Although the neural network (NN) technique plays an important role in machine learning, understanding the mechanism of NN models and the transparency of deep learning still require more basic research. In this study we propose a novel theory based on space partitioning to estimate the approximate training accuracy for two-layer neural networks on random datasets without training. There appear to be no other studies that have proposed a method to estimate training accuracy without using input data or trained models. Our method estimates the training accuracy for two-layer fully-connected neural networks on two-class random datasets using only three arguments: the dimensionality of inputs (d), the number of inputs (N), and the number of neurons in the hidden layer (L). We have verified our method using real training accuracies in our experiments. The results indicate that the method will work for any dimension, and the proposed theory could extend also to estimate deeper NN models. This study may provide a starting point for a new way for researchers to make progress on the difficult problem of understanding deep learning.

The most existing studies in the facial age estimation assume training and test images are captured under similar shooting conditions. However, this is rarely valid in real-world applications, where training and test sets usually have different characteristics. In this paper, we advocate a cross-dataset protocol for age estimation benchmarking. In order to improve the cross-dataset age estimation performance, we mitigate the inherent bias caused by the learning algorithm itself. To this end, we propose a novel loss function that is more effective for neural network training. The relative smoothness of the proposed loss function is its advantage with regards to the optimisation process performed by stochastic gradient descent (SGD). Compared with existing loss functions, the lower gradient of the proposed loss function leads to the convergence of SGD to a better optimum point, and consequently a better generalisation. The cross-dataset experimental results demonstrate the superiority of the proposed method over the state-of-the-art algorithms in terms of accuracy and generalisation capability.

Graph Neural Networks (GNNs) have been increasingly deployed in a multitude of different applications that involve node-wise and graph-level tasks. The existing literature usually studies these questions independently while they are inherently correlated. We propose in this work a unified model, Adaptive Multi-grained GNN (AdamGNN), to learn node and graph level representation interactively. Compared with the existing GNN models and pooling methods, AdamGNN enhances node representation with multi-grained semantics and avoids node feature and graph structure information loss during pooling. More specifically, a differentiable pooling operator in AdamGNN is used to obtain a multi-grained structure that involves node-wise and meso/macro level semantic information. The unpooling and flyback aggregators in AdamGNN is to leverage the multi-grained semantics to enhance node representation. The updated node representation can further enrich the generated graph representation in the next iteration. Experimental results on twelve real-world graphs demonstrate the effectiveness of AdamGNN on multiple tasks, compared with several competing methods. In addition, the ablation and empirical studies confirm the effectiveness of different components in AdamGNN.

The purpose of convolutional layers, as mentioned previously are to extract features or details from an image. A complete CNN will have many convolutional layers. As a general trend, deeper layers will extract specific shapes for example eyes from an image, while shallower layers extract more general shapes like lines and curves. Pooling layers are used to reduce the size of the convolved map. This decreases the number of parameters needed to train and the computing power needed to train the model.

Deep learning is helping Facebook draw value from a larger portion of its unstructured datasets created by almost 2 billion people updating their statuses 293,000 times per minute. Most of its deep learning technology is built on the Torch framework that focuses on deep learning technologies and neural networks. One of the most useful tools used by Facebook is Deeptext Deep Text uses unsupervised machine learning to understand humans and interpret what they say. Deeptext, which decodes the meaning of the content posted to find the relative meaning. Facebook then helps brands to generate leads with this tool by directing people to advertisers based on the conversations they are having.

Some recent breakthroughs in artificial intelligence, though striking, can seem of mainly theoretical interest. It is one thing to generate impressive results in a research setting, quite another to apply them in practice. For many businesses, the reality of machine learning has not lived up to the theory. Preparing and cleaning the data, training the models, generating consistent, usable results: all are often harder than they are made out to be. But the AI era also has the power to surprise, and the advances can come in unpredictable leaps.

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. One of the arguments that is regularly used in favor of machine learning systems is the fact that they can arrive to decisions without being vulnerable to human subjectivity. However, that argument is only partially true.

Neural networks are a series of algorithms that identify underlying relationships in a set of data. These algorithms are heavily based on the way a human brain operates. These networks can adapt to changing input and generate the best result without the requirement to redesign the output criteria. In a way, these neural networks are similar to the systems of biological neurons. Deep learning is an important part of machine learning, and the deep learning algorithms are based on neural networks.

In the field of Deep Learning, datasets are an essential part of every project. To train a neural network that can handle new situations, one has to use a dataset that represents the upcoming scenarios of the world. An image classification model trained on animal images will not perform well on a car classification task. Alongside training the best models, researchers use public datasets as a benchmark of their model performance. I personally think that easy-to-use public benchmarks are one of the most useful tools to help facilitate the research process. A great example of this is the Papers With Code state-of-the-art charts.

The scientists from Stevens will be giving a talk on the AI program's latest password-cracking developments at the 42nd IEEE Symposium on Security and Privacy in 2021. "Since 2017, we have improved PassGAN, and now it uses a form of reinforcement learning very similar to how AlphaZero has learned how to play chess," says Giuseppe Ateniese, the department chair of the Schaefer School of Engineering & Science at Stevens who co-authored the original paper on PassGAN. READ MORE: Three ways artificial intelligence can improve campus cybersecurity. The talk will expand on how deep learning models allow researchers to gain and interpret important intelligence -- such as semantic similarities between user passwords -- from large password data sets. "In our work, we show that these neural representations capture many properties of password distributions and enable new password guessing techniques," the study's leading researcher, Dario Pasquini, says in a preview of the upcoming IEEE talk.