Random fuzzy variables join the modeling of the impreciseness (due to their ``fuzzy part'') and randomness. Statistical samples of such objects are widely used, and their direct, numerically effective generation is therefore necessary. Usually, these samples consist of triangular or trapezoidal fuzzy numbers. In this paper, we describe theoretical results and simulation algorithms for another family of fuzzy numbers -- LR fuzzy numbers with interval-valued cores. Starting from a simulation perspective on the piecewise linear LR fuzzy numbers with the interval-valued cores, their limiting behavior is then considered. This leads us to the numerically efficient algorithm for simulating a sample consisting of such fuzzy values.

Decentralized machine learning (DL) has been receiving an increasing interest recently due to the elimination of a single point of failure, present in Federated learning setting. Yet, it is threatened by the looming threat of Byzantine clients who intentionally disrupt the learning process by broadcasting arbitrary model updates to other clients, seeking to degrade the performance of the global model. In response, robust aggregation schemes have emerged as promising solutions to defend against such Byzantine clients, thereby enhancing the robustness of Decentralized Learning. Defenses against Byzantine adversaries, however, typically require access to the updates of other clients, a counterproductive privacy trade-off that in turn increases the risk of inference attacks on those same model updates. In this paper, we introduce SecureDL, a novel DL protocol designed to enhance the security and privacy of DL against Byzantine threats. SecureDL~facilitates a collaborative defense, while protecting the privacy of clients' model updates through secure multiparty computation. The protocol employs efficient computation of cosine similarity and normalization of updates to robustly detect and exclude model updates detrimental to model convergence. By using MNIST, Fashion-MNIST, SVHN and CIFAR-10 datasets, we evaluated SecureDL against various Byzantine attacks and compared its effectiveness with four existing defense mechanisms. Our experiments show that SecureDL is effective even in the case of attacks by the malicious majority (e.g., 80% Byzantine clients) while preserving high training accuracy.

Initialization is a crucial step in deep learning that assigns initial values to a neural network's weights and biases before training. The choice of initialization technique can significantly impact a network's ability to learn and generalize. In this article, we'll explore the importance of initialization in deep learning and the common techniques used. To build a neural network using the three initialization methods described in the introduction, we will use the provided model() function. This function implements a three-layer neural network with a linear activation in the input layer, a rectified linear unit (ReLU) activation in the hidden layers, and a sigmoid activation in the output layer.

A machine learning model may have several features, but some feature might have a higher impact on the output than others. For example, if a model is predicting apartment prices, the locality of the apartment might have a higher impact on the output than the number of floors the apartment building has. Hence, we come up with the concept of weights. Each feature is associated with a weight (a number) i.e. the higher the feature has an impact on the output, the larger the weight associated with it. But how do you decide what weight should be assigned to each feature?

Why do we need a random initialization? In other words unit1 and unit2 are symmetric, and it can be shown by induction that these two units are computing the same function after every iteration of training. Even if we have a lot of hidden units in the hidden layer they all are symetric if we initialize corresponding parameters to zeros. To solve this problem we need to initialize randomly rather then with zeros. And then we can initialize \(b_1\) with zeros, because initialization of \(W_1\) breaks the symmetry, and unit1 and unit2 will not output the same value even if we initialize \(b_1\) to zero.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner General Adversarial Network (GAN) is a generative modeling approach using #deeplearning neural networks such as CNN. There are two types of modeling techniques, i) Discriminative modeling and ii) generative modeling. Discriminative models are the typical ones that are used for classification in #machinelearning. They take input as features X (image, for image classification) and predict the output Y(probability of the image) for the given features. On the other hand, generative models output features X (image) given a random value.

General Adversarial Network (GAN) are a generative modelling approach using deep learning neural networks such as CNN. There are two types of modelling techniques, i) Discriminative modelling and ii) generative modelling. Discriminative models are typical one that are used for classification in machine learning. They take input as features X (image, for image classification) and predict the output Y(probability of the image) for the given features. On the other hand, generative models outputs features X (image) given a random value.

If you are starting to work with convolutional layers in deep learning you may be confused at times with the mix of parameters, computations and channels involved. From stripe to padding, input and output channels, kernels and learnable parameters, there is a lot going on. In this article, we are going to go deep till the very bottom of what goes on within these conv layers. First, we initialize the libraries we will need. This will allow us to code both with pytorch and with the numpy library so that we can compare their results later.

If you are starting to work with convolutional layers in deep learning you may be confused at times with the mix of parameters, computations and channels involved. From stripe to padding, input and output channels, kernels and learnable parameters, there is a lot going on. In this article, we are going to go deep till the very bottom of what goes on within these conv layers. First, we initialize the libraries we will need. This will allow us to code both with pytorch and with the numpy library so that we can compare their results later.

In contrast to traditional weight optimization in a continuous space, we demonstrate the existence of effective random networks whose weights are never updated. By selecting a weight among a fixed set of random values for each individual connection, our method uncovers combinations of random weights that match the performance of traditionally-trained networks of the same capacity. We refer to our networks as "slot machines" where each reel (connection) contains a fixed set of symbols (random values). Our backpropagation algorithm "spins" the reels to seek "winning" combinations, i.e., selections of random weight values that minimize the given loss. Quite surprisingly, we find that allocating just a few random values to each connection (e.g., 8 values per connection) yields highly competitive combinations despite being dramatically more constrained compared to traditionally learned weights. Moreover, finetuning these combinations often improves performance over the trained baselines. A randomly initialized VGG-19 with 8 values per connection contains a combination that achieves 90% test accuracy on CIFAR-10. Our method also achieves an impressive performance of 98.1% on MNIST for neural networks containing only random weights.