Adversarial training has been proven to be a powerful regularization method to improve the generalization of models. However, current adversarial training methods only attack the original input sample or the embedding vectors, and their attacks lack coverage and diversity. To further enhance the breadth and depth of attack, we propose a novel masked weight adversarial training method called DropAttack, which enhances generalization of model by adding intentionally worst-case adversarial perturbations to both the input and hidden layers in different dimensions and minimize the adversarial risks generated by each layer. DropAttack is a general technique and can be adopt to a wide variety of neural networks with different architectures. To validate the effectiveness of the proposed method, we used five public datasets in the fields of natural language processing (NLP) and computer vision (CV) for experimental evaluating. We compare the proposed method with other adversarial training methods and regularization methods, and our method achieves state-of-the-art on all datasets. In addition, Dropattack can achieve the same performance when it use only a half training data compared to other standard training method. Theoretical analysis reveals that DropAttack can perform gradient regularization at random on some of the input and wight parameters of the model. Further visualization experiments show that DropAttack can push the minimum risk of the model to a lower and flatter loss landscapes. Our source code is publicly available on https://github.com/nishiwen1214/DropAttack.

In recent years, near-term noisy intermediate scale quantum (NISQ) computing devices have become available. One of the most promising application areas to leverage such NISQ quantum computer prototypes is quantum machine learning. While quantum neural networks are widely studied for supervised learning, quantum reinforcement learning is still just an emerging field of this area. To solve a classical continuous control problem, we use a continuous-variable quantum machine learning approach. We introduce proximal policy optimization for photonic variational quantum agents and also study the effect of the data re-uploading. We present performance assessment via empirical study using Strawberry Fields, a photonic simulator Fock backend and a hybrid training framework connected to an OpenAI Gym environment and TensorFlow. For the restricted CartPole problem, the two variations of the photonic policy learning achieve comparable performance levels and a faster convergence than the baseline classical neural network of same number of trainable parameters.

In this paper, we propose an analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks. The method enables the analytical Gaussian inference of the posterior mean vector and diagonal covariance matrix for weights and biases. The method proposed has a computational complexity of $\mathcal{O}(n)$ with respect to the number of parameters $n$, and the tests performed on regression and classification benchmarks confirm that, for a same network architecture, it matches the performance of existing methods relying on gradient backpropagation.

CEO of Demand Science, a buyer intelligence platform that accelerates demand gen for the world's largest software, tech & B2B companies. The rhetoric surrounding artificial intelligence (AI) as the panacea for solving business problems has created some skepticism among marketers. It can also be overwhelming. AI might seem like a cure-all for the issues with your data or marketing and sales analytics. In reality, the technology is none of these things, and AI applications can be classified into several categories, depending on the purpose.

Artificial intelligence (AI) is not new, but it is revolutionizing the world. Paired with emerging technologies, the applications for AI currently appear to be endless. From accelerating the pace of life saving drugs to streamline operations for cost-savings and revenue amplification, AI platforms are omnipresent, and their impact is inescapable. IBM terms it the "innovation equation," and explains that AI became the world's fastest-growing tech tool for one reason: necessity. The digital age ushered in previously unthinkable quantities of data.

Autonomous driving, face detections, and numerous computer applications owe their success to deep neural networks. Many may not realize, however, that the blossom of computer vision advancements was due to a specific type of architecture: residual networks.

By 2025, artificial intelligence (AI) will significantly improve our daily life by handling some of today's complex tasks with great efficiency. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Some of the classical approaches to AI include (non-exhaustive list) Search algorithms such as Breath-First, Depth-First, Iterative Deepening Search, A* algorithm, and the field of Logic including Predicate Calculus and Propositional Calculus. Local Search approaches were also developed for example Simulated Annealing, Hill Climbing (see also Greedy), Beam Search and Genetic Algorithms (see below). Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. A non-exhaustive list of examples of techniques include Linear Regression, Logistic Regression, K-Means, k-Nearest Neighbour (kNN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forests, XG Boost, Light Gradient Boosting Machine (LightGBM), CatBoost. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion.

Nowadays machine learning is acting as a heavy loader tool to help us solve problems related to the computer vision department such as image classification, segmentation, processing and many others. Most of these kinds of applications rely on training the neural network in a supervised passion where the labels are available. When it comes to synthetically generated data, these labels are determined by human interpretation. The main challenge is that training our neural network on such synthetic data does not always generalize on real data i,e the targeted data. Even at this point, we can also have a synthetic model which can perform better on real data but that requires careful construction of training set and inclusion of real noise and some features from the real dataset, but practically synthetic and real data are drawn from a different distribution like synthetic data can be generated by using GAN's which is a function of latent space and it is very essential for the success of neural network that both data should have drawn from the same distribution.

Description Welcome, In this course, we aim to specialize in artificial intelligence by doing Machine Learning and Deep Learning Projects at various levels. Before starting the course, you must have basic Python knowledge. Our aim in this course is to turn real-life problems that seem difficult to do into projects and then solve them using latest versions of artificial intelligence algorithms and Python(3.8). This course was prepared in July 2021. We will carry out some of our projects using machine learning and some using deep learning algorithms.

Google has developed a custom-built System on a Chip (SoC), Tensor, to power Pixel phones. "So excited to share our new custom Google Tensor chip, which has been 4 yrs in the making ( for scale)! Tensor builds off of our 2 decades of computing experience and it's our biggest innovation in Pixel to date. Will be on Pixel 6 Pixel 6 Pro in fall," CEO Sundar Pichai tweeted. After Apple ( M1), Huawei (Kirin), Samsung (Exynos), now Google has joined the in-house SoC club.