In this effort we consider a reinforcement learning (RL) technique for solving personalization tasks with complex reward signals. In particular, our approach is based on state space clustering with the use of a simplistic $k$-means algorithm as well as conventional choices of the network architectures and optimization algorithms. Numerical examples demonstrate the efficiency of different RL procedures and are used to illustrate that this technique accelerates the agent's ability to learn and does not restrict the agent's performance.

Abstract--Bi-type multi-relational heterogeneous graph (BMHG) is one of the most common graphs in practice, for example, academic networks, e-commerce user behavior graph and enterprise knowledge graph. It is a critical and challenge problem on how to learn the numerical representation for each node to characterize subtle structures. However, most previous studies treat all node relations in BMHG as the same class of relation without distinguishing the different characteristics between the intra-class relations and inter-class relations of the bi-typed nodes, causing the loss of significant structure information. To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism. Moreover, to sufficiently model node multi-relational information in BMHG, we adopt a newly proposed hierarchical mechanism.

In this paper, we propose self-supervised speaker representation learning strategies, which comprise of a bootstrap equilibrium speaker representation learning in the front-end and an uncertainty-aware probabilistic speaker embedding training in the back-end. In the front-end stage, we learn the speaker representations via the bootstrap training scheme with the uniformity regularization term. In the back-end stage, the probabilistic speaker embeddings are estimated by maximizing the mutual likelihood score between the speech samples belonging to the same speaker, which provide not only speaker representations but also data uncertainty. Experimental results show that the proposed bootstrap equilibrium training strategy can effectively help learn the speaker representations and outperforms the conventional methods based on contrastive learning. Also, we demonstrate that the integrated two-stage framework further improves the speaker verification performance on the VoxCeleb1 test set in terms of EER and MinDCF.

Tabular data are ubiquitous in real world applications. Although many commonly-used neural components (e.g., convolution) and extensible neural networks (e.g., ResNet) have been developed by the machine learning community, few of them were effective for tabular data and few designs were adequately tailored for tabular data structures. In this paper, we propose a novel and flexible neural component for tabular data, called Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction. Also, we design a structure re-parameterization method to compress AbstLay, thus reducing the computational complexity by a clear margin in the reference phase. A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks. In DANets, a special shortcut path is introduced to fetch information from raw tabular features, assisting feature interactions across different levels. Comprehensive experiments on seven real-world tabular datasets show that our AbstLay and DANets are effective for tabular data classification and regression, and the computational complexity is superior to competitive methods. Besides, we evaluate the performance gains of DANet as it goes deep, verifying the extendibility of our method. Our code is available at https://github.com/WhatAShot/DANet.

When we are programming Logistic Regression or Neural Networks we should avoid explicit \(for \) loops. It's not always possible, but when we can, we should use built-in functions or find some other ways to compute it. Vectorizing the implementation of Logistic Regression makes the code highly efficient. In this post we will see how we can use this technique to compute gradient descent without using even a single \(for \) loop. This code was non-vectorized and highly inefficent so we need to transform it.

In the age of Big Data in biology, data science and machine learning have flourished and benefitted from their interdisciplinary application to biology. As a graduate student in this discipline, I read a lot of papers to stay up to date on the literature (and still have a large reading list to catch up on!), and thought I would share what have been some of the best papers I've read this year. In about 80–90% of the single-cell papers you'll encounter, depending on the research question, there will be at least one or two tSNE or UMAP plots to visualize the data they've collected, usually single-cell RNA-sequencing (scRNA-seq) data, where individual cells are profiled for their RNA abundance across the genome. These unsupervised dimensionality reduction methods have been more or less accepted as the status quo for data visualization in the world of single-cell genomics, so it took Academic Twitter by storm this summer when a new preprint boldly challenged that norm, arguing that these methods do little to preserve the latent structure of the data it seeks to convey to our 3D minds. Using the extreme example of preserving equidistant cells in high-dimensional space, and later relaxing it to near-equidistance, they show how tSNE and UMAP distort the orientation of groups of cells with near-equidistance spacing in the original space, clustering them with groups of cells that are evenly spread further apart.

As the two first groups are more common in our dataset (and in the wild), we will treat them as majority groups. The last two groups, which are rarer, are the minority groups of the dataset.

Clustering is an important topic in busyness, because it helps us to reduce the number of features to some typology, to some clusters which, in a case that data allows us, can give us more informations about our topic of interest. In a data science literature it is usually presented as dimension reduction technique, but in science, or even in data science it could reveal some additional pattern in data that is not obvious at the first glance. Imagine you have some features about some students: their marks, their personality traits, their ability scores, their motivation. Clustering could reveal you the completely new types of (un)successful students (it could be someone with high ability and low motivation -- underachiever, but at the same time it could be someone with high motivation and really good marks, but low abilities -- overachiever). This could simply done by clustering, while our cluster names (overachiever, underachiever) are basically interpretations of the clusters.

What is PCA: Principal Component Analysis (PCA) is a dimensionality reduction technique that enables you to identify correlations and patterns in a data set so that it can be transferred into a data set of significantly lower dimensions without loss of any important information. Step 1: Standardization of the data Standardization is all about scaling your data in such a way that all the variables and values lie within a similar range. Step 2: Computing the covariance matrix A covariance matrix expresses the correlation between the different variables in the data set. It is essential to identify heavily dependent variables because they contain biased and redundant information which reduces the overall performance of the model. Eigenvectors and Eigenvalues are the mathematical constructs that must be computed from the covariance matrix in order to determine the Principal component of the data sets.

Multilayer Perceptrons (MLP), are complex algorithms that take a lot of compute power and a *ton* of data in order to produce satisfactory results in reasonable timeframes. Let's start with what they're not: neural networks, despite the name and every blog post and intro to machine learning text book you've probably read up till now, are not analogs of the human brain. There are some *very* surface-level similarities, but the actual functionality of a neural network has almost nothing in common with the neurons that make up the approximately three pounds of meat that sits between your ears and defines everything you do and how you experience reality. Just like a lot of other machine learning algorithms, they use the formula "label equals weight times data value plus offset" (or y w*x b) to define where they draw their lines/hyperplanes for making predictions. In machine learning, that slope is called a weight.)