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1. A Collective Learning Framework to Boost GNN Expressiveness for Node Classification

   Hang, Mengyue; Neville, Jennifer; Ribeiro, Bruno (July 2021, International Conference on Machine Learning)

   Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighboring nodes rather than dependencies among observed node labels. In this work, we consider the task of inductive node classification using GNNs in supervised and semi-supervised settings, with the goal of incorporating label dependencies. Because current GNNs are not universal (i.e., most-expressive) graph representations, we propose a general collective learning approach to increase the representation power of any existing GNN. Our framework combines ideas from collective classification with self-supervised learning, and uses a Monte Carlo approach to sampling embeddings for inductive learning across graphs. We evaluate performance on five real-world network datasets and demonstrate consistent, significant improvement in node classification accuracy, for a variety of state-of-the-art GNNs. 

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2. Beyond transfer learning: Leveraging ancillary images in automated classification of plankton

   https://doi.org/10.1002/lom3.10648  

   Ellen, Jeffrey_S; Ohman, Mark_D (September 2024, Limnology and Oceanography: Methods)

   Abstract We assess whether a supervised machine learning algorithm, specifically a convolutional neural network (CNN), achieves higher accuracy on planktonic image classification when including non‐plankton and ancillary plankton during the training procedure. We focus on the case of optimizing the CNN for a single planktonic image source, while considering ancillary images to be plankton images from other instruments. We conducted two sets of experiments with three different types of plankton images (from aZooglider, Underwater Vision Profiler 5, and Zooscan), and our results held across all three image types. First, we considered whether single‐stage transfer learning using non‐plankton images was beneficial. For this assessment, we used ImageNet images and the 2015 ImageNet contest‐winning model, ResNet‐152. We found increased accuracy using a ResNet‐152 model pretrained on ImageNet, provided the entire network was retrained rather than retraining only the fully connected layers. Next, we combined all three plankton image types into a single dataset with 3.3 million images (despite their differences in contrast, resolution, and pixel pitch) and conducted a multistage transfer learning assessment. We executed a transfer learning stage from ImageNet to the merged ancillary plankton dataset, then a second transfer learning stage from that merged plankton model to a single instrument dataset. We found that multistage transfer learning resulted in additional accuracy gains. These results should have generality for other image classification tasks. 

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3. Privacy Leak Classification from Mobile Devices

   Shuba, A.; Bakopoulou, E; Markopoulou, A (June 2018, In Proc. of SPAWC (19th IEEE Int’l Workshop in Signal Processing Advances in Wireless Communications)

   Mobile devices have access to personal, potentially sensitive data, and there is a growing number of mobile apps that have access to it and often transmit this personally identifiable information (PII) over the network. In this paper, we present an approach for detecting such PII “leaks” in network packets going out of the device, by first monitoring network packets on the device itself and then applying classifiers that can predict with high accuracy whether a packet contains a PII leak and of which type. We evaluate the performance of our classifiers using datasets that we collected and analyzed from scratch. We also report preliminary results that show that collaboration among users can further improve classification accuracy, thus motivating crowdsourcing and/or distributed learning of privacy leaks. 

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4. Statistical Minimax Lower Bounds for Transfer Learning in Linear Binary Classification

   https://doi.org/10.1109/ISIT50566.2022.9834760  

   Mousavi Kalan, Seyed Mohammadreza; Soltanolkotabi, Mahdi; Avestimehr, A. Salman (June 2022, IEEE)

   Modern machine learning models require a large amount of labeled data for training to perform well. A recently emerging paradigm for reducing the reliance of large model training on massive labeled data is to take advantage of abundantly available labeled data from a related source task to boost the performance of the model in a desired target task where there may not be a lot of data available. This approach, which is called transfer learning, has been applied successfully in many application domains. However, despite the fact that many transfer learning algorithms have been developed, the fundamental understanding of "when" and "to what extent" transfer learning can reduce sample complexity is still limited. In this work, we take a step towards foundational understanding of transfer learning by focusing on binary classification with linear models and Gaussian features and develop statistical minimax lower bounds in terms of the number of source and target samples and an appropriate notion of similarity between source and target tasks. To derive this bound, we reduce the transfer learning problem to hypothesis testing via constructing a packing set of source and target parameters by exploiting Gilbert-Varshamov bound, which in turn leads to a lower bound on sample complexity. We also evaluate our theoretical results by experiments on real data sets. 

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5. Learning-Based Workload Phase Classification and Prediction Using Performance Monitoring Counters

   https://doi.org/10.1109/MLCAD52597.2021.9531161  

   Alcorta, Erika S.; Gerstlauer, Andreas (August 2021, ACM/IEEE Workshop on Machine Learning for CAD (MLCAD))

   null (Ed.)

   Predicting coarse-grain variations in workload behavior during execution is essential for dynamic resource optimization of processor systems. Researchers have proposed various methods to first classify workloads into phases and then learn their long-term phase behavior to predict and anticipate phase changes. Early studies on phase prediction proposed table-based phase predictors. More recently, simple learning-based techniques such as decision trees have been explored. However, more recent advances in machine learning have not been applied to phase prediction so far. Furthermore, existing phase predictors have been studied only in connection with specific phase classifiers even though there is a wide range of classification methods. Early work in phase classification proposed various clustering methods that required access to source code. Some later studies used performance monitoring counters, but they only evaluated classifiers for specific contexts such as thermal modeling. In this work, we perform a comprehensive study of source-oblivious phase classification and prediction methods using hardware counters. We adapt classification techniques that were used with different inputs in the past and compare them to state-of-the-art hardware counter based classifiers. We further evaluate the accuracy of various phase predictors when coupled with different phase classifiers and evaluate a range of advanced machine learning techniques, including SVMs and LSTMs for workload phase prediction. We apply classification and prediction approaches to SPEC workloads running on an Intel Core-i9 platform. Results show that a two-level kmeans clustering combined with SVM-based phase change prediction provides the best tradeoff between accuracy and long-term stability. Additionally, the SVM predictor reduces the average prediction error by 80% when compared to a table-based predictor. 

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