The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning. Despite its apparent successes in these settings, to the best of our knowledge there has been no systematic exploration of its capabilities on simple regression tasks. In this work we characterize these on the UCI datasets, a popular benchmark for Bayesian regression models, as well as on the recently introduced UCI "gap" datasets, which are better tests of out-of-distribution uncertainty. We demonstrate that the neural linear model is a simple method that shows generally good performance on these tasks, but at the cost of requiring good hyperparameter tuning.

With ongoing developments and innovations in single-cell RNA sequencing methods, advancements in sequencing performance could empower significant discoveries as well as new emerging possibilities to address biological and medical investigations. In the study, we will be using the dataset collected by the authors of Systematic comparative analysis of single cell RNA-sequencing methods. The dataset consists of single-cell and single nucleus profiling from three types of samples - cell lines, peripheral blood mononuclear cells, and brain tissue, which offers 36 libraries in six separate experiments in a single center. Our quantitative comparison aims to identify unique characteristics associated with different single-cell sequencing methods, especially among low-throughput sequencing methods and high-throughput sequencing methods. Our procedures also incorporate evaluations of every method's capacity for recovering known biological information in the samples through clustering analysis.

EDU Robotics Institute, Carnegie Mellon University, Pittsburgh, P A 15213, USA Abstract High fidelity behavior prediction of intelligent agents is critical in many applications. However, the prediction model trained on the training set may not generalize to the testing set due to domain shift and time variance. The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance. Inspired by Extended Kalman Filter (EKF), this paper introduces a series of online adaptation methods, which are applicable to neural network-based models. A base adaptation algorithm Modified EKF with forgetting factor (MEKF λ) is introduced first, followed by exponential moving average filtering techniques. Then this paper introduces a dynamic multi-epoch update strategy to effectively utilize samples received in real time. With all these extensions, we propose a robust online adaptation algorithm: MEKF with Exponential Moving Average and Dynamic Multi-Epoch strategy (MEKF EMA-DME). The proposed algorithm outperforms existing methods as demonstrated in experiments. Keywords: Online adaptation, extended Kalman filter, exponential moving average, optimization 1. Introduction Supervised learning has been widely used to obtain models to predict the behaviors of intelligent agents Rudenko et al. (2019). Behavior prediction is a sub-topic of time series prediction Weigend (2018), which includes but is not limited to vehicle trajectory prediction during autonomous driving Lef evre et al. (2014) and human-motion prediction during human-robot collaboration Cheng et al. (2019).

--Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current embedding-based solutions treat entities independently and fail to take into account the interdependence between entities. In addition, most of embedding-based EA methods either fuse different features on representation-level and generate unified entity embedding for alignment, which potentially causes information loss, or aggregate features on outcome-level with hand-tuned weights, which is not practical with increasing numbers of features. T o tackle these deficiencies, we propose a collective embedding-based EA framework with adaptive feature fusion mechanism. We first employ three representative features, i.e., structural, semantic and string signals, for capturing different aspects of the similarity between entities in heterogeneous KGs. These features are then integrated at outcome-level, with dynamically assigned weights generated by our carefully devised adaptive feature fusion strategy. Eventually, in order to make collective EA decisions, we formulate EA as the classical stable matching problem between entities to be aligned, with preference lists constructed using fused feature matrix. It is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and monolingual EA benchmarks against state-of- the-art solutions, and the empirical results verify its effectiveness and superiority. We also perform ablation study to gain insights into framework modules. I NTRODUCTION Knowledge graph (KG) is playing an increasingly more important role in intelligent information services, e.g., information retrieval [27], automatic question answering [14] and recommendation systems [3]. Despite that a large number of KGs have been constructed over recent years, none of them can reach full coverage . These KGs, however, usually contain complementary contents, making it compelling to study the integration of heterogeneous KGs. To incorporate the knowledge from target KGs into the source KG, an indispensable step would be entity alignment (EA). EA aims to discover entities that have the same meaning but locate in different KGs.

