For an extremely prolonged stretch of time, women working in the fields of science, innovation, engineering and math are doing wonders. Take for instance the tale of Katherine Johnson and her partners, who made noteworthy commitments to the early years of NASA's space program. The world had not in any case known about her name until two years back, when the film, Hidden Figures, hit the screens. Women exceed expectations at communication, sustaining a positive aura in the group, critical thinking, problem-solving among an entire host of other things! Let's have a look at the ladies who are doing everything and motivating us to be a superior version of ourselves each and every day.

Cancer is one of the deadliest diseases faced by mankind since ancient times. If the tumour is detected in early stages, the chances of survival increase drastically. Deep Learning (CNN) has transformed computer vision including diagnosis on medical images. In this post we will harness the power of CNNs to detect and segment tumours from Brain MRI images. See example of Brain MRI image with tumour below and the result of segmentation on it.

Judging a book by its cover is not the way to go.. but I guess a summary should do just fine. In a world where internet is getting exploded with a hulking amount of data every day, being able to automatically summarize is an important challenge. Summaries of long documents, news articles, or even conversations can help us consume content faster and more efficiently. Automatic Text Summarization is a growing field in NLP and has been getting a lot of attention in the last few years. I will not be discussing specific details of any algorithm or implementation.

In this blog post we introduce Population Based Augmentation (PBA), an algorithm that quickly and efficiently learns a state-of-the-art approach to augmenting data for neural network training. PBA matches the previous best result on CIFAR and SVHN but uses one thousand times less compute, enabling researchers and practitioners to effectively learn new augmentation policies using a single workstation GPU. You can use PBA broadly to improve deep learning performance on image recognition tasks. We discuss the PBA results from our recent paper and then show how to easily run PBA for yourself on a new data set in the Tune framework. Recent advances in deep learning models have been largely attributed to the quantity and diversity of data gathered in recent years.

Deep Q Networks are the deep learning/neural network versions of Q-Learning. With DQNs, instead of a Q Table to look up values, you have a model that you inference (make predictions from), and rather than updating the Q table, you fit (train) your model. The DQN neural network model is a regression model, which typically will output values for each of our possible actions. These values will be continuous float values, and they are directly our Q values. As we enage in the environment, we will do a .predict() to figure out our next move (or move randomly).

Then I'll explain the internal mechanisms that allow LSTM's and GRU's to perform so well. If you want to understand what's happening under the hood for these two networks, then this post is for you. You can also watch the video version of this post on youtube if you prefer. Recurrent Neural Networks suffer from short-term memory. If a sequence is long enough, they'll have a hard time carrying information from earlier time steps to later ones. So if you are trying to process a paragraph of text to do predictions, RNN's may leave out important information from the beginning.

This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations. Though these methods have been shown quite effective, they lack good explanations, which are critical to recommender systems. In this paper, we take a different path and propose generating recommendations by finding meaningful paths from users to items. Specifically, we formulate the problem as a sequential decision process, where the target user is defined as the initial state, and the walks on the graphs are defined as actions. We shape the rewards according to existing state-of-the-art methods and then train a policy function with policy gradient methods. Experimental results on three real-world datasets show that our proposed method not only provides effective recommendations but also offers good explanations .

Diagnosis of fungal infections can rely on microscopic examination, however, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted treatment is grave in consequences as the mortality rate for immunosuppressed patients is high. In this paper, we apply machine learning approach based on deep learning and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days and reducing the cost of the diagnostic examination.

A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions. When the likelihood ratio is unknown, it can be estimated by training a probabilistic classifier to distinguish samples from the two distributions. In this paper, we employ this likelihood-free importance weighting framework to correct for the bias in state-of-the-art deep generative models. We find that this technique consistently improves standard goodness-of-fit metrics for evaluating the sample quality of state-of-the-art generative models, suggesting reduced bias. Finally, we demonstrate its utility on representative applications in a) data augmentation for classification using generative adversarial networks, and b) model-based policy evaluation using off-policy data.

We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of 12 quantum mechanical properties of 119,487 organic molecules with up to 14 heavy atoms, sampled from the GDB MedChem database. The Alchemy dataset expands the volume and diversity of existing molecular datasets. Our extensive benchmarks of the state-of-the-art graph neural network models on Alchemy clearly manifest the usefulness of new data in validating and developing machine learning models for chemistry and material science. We further launch a contest to attract attentions from researchers in the related fields. More details can be found on the contest website \footnote{https://alchemy.tencent.com}. At the time of benchamrking experiment, we have generated 119,487 molecules in our Alchemy dataset. More molecular samples are generated since then. Hence, we provide a list of molecules used in the reported benchmarks.