We propose a framework for deep ordinal regression, based on unimodal output distribution and optimal transport loss. Despite being seemingly appropriate, in many recent works the unimodality requirement is either absent, or implemented using soft targets, which do not guarantee unimodal outputs at inference. In addition, we argue that the standard maximum likelihood objective is not suitable for ordinal regression problems, and that optimal transport is better suited for this task, as it naturally captures the order of the classes. Inspired by the well-known Proportional Odds model, we propose to modify its design by using an architectural mechanism which guarantees that the model output distribution will be unimodal. We empirically analyze the different components of our propose approach and demonstrate their contribution to the performance of the model. Experimental results on three real-world datasets demonstrate that our proposed approach performs on par with several recently proposed deep learning approaches for deep ordinal regression with unimodal output probabilities, while having guarantee on the output unimodality. In addition, we demonstrate that the level of prediction uncertainty of the model correlates with its accuracy.

The problem of adversarial examples has shown that modern Neural Network (NN) models could be rather fragile. Among the more established techniques to solve the problem, one is to require the model to be {\it $\epsilon$-adversarially robust} (AR); that is, to require the model not to change predicted labels when any given input examples are perturbed within a certain range. However, it is observed that such methods would lead to standard performance degradation, i.e., the degradation on natural examples. In this work, we study the degradation through the regularization perspective. We identify quantities from generalization analysis of NNs; with the identified quantities we empirically find that AR is achieved by regularizing/biasing NNs towards less confident solutions by making the changes in the feature space (induced by changes in the instance space) of most layers smoother uniformly in all directions; so to a certain extent, it prevents sudden change in prediction w.r.t. perturbations. However, the end result of such smoothing concentrates samples around decision boundaries, resulting in less confident solutions, and leads to worse standard performance. Our studies suggest that one might consider ways that build AR into NNs in a gentler way to avoid the problematic regularization.

HIGHEST RATED, 4.7 (2,024 ratings), Created by Lazy Programmer Inc., English [Auto-generated], Italian [Auto-generated], 3 more This is one of the most exciting courses I've done and it really shows how fast and how far deep learning has come over the years. When I first started my deep learning series, I didn't ever consider that I'd make two courses on convolutional neural networks. I think what you'll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover. We're going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!) We're going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.

Prior to 2017, most renditions of neural network models were coded in a batch scripting style. As AI researchers and experienced software engineers became increasingly involved in research and design, we started to see a shift in the coding of models that reflected software engineering principles for reuse and design patterns. A design pattern implies that there is a "best practice" for constructing and coding a model that can be reapplied across a wide range of cases, such as image classification, object detection and tracking, facial recognition, image segmentation, super resolution and style transfer. The introduction of design patterns also helped advance convolutional neural networks (as well as other network architectures) by aiding other researchers in understanding and reproducing a model's architecture. A procedural style for reuse was one of the earliest versions of using design patterns for neural network models.

The initial set of layers for recurrent neural operations universally begins with LSTM, GRU and RNN. But with an increase in the complexity of the task, we should use more complex models. That said, before moving directly to different and relatively complex models like attention or transformers, we should first ask a simple question -- can we still do something quickly with the basic recurrent layers? In this article, I will focus on the same philosophy that -- we should first exhaust the simple solutions before going for the more complex ones. In the next sections, we will explore the same old set of recurrent layers (albeit with some interesting arrangements) to get better inference on the data.

A new study has developed a deep learning approach that analyses protein interactions, which could improve the design of drugs in the future. Researchers have designed a novel approach to use deep learning to improve their understanding of how proteins interact in the body. According to the team, their findings could result in more accurate structure models of protein interactions in various diseases and better drug designs that target protein interactions. The study, conducted at Purdue University, US, designed a system called the docking decoy selection with voxel-based deep neural network (DOVE). This applies deep learning principles to virtual models of protein interactions.

AI legend Yann LeCun, one of the godfathers of deep learning, sees self-supervised learning as the ... [ ] key to AI's future. For the second part of this article series, see here. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating.

Strokes are life-threatening medical emergencies where urgent treatment is essential. They occur when part of the brain is cut off from its normal blood supply. The most common type of stroke (accounting for almost 85% of all cases) is an ischemic stroke, which is caused by a clot interrupting the supply of blood to the brain. Large vessel occlusion (LVO) strokes occur when such a blockage is found in one of the major arteries of the brain. As LVO strokes are more severe, they require immediate diagnosis and opening of the blocked artery as fast as possible. In clinical practice, the most common method used to detect LVOs is an imaging modality called CT angiography.

Python has been the primary language for most Deep/Machine Learning enthusiasts, but there are quite a few JavaScript libraries that bring the magic of ML directly to the browser. Brain.js is one of the most popular JavaScript ML libraries known for its simple and easy usage. The library greatly simplifies building and training Neural Networks to just a few lines of code eliminating much of the math and jargon needed to fully understand the theoretical aspects of the model. In addition, the library has pros and cons like any other that we will explore in the end. NOTE(s): I am much more of a Python developer and am still in the process of getting proficient with JavaScript, so feel free to point out any best practices coding wise.

This is where you'll improve your coding abilities, math understanding and start working on real data science problems. In this respect, I highly recommend taking what I consider is the best data science boot camp out there: Le Wagon. With a heavy emphasis on practical exercises and a final project in which you get to deploy your own machine learning model, this highly intensive boot camp will give you a big picture data science end to end: just enough theory, data wrangling, data viz, programming inside an IDE, Git, machine learning, deep learning, data engineering. Once you understand and have worked on the most important parts of data science, you'll have a better idea of what you like, what your strengths and weaknesses are, and where to head next. This is also an opportunity to build your network.