The ubiquity of embodied gameplay, observed in a wide variety of animal species including turtles and ravens, has led researchers to question what advantages play provides to the animals engaged in it. Mounting evidence suggests that play is critical in developing the neural flexibility for creative problem solving, socialization, and can improve the plasticity of the medial prefrontal cortex. Comparatively little is known regarding the impact of gameplay upon embodied artificial agents. While recent work has produced artificial agents proficient in abstract games, the environments these agents act within are far removed the real world and thus these agents provide little insight into the advantages of embodied play. Hiding games have arisen in multiple cultures and species, and provide a rich ground for studying the impact of embodied gameplay on representation learning in the context of perspective taking, secret keeping, and false belief understanding. Here we are the first to show that embodied adversarial reinforcement learning agents playing cache, a variant of hide-and-seek, in a high fidelity, interactive, environment, learn representations of their observations encoding information such as occlusion, object permanence, free space, and containment; on par with representations learnt by the most popular modern paradigm for visual representation learning which requires large datasets independently labeled for each new task. Our representations are enhanced by intent and memory, through interaction and play, moving closer to biologically motivated learning strategies. These results serve as a model for studying how facets of vision and perspective taking develop through play, provide an experimental framework for assessing what is learned by artificial agents, and suggest that representation learning should move from static datasets and towards experiential, interactive, learning.

Data preparation is a big part of applied machine learning. Correctly preparing your training data can mean the difference between mediocre and extraordinary results, even with very simple linear algorithms. Performing data preparation operations, such as scaling, is relatively straightforward for input variables and has been made routine in Python via the Pipeline scikit-learn class. On regression predictive modeling problems where a numerical value must be predicted, it can also be critical to scale and perform other data transformations on the target variable. This can be achieved in Python using the TransformedTargetRegressor class.

Data Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. For beginners, it's only natural to raise the following question: What skills do I need to become a data scientist? This article will discuss 10 essential skills that are necessary for practicing data scientists. These skills could be grouped into 2 categories, namely, technological skills (Math & Statistics, Coding Skills, Data Wrangling & Preprocessing Skills, Data Visualization Skills, Machine Learning Skills,and Real World Project Skills) and soft skills (Communication Skills, Lifelong Learning Skills, Team Player Skills and Ethical Skills). Data science is a field that is ever-evolving, however mastering the foundations of data science will provide you with the necessary background that you need to pursue advance concepts such as deep learning, artificial intelligence, etc.

Sorry this is not related to the paper itself, but I'm new in research and I've been wondering how papers with so many authors even come together? Its kind of mind blowing to me how an idea gets worked on by so many people? Would something like this be initiated by the graduate student or the professor or someone else?

Machine learning algorithms have hyperparameters that allow you to tailor the behavior of the algorithm to your specific dataset. Hyperparameters are different from parameters, which are the internal coefficients or weights for a model found by the learning algorithm. Unlike parameters, hyperparameters are specified by the practitioner when configuring the model. Typically, it is challenging to know what values to use for the hyperparameters of a given algorithm on a given dataset, therefore it is common to use random or grid search strategies for different hyperparameter values. The more hyperparameters of an algorithm that you need to tune, the slower the tuning process.

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order proximity/adjacency matrix of the graph via computationally expensive matrix factorization techniques. These approaches typically require significant resources for the learning process and rely on multiple parameters, which limits their applicability in practice. Moreover, most of the existing graph embedding techniques operate effectively in one specific metric space only (e.g., the one produced with cosine similarity), do not preserve higher-order structural features of the input graph and cannot automatically determine a meaningful number of embedding dimensions. Typically, the produced embeddings are not easily interpretable, which complicates further analyses and limits their applicability. To address these issues, we propose DAOR, a highly efficient and parameter-free graph embedding technique producing metric space-robust, compact and interpretable embeddings without any manual tuning. Compared to a dozen state-of-the-art graph embedding algorithms, DAOR yields competitive results on both node classification (which benefits form high-order proximity) and link prediction (which relies on low-order proximity mostly). Unlike existing techniques, however, DAOR does not require any parameter tuning and improves the embeddings generation speed by several orders of magnitude. Our approach has hence the ambition to greatly simplify and speed up data analysis tasks involving graph representation learning.