graph into a succinct graph-level representation through aggregating node embeddings obtained via graph convolution. However, most graph pooling methods are heavily node-centric and are unable to fully leverage the crucial information contained in global graph structure. This paper presents a cross-view graph pooling (Co-Pooling) method to better exploit crucial graph structure information. The proposed Co-Pooling fuses pooled representations learnt from both node view and edge view. Through cross-view interaction, edge-view pooling and node-view pooling seamlessly reinforce each other to learn more informative graph-level representations. Co-Pooling has the advantage of handling various graphs with different types of node attributes. Extensive experiments on a total of 15 graph benchmark datasets validate the effectiveness of our proposed method, demonstrating its superior performance over state-of-the-art pooling methods on both graph classification and graph regression tasks.

In my last article, I introduced you to the world of marketing mix modeling. If you have not read it so far, please do before you proceed. There, we have a created a linear regression model that is able to predict sales based on raw advertising spends in several advertising channels, such as TV, radio, web banners. For me as a machine learning practitioner, such a model is nice already on its own. Even better, it also makes business people happy because the model lets us calculate ROIs, allowing us to judge how well each channel performed.

Polynomial regression is one of the machine learning algorithms used for making predictions. For example, polynomial regression is widely applied to predict the spread rate of COVID-19 and other infectious diseases. If you would like to learn more about what polynomial regression analysis is, continue reading. Regression analysis is a helpful statistical tool for studying the correlation between two sets of events, or, statistically speaking, variables ― between a dependent variable and one or more independent variables. For example, your weight loss (dependent variable) depends on the number of hours you spend in the gym (independent variable).

There are other optimization algorithms than gradient descent. These algorithms automatically pick the appropriate learning rate alpha, and are usually faster. One way to do this is to do a "One vs all" binary classification. To do this we do a binary classification with a certain class and all the other classes, and select the largest one that has the largest hypothesis output. Since we have 3 classes here, we do the binary classification 3 times.

Machine learning is broad and applicable in many fields. So you might get lost trying to find a foothold as a beginner. Nonetheless, taking up projects while learning helps you decipher your interests and focus on a specific path. Additionally, it lets you familiarize yourself with the typical machine learning workflow. Here, we'll show you some of the best beginner project ideas that'll help you dive deeper into the nitty-gritty of machine learning.

Remaining useful life (RUL) refers to the expected remaining lifespan of a component or system. Accurate RUL prediction is critical for prognostic and health management and for maintenance planning. In this work, we address three prevalent challenges in data-driven RUL prediction, namely the handling of high dimensional input features, the robustness to noise in sensor data and prognostic datasets, and the capturing of the time-dependency between system degradation and RUL prediction. We devise a highly accurate RUL prediction model with uncertainty quantification, which integrates and leverages the advantages of deep learning and nonstationary Gaussian process regression (DL-NSGPR). We examine and benchmark our model against other advanced data-driven RUL prediction models using the turbofan engine dataset from the NASA prognostic repository. Our computational experiments show that the DL-NSGPR predictions are highly accurate with root mean square error 1.7 to 6.2 times smaller than those of competing RUL models. Furthermore, the results demonstrate that RUL uncertainty bounds with the proposed DL-NSGPR are both valid and significantly tighter than other stochastic RUL prediction models. We unpack and discuss the reasons for this excellent performance of the DL-NSGPR.