Ensemble and Boosting methods such as Random Forests [3] and AdaBoost [19] are often recognized as some of the best out-of-the-box classifiers, consistently achieving state-ofthe-art performance across a wide range of computer vision tasks including applications in image classification [1], semantic segmentation [22], object recognition [12] and data clustering [16]. The success of these methods is attributed to their ability to learn models (strong learners) which possess low bias and variance through the combination of weakly correlated learners (weak learners). Forests reduce variance through averaging its weak learners over the ensemble. Boosting, on the other hand, looks towards reducing both bias and variance through sequentially optimizing under conditional constraints. The commonality between both approaches is in the way each learner is constructed: both methods use a top-down induction algorithm (such as CART [4]) which greedily learns decision nodes in a recursive manner. This approach is known to be suboptimal in terms of objective maximization as there are no guarantees that a global loss is being minimized [14]. In practice, this type of optimization requires the non-linearity offered by several (very) deep trees, which results in redundancy in learned models with large overlaps of information between weak learners. To address these limitations, the ensemble approaches of [11, 20] have utilized gradient information within a boosting framework. This allows weak learners to be fit via pseudoresiduals or to a set of adaptive weights and allows for the minimization of a global loss via gradient descent.

In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain. In addi-tion, we also give a list of recommendations for the collection and management of datasets. This work is one step towards the dataset engineering process that will be required for ML to be used on safety critical systems.

Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for problems with continuous observation spaces remains challenging. Most on-line solvers rely on discretising the observation space or artificially limiting the number of observations that are considered during planning to compute tractable policies. In this paper we propose a new on-line POMDP solver, called Lazy Belief Extraction for Continuous POMDPs (LABECOP), that combines methods from Monte-Carlo-Tree-Search and particle filtering to construct a policy reprentation which doesn't require discretised observation spaces and avoids limiting the number of observations considered during planning. Experiments on three different problems involving continuous observation spaces indicate that LABECOP performs similar or better than state-of-the-art POMDP solvers.

Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models. In this work, to generate high quality synthetic data for low-resource tagging tasks, we propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings. For the supervised settings, we conduct extensive experiments on named entity recognition (NER), part of speech (POS) tagging and end-to-end target based sentiment analysis (E2E-TBSA) tasks. For the semi-supervised settings, we evaluate our method on the NER task under the conditions of given unlabeled data only and unlabeled data plus a knowledge base. The results show that our method can consistently outperform the baselines, particularly when the given gold training data are less.

An \textit{abstract argumentation framework} ({\sc af} for short) is a directed graph $(A,R)$ where $A$ is a set of \textit{abstract arguments} and $R\subseteq A \times A$ is the \textit{attack} relation. Let $H=(A,R)$ be an {\sc af}, $S \subseteq A$ be a set of arguments and $S^+ = \{y \mid \exists x\in S \text{ with }(x,y)\in R\}$. Then, $S$ is a \textit{stable extension} in $H$ if and only if $S^+ = A\setminus S$. In this paper, we present a thorough, formal validation of a known backtracking algorithm for listing all stable extensions in a given {\sc af}.

Neural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget previously learned knowledge and swing to fit the newly added data which may have a different distribution, e.g. a different domain. Although many methods have been proposed to solve this problem, we cannot get to know what causes this phenomenon yet. Under the background of domain adaptation, we investigate the cause of catastrophic forgetting from the perspectives of modules and parameters (neurons). The investigation on the modules of the NMT model shows that some modules have tight relation with the general-domain knowledge while some other modules are more essential in the domain adaptation. And the investigation on the parameters shows that some parameters are important for both the general-domain and in-domain translation and the great change of them during continual training brings about the performance decline in general-domain. We conduct experiments across different language pairs and domains to ensure the validity and reliability of our findings.

Regularization plays a vital role in the context of deep learning by preventing deep neural networks from the danger of overfitting. This paper proposes a novel deep learning regularization method named as DL-Reg, which carefully reduces the nonlinearity of deep networks to a certain extent by explicitly enforcing the network to behave as much linear as possible. The key idea is to add a linear constraint to the objective function of the deep neural networks, which is simply the error of a linear mapping from the inputs to the outputs of the model. More precisely, the proposed DL-Reg carefully forces the network to behave in a linear manner. This linear constraint, which is further adjusted by a regularization factor, prevents the network from the risk of overfitting. The performance of DL-Reg is evaluated by training state-of-the-art deep network models on several benchmark datasets. The experimental results show that the proposed regularization method: 1) gives major improvements over the existing regularization techniques, and 2) significantly improves the performance of deep neural networks, especially in the case of small-sized training datasets.

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For my final capstone project in Flatiron School's Immersive Data Science Program, I decided to test my newfound skills and continue furthering my personal investigations into the relationships that exist between data, waste, and energy. Recently, I have been learning more about the various ways that Municipal Solid Waste (MSW) can be transformed into energy. The most promising and efficient technology that I have come across to date is Plasma Arc Gasification. In my research, I discovered that understanding specific composition details about the MSW to be used as feedstock is one of many critical steps in designing a plasma gasification facility. What I set out to do for my capstone project, was to see if I could find some MSW collection datasets and perform a Feedstock Analysis with the intent of calculating specific Waste Type Compositions, Energy Density (kWh/kg), and Total Energy (kWh) for each sample.

If you walk into Capitol Hill's restaurants like Emilie's or Kevin Tien, you'll find yourself alone. It is because only one customer can enter at a time due to the coronavirus pandemic. There will be an employee to greet you, wearing a mask and gloves. If you order something, they deliver your food in a new white paper bag. Demonstrating these cautions helps them to win customers' trust that their food is safe from coronavirus.