Artificial intelligence researchers at North Carolina State University have improved the performance of deep neural networks by combining feature normalization and feature attention modules into a single module that they call attentive normalization (AN). The hybrid module improves the accuracy of the system significantly, while using negligible extra computational power. "Feature normalization is a crucial element of training deep neural networks, and feature attention is equally important for helping networks highlight which features learned from raw data are most important for accomplishing a given task," says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at NC State. "But they have mostly been treated separately. We found that combining them made them more efficient and effective."

The ship of technology is in the mid-way sailing between applications of AI lake big data, deep learning, machine learning, IoT, cloud computing, etc.

We will be looking at the history of neural networks. After thoroughly going through various sources, I found out that the history of neural networks piqued my interest, and I became engrossed. I had a lot of fun because researching this topic was gratifying. Below is the list of the table of contents. Feel free to skip to the topic which fascinates you the most.

Deep learning has found its way into all kinds of research areas in the present times and has also become an integral part of our lives. "Artificial Intelligence is the new electricity." However, with any great technical breakthroughs come a large number of challenges too. This article will help you solve one of these hurdles, which is optimization. We know that if we set the learning rate too small, the algorithm will take too much time to converge fully, and if it's too large, the algorithm will diverge instead of converging.

Spiking Neural Networks (SNN) has recently been a topic of interest in the field of Artificial Intelligence. The premise behind SNN is that neurons in the brain, unlike our current modelling of it, communicate with one another via spike trains that occur at different frequencies and timings. Another way of visualizing the workings of natural neural netwoks is to image a pond with waves interacting with one another forming variety of patterns. The crucial advantage of SNN is the ability to encode time in a more meaningful way by making use of relative timings of the spikes. New hardware and math solutions are being worked on in the research community to make SNN practical.

Floating-point formats are not the most glamorous or (frankly) the important consideration when working with deep learning models: if your model isn't working well, then your floating-point format certainly isn't going to save you! However, past a certain point of model complexity/model size/training time, your choice of floating-point format can have a significant impact on your model training times and even performance. Here's how the rest of this post is structured: It's more likely than you think! It's been known for quite some time that deep neural networks can tolerate lower numerical precision. High-precision calculations turn out not to be that useful in training or inferencing neural networks: the additional precision confers no benefit while being slower and less memory-efficient.

In this tutorial, you will create an automatic Sudoku puzzle solver using OpenCV, Deep Learning, and Optical Character Recognition (OCR). My wife is a huge Sudoku nerd. Every time we travel, whether it be a 45-minute flight from Philadelphia to Albany or a 6-hour transcontinental flight to California, she always has a Sudoku puzzle with her. The funny thing is, she prefers the printed Sudoku puzzle books. She hates the digital/smartphone app versions and refuses to play them. I'm not a big puzzle person myself, but one time, we were sitting on a flight, and I asked: How do you know if you solved the puzzle correctly?

The study proposes an alternative repurposing technique for turning the weakness of deep neural networks into strengths. Deep learning has been an expanse of artificial intelligence, heavily researched by the data scientists in the past few areas. Experts are more curious about supplementing this technology in sectors where human skills perform mundane tasks. As it uses big data, which is garnered from various sources, makes patterns of this collected data and learn to perform a task without any supervision, it becomes data Hungry, which becomes a major challenge when the data is in scarcity. Apart from being hungry, two significant drawbacks presented with deep learning is the opacity and its shallowness.

Sanjeev is a recipient of the 40 Under 40 Data Scientists award, at the Machine Learning Dev Summit for his research in autonomous tech.

Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on the setting where all the entities are presented at training time. Although this setting is standard for conventional KG completion, it has limitations for CKG completion. At test time, entities in CKGs can be unseen because they may have unseen text/names and entities may be disconnected from the training graph, since CKGs are generally very sparse. Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time. We develop a novel learning framework named InductivE. Different from previous approaches, InductiveE ensures the inductive learning capability by directly computing entity embeddings from raw entity attributes/text. InductiveE consists of a free-text encoder, a graph encoder, and a KG completion decoder. Specifically, the free-text encoder first extracts the textual representation of each entity based on the pre-trained language model and word embedding. The graph encoder is a gated relational graph convolutional neural network that learns from a densified graph for more informative entity representation learning. We develop a method that densifies CKGs by adding edges among semantic-related entities and provide more supportive information for unseen entities, leading to better generalization ability of entity embedding for unseen entities. Finally, inductiveE employs Conv-TransE as the CKG completion decoder. Experimental results show that InductiveE significantly outperforms state-of-the-art baselines in both standard and inductive settings on ATOMIC and ConceptNet benchmarks. InductivE performs especially well on inductive scenarios where it achieves above 48% improvement over present methods.