Company's state-of-the-art architecture identifies unique concepts within text-based communications, and analyzes the sentiment of each concept Luminoso, the company that automatically turns unstructured text data into business-critical insights, unveiled its new deep learning model for analyzing sentiment of multiple concepts within the same text-based document. "While sentiment analysis has been prevalent for well over a decade, the most common form of sentiment analysis today involves evaluating whether a document's sentiment is overall more positive than negative," said Adam Carte, CEO of Luminoso. "This type of analysis is overly-simplistic, as it fails to address nuanced comments such as customers explaining what they like and dislike about a product, or employee feedback about a company's strengths and weaknesses. With Concept-Level Sentiment in Luminoso Daylight, businesses across industries will be able to upload any text-based document, and quickly receive a nuanced analysis of the author's sentiment regarding the topics they wrote about." Luminoso's new deep learning model understands documents using multiple layers of attention, a mechanism that identifies which words are relevant to get context around a specific concept as expressed by a word or phrase.

Horses don't normally wear hats, and deep generative models, or GANs, don't normally follow rules laid out by human programmers. But a new tool developed at MIT lets anyone go into a GAN and tell the model, like a coder, to put hats on the heads of the horses it draws. In a new study appearing at the European Conference on Computer Vision this month, researchers show that the deep layers of neural networks can be edited, like so many lines of code, to generate surprising images no one has seen before. "GANs are incredible artists, but they're confined to imitating the data they see," says the study's lead author, David Bau, a PhD student at MIT. "If we can rewrite the rules of a GAN directly, the only limit is human imagination." Generative adversarial networks, or GANs, pit two neural networks against each other to create hyper-realistic images and sounds.

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This blog post summarises a complete lecture on deep learning with many links to further reading.

In the last part, we started an introductory discussion on the present state of Deep Learning in histopathology. In the last part, we started an introductory discussion on the present state of Deep Learning in histopathology, we discussed Histopathology, Digital Histopathology, the possibilities of Machine Learning in the area, the various applications, followed by a detailed discussion of the challenges involved in working with Digital Microscopic Slide Images and in the application of Deep Learning Algorithms to them. In this blog, we shall be discussing in greater detail the applicability of Deep Learning to Histopathology from a methodological perspective along with the tasks it helps accomplish using relevant work for illustration. The applicability of deep learning can be studied in terms of the tasks it performs or in terms of the learning paradigm, which is the classification we shall be using in this writeup. The different learning algorithms, viz a viz Deep Learning for histopathology, along with the tasks are visualized in the following overview.

These attributes claim the researchers, make EagerPy easier and safer to work with than the underlying framework-specific APIs. Despite these changes and improvements, the team behind EagerPy made sure that the EagerPy API follows the standards set by NumPy, PyTorch, and JAX.

TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop. Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks.

Quantum Language Models (QLMs) in which words are modelled as quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability. Nevertheless, in the current literature word sequences are basically modelled as a classical mixture of word states, which cannot fully exploit the potential of a quantum probabilistic description. A full quantum model is yet to be developed to explicitly capture the non-classical correlations within the word sequences. We propose a neural network model with a novel Entanglement Embedding (EE) module, whose function is to transform the word sequences into entangled pure states of many-body quantum systems. Strong quantum entanglement, which is the central concept of quantum information and an indication of parallelized correlations among the words, is observed within the word sequences. Numerical experiments show that the proposed QLM with EE (QLM-EE) achieves superior performance compared with the classical deep neural network models and other QLMs on Question Answering (QA) datasets. In addition, the post-hoc interpretability of the model can be improved by quantizing the degree of entanglement among the words.

We address the problem of counterfactual regression using causal inference (CI) in observational studies consisting of high dimensional covariates and high cardinality treatments. Confounding bias, which leads to inaccurate treatment effect estimation, is attributed to covariates that affect both treatments and outcome. The presence of high-dimensional co-variates exacerbates the impact of bias as it is harder to isolate and measure the impact of these confounders. In the presence of high-cardinality treatment variables, CI is rendered ill-posed due to the increase in the number of counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural network (DNN) based framework for estimating causal effects in the presence of large number of covariates, and high-cardinal and continuous treatment variables. The proposed architecture comprises of a decorrelation network and an outcome prediction network. In the decorrelation network, we learn a data representation in lower dimensions as compared to the original covariates and addresses confounding bias alongside. Subsequently, in the outcome prediction network, we learn an embedding of high-cardinality and continuous treatments, jointly with the data representation. We demonstrate the efficacy of causal effect prediction of the proposed Hi-CI network using synthetic and real-world NEWS datasets.