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1. BANANA: when Behavior ANAlysis meets social Network Alignment

   https://doi.org/10.24963/ijcai.2020/200  

   Ren, Fuxin; Zhang, Zhongbao; Zhang, Jiawei; Su, Sen; Sun, Li; Zhu, Guozhen; Guo, Congying (July 2020, International Joint Conference on Artificial Intelligence)

   null (Ed.)

   Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively. 

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2. Learning Cortical Parcellations Using Graph Neural Networks

   https://doi.org/10.3389/fnins.2021.797500  

   Eschenburg, Kristian M.; Grabowski, Thomas J.; Haynor, David R. (December 2021, Frontiers in Neuroscience)

   Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance. 

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3. Effects of Nonlinearity and Network Architecture on the Performance of Supervised Neural Networks

   https://doi.org/10.3390/a14020051  

   Kulathunga, Nalinda; Ranasinghe, Nishath Rajiv; Vrinceanu, Daniel; Kinsman, Zackary; Huang, Lei; Wang, Yunjiao (February 2021, Algorithms)

   null (Ed.)

   The nonlinearity of activation functions used in deep learning models is crucial for the success of predictive models. Several simple nonlinear functions, including Rectified Linear Unit (ReLU) and Leaky-ReLU (L-ReLU) are commonly used in neural networks to impose the nonlinearity. In practice, these functions remarkably enhance the model accuracy. However, there is limited insight into the effects of nonlinearity in neural networks on their performance. Here, we investigate the performance of neural network models as a function of nonlinearity using ReLU and L-ReLU activation functions in the context of different model architectures and data domains. We use entropy as a measurement of the randomness, to quantify the effects of nonlinearity in different architecture shapes on the performance of neural networks. We show that the ReLU nonliearity is a better choice for activation function mostly when the network has sufficient number of parameters. However, we found that the image classification models with transfer learning seem to perform well with L-ReLU in fully connected layers. We show that the entropy of hidden layer outputs in neural networks can fairly represent the fluctuations in information loss as a function of nonlinearity. Furthermore, we investigate the entropy profile of shallow neural networks as a way of representing their hidden layer dynamics. 

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4. A Comparison Study for Disaster Tweet Classification Using Deep Learning Models

   https://doi.org/10.5220/0012129300003541  

   Dinani, Soudabeh; Caragea, Doina (January 2023, Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA)

   Effectively filtering and categorizing the large volume of user-generated content on social media during disaster events can help emergency management and disaster response prioritize their resources. Deep learning approaches, including recurrent neural networks and transformer-based models, have been previously used for this purpose. Capsule Neural Networks (CapsNets), initially proposed for image classification, have been proven to be useful for text analysis as well. However, to the best of our knowledge, CapsNets have not been used for classifying crisis-related messages, and have not been extensively compared with state-of-the-art transformer-based models, such as BERT. Therefore, in this study, we performed a thorough comparison between CapsNet models, state-of-the-art BERT models and two popular recurrent neural network models that have been successfully used for tweet classification, specifically, LSTM and Bi-LSTM models, on the task of classifying crisis tweets both in terms of their informativeness (binary classification), as well as their humanitarian content (multi-class classification). For this purpose, we used several benchmark datasets for crisis tweet classification, namely CrisisBench, CrisisNLP and CrisisLex. Experimental results show that the performance of the CapsNet models is on a par with that of LSTM and Bi-LSTM models for all metrics considered, while the performance obtained with BERT models have surpassed the performance of the other three models across different datasets and classes for both classification tasks, and thus BERT could be considered the best overall model for classifying crisis tweets. 

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5. SCORCH: Neural Architecture Search and Hardware Accelerator Co-design with Reinforcement Learning

   https://doi.org/10.1109/ISQED60706.2024.10528756  

   Liu, Siqin; Karanth, Avinash (April 2024, IEEE)

   The ability to automatically generate a neural network architecture and the corresponding hardware implementation to optimize both accuracy and performance characteristics (latency, power) simultaneously for edge-based Artificial Intelligence (AI) applications is becoming prevalent. As both neural architecture search (NAS) and hardware implementation have ample design space, it is very challenging to integrate with resource-constrained edge computing hardware since the current co-search frameworks take several hundreds of GPU hours to converge. In this paper, we propose SCORCH, a novel neural architecture search and hardware accelerator co-design framework with reinforcement learning to maximize accuracy, and increase energy efficiency and throughput while converging faster. By predicting hyperparameters of neural networks together with hardware resources, we use a reinforcement-based multi-phased controller to explore neural architecture to achieve higher accuracy and hardware performance simultaneously by applying customized dataflows, voltage/frequency scaling, and tunable Network-on-Chip (NoC) hardware parameters. Our simulation results on the CIFAR-10/100 and ImageNet datasets show that SCORCH achieves identical neural network accuracy while achieving 2.6% higher accuracy, and 35.6%, 26.2%, and 65.8% reductions in latency, energy, and area compared with state-of-art co-search frameworks such as DANCE, NANDS, and NASAIC. 

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