As you can see in these images, there is a big difference between the synthesized old images and the real old ones. You can see that the synthesized image is already in high definition even with the fake scratches and color changes compared to the other one that contains way fewer details. They addressed this issue by creating their own new network specifically for the task. Basically, they used two variational auto-encoders, also called VAEs, to respectively transform old (degraded) and clean (restored) photos into two latent space. This translation into latent spaces is learned through synthetic paired data but is able to generalize well on real photos since this same domain gap is way smaller on such compact latent spaces. The domain gap from the two latent spaces produced by the VAEs is closed by jointly training an adversarial discriminator.

The graph represents a network of 1,076 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 02 October 2020 at 10:43 UTC. The requested start date was Friday, 02 October 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 9-hour, 4-minute period from Tuesday, 29 September 2020 at 14:35 UTC to Thursday, 01 October 2020 at 23:40 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Transformers have become the defacto standard for NLP tasks nowadays. While the Transformer architecture was introduced with NLP, they are now being used in Computer Vision and to generate music as well. I am sure you would all have heard about the GPT3 Transformer and its applications thereof. But all these things aside, they are still hard to understand as ever. It has taken me multiple readings through the Google research paper that first introduced transformers along with just so many blog posts to really understand how a transformer works. So, I thought of putting the whole idea down in as simple words as possible and with some very basic Math and some puns as I am a proponent of having some fun while learning. I will try to keep both the jargon and the technicality to a minimum, yet it is such a topic that I could only do so much. And my goal is to make the reader understand even the most gory details of Transformer by the end of this post. Also, this is officially my longest post both in terms of time taken to write it as well as length of the post. So, here goes -- This post will be a highly conversational one and it is about "Decoding The Transformer".

Sponsored Artificial intelligence (AI) started out as laboratory research, but today's AI techniques like machine learning and deep learning are increasingly finding their way into real-world applications such as detecting fraudulent activity in financial transactions, analysing retail data to deliver a personalised shopping experience, or finding the optimal route for delivery vehicles to take. These trends mean that AI is rapidly becoming an integral part of many enterprise workflows, from email to CRM or ERP, and especially data analytics to glean business intelligence insights from an organisation's own datasets on how to drive efficiencies or even create new business opportunities. Not surprisingly, a recent IDC survey showed that 85 per cent of organisations are evaluating AI or already using it in production. However, IDC found that most organisations reported experiencing failures among their AI projects, with a quarter reporting a failure rate of up to a 50 per cent. These failures have been attributed to a lack of skilled staff or cultural challenges, but another common cause of failure is the inability to scale from a proof-of-concept project to running in a production capacity that can serve the entire organisation.

TEACH ME AND I REMEMBER. I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Life is a journey through learning experiences.

For me, the new storytelling approaches are indeed the most amazing part of the story. I want to share with you the narrative capabilities of GPT-3 as a writer, and the creative experiments we did with it so far. But first, let's speak about Tokens. Because they determine the quantitative limits of GPT-3 outputs. You cannot write coherent novels using this NLP framework.

An efficient traffic signal control system (TSCS) should not only be reactive to the current traffic but also be predictive by anticipating future traffic disturbances. In this study, we investigate the potential of using convolution neural network (CNN) in detecting emergency cases and forecasting events that can interrupt the traffic flow. Case-based reasoning (CBR) is then exploited to react to detected and forecasted events. We further develop an adapted Reinforcement Leaning (RL) algorithm in building and enhancing the case bases. The proposed system inherits the advantages of CNN, CBR, and RL, which allow detection, prediction, control, evaluation, and learning in a unified framework.

The University of Helsinki (https://www.helsinki.fi/en) is an international scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities in Europe and ranks among the top 100 international universities in the world. The research themes of the Department cover machine learning and algorithms, computer networks and distributed systems, software systems and bioinformatics. A fixed-term postdoctoral researcher position is available in the research group of Professor Aapo Hyvärinen at the Department of Computer Science, University of Helsinki. The project is broadly defined as unsupervised deep learning, including frameworks such as nonlinear independent component analysis, energy-based modelling, and causal discovery, which are at the cutting edge of unsupervised deep learning.

Hi! I am working on a CNN project and I have some confusions. I didn't understand the importance of using bias during training a model. What are the effects of bias usage? And if we don't use bias what are the challenges that we are going to face with?

The buzz around Deep Learning often misleads layman people to think that it is a newly invented technology, but it comes as a shock for them when they know that foundations of Deep Learning were laid down as early as in the 1940–1950s. There is a long history of deep learning where most of the popular deep neural network architectures and theories were already proposed throughout the latter half of the 20th century. If it was such the case, then you may ask why the Deep Learning revolution is taking place in the current times and why not a few decades back. The short answer is that the right hardware and compute power, required to train the large neural networks efficiently, did not exist during those times; thus all the theories were mostly on papers without practical support. There was a time when if you were researching on neural networks, you would not have been taken seriously by the machine learning research community.