A 2016 report claims that annually upwards of 235 000 Canadians endure periods of homelessness, with approximately 35 000 individuals lacking a place to stay each night [1]. Between 2005 and 2014, there was a downward trend in the total number of Canadians using shelters; however, the occupancy rates of shelters has been increasing [1]. One factor accounting for this ongoing decrease in the number of homeless individuals paired with an increase in shelter occupancy is an increase in chronic homelessness. London's Homeless Prevention division identifies an individual as chronically homelessness if they have spent 6 or more months ( 180 days) of the last year in a shelter, which was based on the definition of chronic homelessness outlined by the Canadian government's homelessness strategy directives [2]. In addition to this trend, the demographics of homelessness are changing in Canada. In preceding decades, older, single males are over-represented in the homeless population; in contrast, the homeless population of today is increasingly diverse, with families, women, and youth comprising a greater fraction [1].

Now, let's jump to the implementation. Firstly, we need to, obviously, import some libraries. We import tensorflow as it is the main thing we use for the implementation, matplotlib for visualizing our results, make_regression function, from sklearn, which we will be using to generate a regression dataset for using as an example, and the python's built-in math module. The first thing we do inside .fit() is to concatenate an extra column of 1's to our input matrix X. This is to simplify our math and treat the bias as the weight of an extra variable that's always 1.

Neural Networks are quite confusing, especially when you are programming them from scratch. This article will give you an intuition for the relevant concepts necessary for the neural network to work, by creating and training a univariate regression model, with one synapse, between the input and output. Imagine you are a doctor, and you want to find a quantitative relationship between height and weight: That is, you want to be able to reliably predict the weight of someone, given their height. So far, you have data on 5 people's height and weight. When we look at this graph, we can visualize a line that best explains the data, but we want to quantify this.

In data science or machine learning, we use data for descriptive analytics to draw out meaningful conclusions from the data, or we can use data for predictive purposes to build models that can make predictions on unseen data. The reliability of any model depends on the level of expertise of the data scientist. It is one thing to build a machine learning model. It is another thing to ensure the model is optimal and of the highest quality. This article will discuss six common mistakes that can adversely influence the quality or predictive power of a machine learning model with several case studies included.

A common technique for compressing a neural network is to compute the $k$-rank $\ell_2$ approximation $A_{k,2}$ of the matrix $A\in\mathbb{R}^{n\times d}$ that corresponds to a fully connected layer (or embedding layer). Here, $d$ is the number of the neurons in the layer, $n$ is the number in the next one, and $A_{k,2}$ can be stored in $O((n+d)k)$ memory instead of $O(nd)$. This $\ell_2$-approximation minimizes the sum over every entry to the power of $p=2$ in the matrix $A - A_{k,2}$, among every matrix $A_{k,2}\in\mathbb{R}^{n\times d}$ whose rank is $k$. While it can be computed efficiently via SVD, the $\ell_2$-approximation is known to be very sensitive to outliers ("far-away" rows). Hence, machine learning uses e.g. Lasso Regression, $\ell_1$-regularization, and $\ell_1$-SVM that use the $\ell_1$-norm. This paper suggests to replace the $k$-rank $\ell_2$ approximation by $\ell_p$, for $p\in [1,2]$. We then provide practical and provable approximation algorithms to compute it for any $p\geq1$, based on modern techniques in computational geometry. Extensive experimental results on the GLUE benchmark for compressing BERT, DistilBERT, XLNet, and RoBERTa confirm this theoretical advantage. For example, our approach achieves $28\%$ compression of RoBERTa's embedding layer with only $0.63\%$ additive drop in the accuracy (without fine-tuning) in average over all tasks in GLUE, compared to $11\%$ drop using the existing $\ell_2$-approximation. Open code is provided for reproducing and extending our results.

With the ever-increasing use of complex machine learning models in critical applications within the finance domain, explaining the decisions of the model has become a necessity. With applications spanning from credit scoring to credit marketing, the impact of these models is undeniable. Among the multiple ways in which one can explain the decisions of these complicated models, local post hoc model agnostic explanations have gained massive adoption. These methods allow one to explain each prediction independent of the modelling technique that was used while training. As explanations, they either give individual feature attributions or provide sufficient rules that represent conditions for a prediction to be made. The current state of the art methods use rudimentary methods to generate synthetic data around the point to be explained. This is followed by fitting simple linear models as surrogates to obtain a local interpretation of the prediction. In this paper, we seek to significantly improve on both, the method used to generate the explanations and the nature of explanations produced. We use a Generative Adversarial Network for synthetic data generation and train a piecewise linear model in the form of Linear Model Trees to be used as the surrogate model.In addition to individual feature attributions, we also provide an accompanying context to our explanations by leveraging the structure and property of our surrogate model.

We present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled video. We first train an autoencoder that maps each video frame into a low-dimensional latent space where the laws of motion are as simple as possible, by minimizing a combination of non-linearity, acceleration and prediction error. Differential equations describing the motion are then discovered using Pareto-optimal symbolic regression. We find that our pre-regression ("pregression") step is able to rediscover Cartesian coordinates of unlabeled moving objects even when the video is distorted by a generalized lens. Using intuition from multidimensional knot-theory, we find that the pregression step is facilitated by first adding extra latent space dimensions to avoid topological problems during training and then removing these extra dimensions via principal component analysis.

Communicating through social platforms has become one of the principal means of personal communications and interactions. Unfortunately, healthy communication is often interfered by offensive language that can have damaging effects on the users. A key to fight offensive language on social media is the existence of an automatic offensive language detection system. This paper presents the results and the main findings of SemEval-2020, Task 12 OffensEval Sub-task A Zampieri et al. (2020), on Identifying and categorising Offensive Language in Social Media. The task was based on the Arabic OffensEval dataset Mubarak et al. (2020). In this paper, we describe the system submitted by WideBot AI Lab for the shared task which ranked 10th out of 52 participants with Macro-F1 86.9% on the golden dataset under CodaLab username "yasserotiefy". We experimented with various models and the best model is a linear SVM in which we use a combination of both character and word n-grams. We also introduced a neural network approach that enhanced the predictive ability of our system that includes CNN, highway network, Bi-LSTM, and attention layers.

While objective indicators are more transparent and robust, the subjective evaluation contains a wealth of expert knowledge and intuition. In this work, we demonstrate the potential of pairwise ranking methods to align the subjective evaluation with objective indicators, creating a new score that combines their advantages and facilitates diagnosis. In a case study on patients at risk for developing Psoriatic Arthritis, we illustrate that the resulting score (1) increases classification accuracy when detecting disease presence/absence, (2) is sparse and (3) provides a nuanced assessment of severity for subsequent analysis.

Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The spatio-temporal regression problem is of paramount importance from both the methodology development and real-world application perspectives. Given the observed spatially encoded time series covariates and real-valued response data samples, the goal of spatio-temporal regression is to leverage the temporal and spatial dependencies to build a mapping from covariates to response with minimized prediction error. Prior arts, including the convolutional Long Short-Term Memory (CovLSTM) and variations of the functional linear models, cannot learn the spatio-temporal information in a simple and efficient format for proper model building. In this work, we propose two novel extensions of the Functional Neural Network (FNN), a temporal regression model whose effectiveness and superior performance over alternative sequential models have been proven by many researchers. The effectiveness of the proposed spatio-temporal FNNs in handling varying spatial correlations is demonstrated in comprehensive simulation studies. The proposed models are then deployed to solve a practical and challenging precipitation prediction problem in the meteorology field.