In this work, we propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods, particularly in the deep Q-networks where the overestimation is exaggerated by function approximation errors. Our algorithm builds on double Q-learning, by maintaining a set of parallel models and estimate the Q-value based on a randomly selected network, which leads to reduced overestimation bias as well as the variance. We provide empirical evidence on the advantages of our method by evaluating on some benchmark environment, the experimental results demonstrate significant improvement of performance in reducing the overestimation bias and stabilizing the training, further leading to better derived policies.

At this point, computer vision is the hottest research field within deep learning. It fits in many academic subjects such as Computer science, Mathematics, Engineering, Biology, and psychology. Computer vision represents a relative understanding of visual environments. Therefore, due to its cross-domain mastery, many scientists believe the field paves the way towards Artificial General Intelligence. Recent developments in neural networks and deep learning approaches have immensely advanced the performance of state-of-the-art visual recognition systems. Let's look at what are the five primary computer vision techniques.

So Focal Loss reduces the loss contribution from easy examples and increases the importance of correcting misclassified examples.) So, let's first understand what Cross-Entropy loss for binary classification. The idea behind Cross-Entropy loss is to penalize the wrong predictions more than to reward the right predictions.

In last month's column, I tackled the subject of machine learning (ML) in Artificial intelligence: machine learning and I suppose one obvious question would be "What's the difference between machine and deep learning?" Well, both terms are subsets of artificial intelligence (AI), although deep learning is a subset of machine learning. In the meantime, let's touch upon machine learning so as to re-establish what we have already understood and, that is, ML is a broad field of study, where the development of software (a computer program) automatically improves through experience, based on the data that it has received. We nowadays associate the term with "big data," "data modelling" or "data science," where retailers, for example, like to collect information about your shopping habits so, in turn, they can more accurately target their advertising. And then there are the likes of Amazon and Netflix, who use data analytics to predict or suggest what you might be interested in viewing or purchasing next.

Image matting plays a key role in image and video editing and composition. Although existing deep learning approaches can produce acceptable image matting results, their performance suffers in real-world applications, where the input images are mostly high resolution. To address this, a group of researchers from UIUC, Adobe Research and the University of Oregon have proposed HDMatt, the first deep learning-based image matting approach for high-resolution image inputs. Generally, deep learning approaches take an entire input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such methods however may fail when dealing with high-resolution input images in sizes of 5000 5000 pixels or higher due to hardware limitations. The researchers designed HDMatt to crop an input image and trimap into patches, then estimate the alpha values of each patch.

This article was originally published here Cancer Discov. ABSTRACT Real-world evidence (RWE) – conclusions derived from analysis of patients not treated in clinical trials – is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE may be facilitated through machine learning techniques to integrate and interrogate large and otherwise underutilized data sets. In cancer research, an ongoing challenge for RWE is the lack of reliable, reproducible, scalable assessment of treatment-specific outcomes. We hypothesized a deep learning model could be trained to use radiology text reports to estimate gold-standard Response Evaluation Criteria in Solid Tumors (RECIST)-defined outcomes.

One of the most favourite languages amongst the developers, Python is well-known for its abundance of tools and libraries available for the community. The language also provides several computer vision libraries and frameworks for developers to help them automate tasks, which includes detections and visualisations. Below here, we are listing down 10 best Python libraries that developers can use for Computer Vision. It also provides researchers with low-level components that can be mixed and matched to build new approaches. IPSDK is an image processing library in C and Python.

Transformers have now become the defacto standard for NLP tasks. Originally developed for sequence transduction processes such as speech recognition, translation, and text to speech, transformers work by using convolutional neural networks together with attention models, making them much more efficient than previous architectures. And although transformers were developed for NLP, they've also been implemented in the fields of computer vision and music generation. However, for all their wide and varied uses, transformers are still very difficult to understand, which is why I wrote a detailed post describing how they work on a basic level. It covers the encoder and decoder architecture, and the whole dataflow through the different pieces of the neural network.

To stay competitive and relevant in their industries, enterprises increasingly need to become AI-driven. AI is a new key to improving business processes, making better decisions, monetizing data, increasing security and more. The growing importance of AI in the enterprise is a point that industry observers now emphasize. Just consider this view from the global consulting firm Deloitte: "As AI technologies standardize across industries, becoming an AI-fueled organization will likely be table stakes for survival. And that means rethinking the way humans and machines interact within working environments."1

The Fourth Industrial Revolution just took a huge step forward, thanks to a breakthrough artificial intelligence (AI) model that can learn virtually anything about the world -- and produce the content to tell us about it. The AI program is GPT-3 by OpenAI, which started out as a language model to predict the next word in a sentence and has vastly exceeded that capability. Now, drawing from voluminous data -- essentially all of Wikipedia, links from Reddit, and other Internet content -- GPT-3 has shown it can also compose text that is virtually indistinguishable from human-generated content. Asger Alstrup Palm, Area9's chief technology officer, explained that GPT-3 was tasked with testing the "scaling hypothesis" -- to see if a bigger model with ever-increasing amounts of information would lead to better performance. Although it's too early to call the scaling hypothesis proven, there are some strong indications that this is, indeed, the case. Further validating the potential of GPT-3, Microsoft recently announced it will exclusively license the model from OpenAI, with the intention of developing and delivering AI solutions for customers and creating new solutions using natural language generation.