Search for: All records

Abstract The rapid development of modeling techniques has brought many opportunities for data‐driven discovery and prediction. However, this also leads to the challenge of selecting the most appropriate model for any particular data task. Information criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), have been developed as a general class of model selection methods with profound connections with foundational thoughts in statistics and information theory. Many perspectives and theoretical justifications have been developed to understand when and how to use information criteria, which often depend on particular data circumstances. This review article will revisit information criteria by summarizing their key concepts, evaluation metrics, fundamental properties, interconnections, recent advancements, and common misconceptions to enrich the understanding of model selection in general. This article is categorized under:Data: Types and Structure > Traditional Statistical DataStatistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Information Theoretic MethodsStatistical Models > Model Selection 

Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server. Recent advancements in FL use predefined architecture-based learning for all clients. However, given that clients’ data are invisible to the server and data distributions are non-identical across clients, a predefined architecture discovered in a centralized setting may not be an optimal solution for all the clients in FL. Motivated by this challenge, we introduce SPIDER, an algorithmic frame- work that aims to Search PersonalIzed neural architecture for feDERated learning. SPIDER is designed based on two unique features: (1) alternately optimizing one architecture- homogeneous global model in a generic FL manner and architecture-heterogeneous local models that are connected to the global model by weight-sharing-based regularization, (2) achieving architecture-heterogeneous local models by a perturbation-based neural architecture search method. Experimental results demonstrate superior prediction performance compared with other state-of-the-art personalization methods. Code is available at https://github.com/ErumMushtaq/SPIDER.git. 

Understanding optimization in deep learning is a fundamental problem, and recent findings have challenged the previously held belief that gradient descent stably trains deep networks. In this study, we delve deeper into the instability of gradient descent during the training of deep networks. By employing gradient descent to train various modern deep networks, we provide empirical evidence demonstrating that a significant portion of the optimization progress occurs through the utilization of oscillating gradients. These gradients exhibit a high negative correlation between adjacent iterations. Further- more, we make the following noteworthy observations about these gradient oscillations (GO): (i) GO manifests in different training stages for networks with diverse architectures; (ii) when using a large learning rate, GO consistently emerges across all layers of the networks; and (iii) when employing a small learning rate, GO is more prominent in the input layers compared to the output layers. These discoveries indicate that GO is an inherent characteristic of training different types of neural networks and may serve as a source of inspiration for the development of novel optimizer designs. 

Deep models are known to be vulnerable to data adversarial attacks, and many adversarial training techniques have been developed to improve their adversarial robustness. While data adversaries attack model predictions through modifying data, little is known about their impact on the neuron activations produced by the model, which play a crucial role in determining the model’s predictions and interpretability. In this work, we aim to develop a topological understanding of adversarial training to enhance its interpretability. We analyze the topological structure—in particular, mapper graphs—of neuron activations of data samples produced by deep adversarial training. Each node of a mapper graph represents a cluster of activations, and two nodes are connected by an edge if their corresponding clusters have a nonempty intersection. We provide an interactive visualization tool that demonstrates the utility of our topological framework in exploring the activation space. We found that stronger attacks make the data samples more indistinguishable in the neuron activation space that leads to a lower accuracy. Our tool also provides a natural way to identify the vulnerable data samples that may be useful in improving model robustness.