This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions. We prove that dropout can suppress the strength of interactions between input variables of deep neural networks (DNNs). The theoretic proof is also verified by various experiments. Furthermore, we find that such interactions were strongly related to the over-fitting problem in deep learning. Thus, the utility of dropout can be regarded as decreasing interactions to alleviate the significance of over-fitting. Based on this understanding, we propose an interaction loss to further improve the utility of dropout. Experimental results have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.

Fast and accurate, that is what most sports are about. Although when computer vision has been around for many years, not many people in sports seem to be aware of its values, feasibility and applications to the real stadium and the world. Computer Vision (CV) is a subfield of artificial intelligence and machine learning that develops techniques to train computers to interpret and understand the contents inside images. Computer Vision aims to replicate parts of the complexities in the human visual system and visual perception by applying deep learning models to accurately detect and classify objects from the dynamic and varying physical world. Many types of sports are often multidimensional systems that incorporate a plethora of data points that make one team or athlete better than the other.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. It's not hard to tell that the image below shows three different things: a bird, a dog, and a horse. This example portrays one of the dangerous characteristics of machine learning models, which can be exploited to force them into misclassifying data. This is an example of data poisoning, a special type of adversarial attack, a series of techniques that target the behavior of machine learning and deep learning models. If applied successfully, data poisoning can provide malicious actors backdoor access to machine learning models and enable them to bypass systems controlled by artificial intelligence algorithms.

Deep learning is a subset of machine learning, which both fall under the artificial intelligence (AI) and internet of things (IoT) umbrellas. Without deep learning applications, though, automation and intelligence would not be where they are today. For instance, AI in manufacturing has come a long way with tech like predictive maintenance. Here are 10 examples of the deep learning revolution. One of the most common deep learning applications is with digital assistants.

As a subset of artificial intelligence, deep learning lies at the heart of various innovations: self-driving cars, natural language processing, image recognition and so on. Companies that deliver DL solutions (such as Amazon, Tesla, Salesforce) are at the forefront of stock markets and attract impressive investments. According to Statista, the total funding of artificial intelligence startup companies worldwide in 2014–2019 is equal to more than $26 billion. This high interest can be explained by the amazing benefits of deep learning and its architectures -- artificial neural networks. Deep learning is one of the subsets of machine learning that uses deep learning algorithms to implicitly come up with important conclusions based on input data.

In this post, I am going to write about the general blueprint to be followed for any deep learning model. Here I am not going in-depth into deep learning concepts but this acts as a basic step that can be followed to develop neural networks. Some steps may be added or can be removed from the below list based on the requirement. The data we get for modeling is most of the time unstructured and raw, where we have lots of data that is not required for our case. The first step comes in modeling a neural network is weight initialization and this is an extremely important step because if the weights are not initialized properly then converging to minima is impossible, but if done is the right way then optimization is achieved in the least time.

To improve the robustness of deep learning–based glioblastoma segmentation in a clinical setting with sparsified datasets. In this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted MRI from 117 patients (median age, 64 years; interquartile range [IQR], 55–73 years; 76 men) included within the Multimodal Brain Tumor Image Segmentation (BraTS) dataset plus a clinical dataset (2012–2013) with similar imaging modalities of 634 patients (median age, 59 years; IQR, 49–69 years; 382 men) with glioblastoma from six hospitals were used. Expert tumor delineations on the postcontrast images were available, but for various clinical datasets, one or more sequences were missing. The convolutional neural network, DeepMedic, was trained on combinations of complete and incomplete data with and without site-specific data. Sparsified training was introduced, which randomly simulated missing sequences during training. The effects of sparsified training and center-specific training were tested using Wilcoxon signed rank tests for paired measurements. A model trained exclusively on BraTS data reached a median Dice score of 0.81 for segmentation on BraTS test data but only 0.49 on the clinical data.

OpenAI's GPT-3 has been in the news since its launch last month owing to the exciting features and largest language model trained in the current era. OpenAI announced this deep-learning model for natural language processing with over 175 billion parameters and set a benchmark for surpassing high-performance meeting NLP benchmarks. Generative Pretrained Transformer-3 is the third generation of OpenAI's machine learning model algorithms for a straightforward interpretation of voice, text, answering various questions by analyzing data and giving accurate output. With exceptional language abilities, GPT-3 is pre-trained with a vast amount of 45TB text and more than 499 billion words, resulting in 175 billion parameters. GPT-3 is also seeing a major future in mobile application development as well.

Deep learning has already achieved remarkable results in many fields. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You'll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine--an example that represents one of science's greatest challenges.

Work by Wyss Core Faculty member Peng Yin in collaboration with Collins and others has demonstrated that different toehold switches can be combined to compute the presence of multiple "triggers," similar to a computer's logic board. Credit: Wyss Institute at Harvard University DNA and RNA have been compared to "instruction manuals" containing the information needed for living "machines" to operate. But while electronic machines like computers and robots are designed from the ground up to serve a specific purpose, biological organisms are governed by a much messier, more complex set of functions that lack the predictability of binary code. Inventing new solutions to biological problems requires teasing apart seemingly intractable variables -- a task that is daunting to even the most intrepid human brains. Two teams of scientists from the Wyss Institute at Harvard University and the Massachusetts Institute of Technology have devised pathways around this roadblock by going beyond human brains; they developed a set of machine learning algorithms that can analyze reams of RNA-based "toehold" sequences and predict which ones will be most effective at sensing and responding to a desired target sequence.