There are words with multiple clusters (Figure 1b).

When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2 dimensional view of the data. We describe ENS-t-SNE: an algorithm for Embedding Neighborhoods Simultaneously that generalizes the t-Stochastic Neighborhood Embedding approach. By using different viewpoints in ENS-t-SNE's 3D embedding, one can visualize different types of clusters within the same high-dimensional dataset. This enables the viewer to see and keep track of the different types of clusters, which is harder to do when providing multiple 2D embeddings, where corresponding points cannot be easily identified. We illustrate the utility of ENS-t-SNE with real-world applications and provide an extensive quantitative evaluation with datasets of different types and sizes.

Data drift in unstructured data like images is complicated to measure. The metrics typically used for drift in structured data -- such as population stability index (PSI), Kullback-Leibler divergence (KL divergence), and Jensen-Shannon divergence (JS divergence) -- allow for statistical analysis on structured labels, but do not extend to unstructured data. The general challenge with measuring unstructured data drift is that you need to understand the change in relationships inside the unstructured data itself. In short, you need to understand the data in a deeper way before you can understand drift. The goal of unstructured drift is to detect whether two unstructured datasets are different -- and, if so, to give workflows to understand why the datasets are different.

I've always had a passion for learning and consider myself a lifelong learner. Being at SAS, as a data scientist, allows me to learn and try out new algorithms and functionalities that we regularly release to our customers. Often times, the algorithms are not technically new, but they're new to me which makes it a lot of fun. Recently, I had the opportunity to learn more about t-Distributed Stochastic Neighbor Embedding (t-SNE). In this post I'm going to give a high-level overview of the t-SNE algorithm.

Data visualisation helps understanding data represented by multiple variables, also called features, stored in a large matrix where individuals are stored in lines and variable values in columns. These data structures are frequently called multidimensional spaces.In this paper, we illustrate ways of employing the visual results of multidimensional projection algorithms to understand and fine-tune the parameters of their mathematical framework. Some of the common mathematical common to these approaches are Laplacian matrices, Euclidian distance, Cosine distance, and statistical methods such as Kullback-Leibler divergence, employed to fit probability distributions and reduce dimensions. Two of the relevant algorithms in the data visualisation field are t-distributed stochastic neighbourhood embedding (t-SNE) and Least-Square Projection (LSP). These algorithms can be used to understand several ranges of mathematical functions including their impact on datasets. In this article, mathematical parameters of underlying techniques such as Principal Component Analysis (PCA) behind t-SNE and mesh reconstruction methods behind LSP are adjusted to reflect the properties afforded by the mathematical formulation. The results, supported by illustrative methods of the processes of LSP and t-SNE, are meant to inspire students in understanding the mathematics behind such methods, in order to apply them in effective data analysis tasks in multiple applications.

In the road so far, we have talked about MLP, CNN, and RNN architectures. These are discriminative models, that is models that can make predictions. Discriminative models essentially learn to estimate a conditional probability distribution p( x); that is, given a value, they try to predict the outcome based on what they learned about the probability distribution of x. Generative models are architectures of neural networks that learn the probability distribution of the data and learn how to generate data that seems to come from that probability distribution. Creating synthetic data is one use of generative models, but is not the only one.

The problem of finding K-nearest neighbors in the given dataset for a given query point has been worked upon since several years. In very high dimensional spaces the K-nearest neighbor search (KNNS) suffers in terms of complexity in computation of high dimensional distances. With the issue of curse of dimensionality, it gets quite tedious to reliably bank on the results of variety approximate nearest neighbor search approaches. In this paper, we survey some novel K-Nearest Neighbor Search approaches that tackles the problem of Search from the perspectives of computations, the accuracy of approximated results and leveraging parallelism to speed-up computations. We attempt to derive a relationship between the true positive and false points for a given KNNS approach. Finally, in order to evaluate the robustness of a KNNS approach against adversarial points, we propose a generic Reinforcement Learning based framework for the same.

This is a big question, and I'm not a particularly big person. As such, these are all likely to be obvious observations to someone deep in the literature and theory. What I find however is that there are a base of unspoken intuitions that underlie expert understanding of a field, that are never directly stated in the literature, because they can't be easily proved with the rigor that the literature demands. And as a result, the insights exist only in conversation and subtext, which make them inaccessible to the casual reader. Because I have no need of rigor to post on the internet, (or even a need to be correct) I'm going to post some of those intuitions here as I understand them.

Tensor networks are factorisations of high rank tensors into networks of lower rank tensors and have primarily been used to analyse quantum many-body problems. Tensor networks have seen a recent surge of interest in relation to supervised learning tasks with a focus on image classification. In this work, we improve upon the matrix product state (MPS) tensor networks that can operate on one-dimensional vectors to be useful for working with 2D and 3D medical images. We treat small image regions as orderless, squeeze their spatial information into feature dimensions and then perform MPS operations on these locally orderless regions. These local representations are then aggregated in a hierarchical manner to retain global structure. The proposed locally orderless tensor network (LoTeNet) is compared with relevant methods on three datasets. The architecture of LoTeNet is fixed in all experiments and we show it requires lesser computational resources to attain performance on par or superior to the compared methods.

"Life requires Movement" -- (Aristotle, 4th century BC) The more life there is, the more flexibility there is. The more fluid you are, the more you are alive." "Life is movement, movement is change" -- beginning of a quote by Neale Donald Walsch "If life boils down to one thing, it's movement. To live is to keep moving" -- Jerry Seinfeld As many well known people have told us throughout history, life is movement. Life is changing your state in a proactive way, going from A to B. Life is also an expensive process and that makes the process of going from A to B a delicate, fascinating process that needs to be optimized. And that's where we begin this article. And although we will focus throughout the following sections on deep learning and A.I, we will be touching simultaneously on universal themes and principles that go to the core of what means being alive. In a process that depends on a very large number of parameters, which makes it multidimensional. Which makes it hard to visualize for beings that operate in only 3 dimensions (4 with time). Which is the whole point of this article. So let's begin this ride where it all begins, with movement. Say we want to go from A to B in regards to some objective. Some of these challenges will take minutes, other hours, others days and some of them years. Some of them depend on a moderate number of factors, others depend on a massive number of them. We want to optimize these and infinite other challenges and the objective is always going from A to B. You may also combine many challenges and see life itself as a massive fractal made of optimization processes at different scales. Going from A to B could be tackled in different ways. We could do it very systematically, trying lots of possibilities. Or we could try to find the most efficient way to get there as soon as possible.