Regularization techniques play a crucial role in preventing overfitting and improving the generalization performance of neural networks. Dropout, a widely used regularization technique, randomly deactivates units during training to introduce redundancy and prevent co-adaptation among neurons. Despite its effectiveness, dropout has limitations, such as its static nature and lack of interpretability. In this paper, we propose a novel approach to regularization by substituting dropout with Conway's Game of Life (GoL), a cellular automata with simple rules that govern the evolution of a grid of cells. We introduce dynamic unit deactivation during training by representing neural network units as cells in a GoL grid and applying the game's rules to deactivate units. This approach allows for the emergence of spatial patterns that adapt to the training data, potentially enhancing the network's ability to generalize. We demonstrate the effectiveness of our approach on the CIFAR-10 dataset, showing that dynamic unit deactivation using GoL achieves comparable performance to traditional dropout techniques while offering insights into the network's behavior through the visualization of evolving patterns. Furthermore, our discussion highlights the applicability of our proposal in deeper architectures, demonstrating how it enhances the performance of different dropout techniques.

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We will also investigate training AlexNet and VGG architectures using our approaches. We will show these plots in a new Appendix. In our ResNet-18 and ResNet-50 tests, the computational costs of WM's additional forward passes were 1.8 GFLOPs Could the brain have one-to-one wiring between forward and feedback neurons? We know that very precise and consistent neuronal wiring is found in simple organisms such as C. elegans And finally, something less than strict one-to-one wiring may suffice for effective learning.

This study presents an innovative AI-driven tool for diagnosing Peyronie's Disease (PD), a condition that affects between 0.3% and 13.1% of men worldwide. Our method uses key point detection on both images and videos to measure penile curvature angles, utilizing advanced computer vision techniques. This tool has demonstrated high accuracy in identifying anatomical landmarks, validated against conventional goniometer measurements. Traditional PD diagnosis often involves subjective and invasive methods, which can lead to patient discomfort and inaccuracies. Our approach offers a precise, reliable, and non-invasive diagnostic tool to address these drawbacks. The model distinguishes between PD and normal anatomical changes with a sensitivity of 96.7% and a specificity of 100%. This advancement represents a significant improvement in urological diagnostics, greatly enhancing the efficacy and convenience of PD assessment for healthcare providers and patients.

Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision. However, a principled way in which to train such hierarchies in the unsupervised setting has remained elusive. In this work we suggest a new architecture and loss for training deep feature hierarchies that linearize the transformations observed in unlabeled natural video sequences. This is done by training a generative model to predict video frames. We also address the problem of inherent uncertainty in prediction by introducing latent variables that are non-deterministic functions of the input into the network architecture.

The use of automated decision support and decision-making systems (ADM) (Hardt, Price, and Srebro 2016) in applications with direct impact on people's lives has increasingly become a fact of life, e,g. in criminal justice (Kleinberg, Contributions. We propose a technique that uses Bias Mullainathan, and Raghavan 2016; Jain et al. 2020b; Dressel Parity Score (BPS) measures to characterize fairness and develop and Farid 2018), medical diagnosis (Kleinberg, Mullainathan, a family of corresponding loss functions that are used and Raghavan 2016; Ahsen, Ayvaci, and Raghunathan as regularizers during training of Neural Networks to enhance 2019), insurance (Baudry and Robert 2019), credit fairness of the trained models. The goal here is to permit card fraud detection (Dal Pozzolo et al. 2014), electronic the system to actively pursue fair solutions during training health record data (Gianfrancesco et al. 2018), credit scoring while maintaining as high a performance on the task as (Huang, Chen, and Wang 2007) and many more diverse possible. We apply the approach in the context of several domains. This, in turn, has lead to an urgent need fairness measures and investigate multiple loss function formulations for study and scrutiny of the bias-magnifying effects of machine and regularization weights in order to study the learning and Artificial Intelligence algorithms and thus performance as well as potential drawbacks and deployment their potential to introduce and emphasize social inequalities considerations. In these experiments we show that, if used and systematic discrimination in our society. Appropriately, with appropriate settings, the technique measurably reduces much research is being done currently to mitigate bias race-based bias in recidivism prediction, and demonstrate in AI-based decision support systems (Ahsen, Ayvaci, and on the gender-based Adult Income dataset that the proposed Raghunathan 2019; Kleinberg, Mullainathan, and Raghavan method can outperform state-of-the art techniques aimed at 2016; Noriega-Campero et al. 2019; Feldman 2015; more targeted aspects of bias and fairness.

Note that most widely used benchmark datasets for point cloud classification only contain foreground objects. Therefore, we generate a new dataset, where each point cloud contains both the foreground object and the background. In this new dataset, the background is composed of points that carry no relevant information of the foreground. We will introduce details in Section 5. Metric 3, rotation robustness: The rotation robustness is proposed to measure whether a DNN uses similar subsets of two point clouds to compute the intermediate-layer feature, if the two point clouds have the same shape but different orientations. Let X θ 1 and X θ 2 denote the point clouds that have the same global shape but different orientations θ 1 and θ 2. To quantify the similarity of the attention on the two point clouds, we compute the Jensen-Shannon divergence between the distributions of the perturbed inputs ˆ X θ 1 X θ 1 δ 1 and ˆ X θ 2 X θ 2 δ 2. ˆ X θ 1 and ˆ X θ 2 denote the perturbed inputs, which are computed to measure information discarding in Equation (1).