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Title: A Sharp Characterization of Linear Estimators for Offline Policy Evaluation Abstract: Offline policy evaluation is a fundamental statistical problem in reinforcement learning that involves estimating the value function of some decision-making policy given data collected by a potentially different policy. In orde... |
Title: Dual Lottery Ticket Hypothesis Abstract: Fully exploiting the learning capacity of neural networks requires overparameterized dense networks. On the other side, directly training sparse neural networks typically results in unsatisfactory performance. Lottery Ticket Hypothesis (LTH) provides a novel view to inves... |
Title: Second-life Lithium-ion batteries: A chemistry-agnostic and scalable health estimation algorithm Abstract: Battery state of health is an essential metric for diagnosing battery degradation during testing and operation. While many unique measurements are possible in the design phase, for practical applications of... |
Title: Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models Abstract: For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensive, time-consuming, or pose safety hazards... |
Title: Leveraging Initial Hints for Free in Stochastic Linear Bandits Abstract: We study the setting of optimizing with bandit feedback with additional prior knowledge provided to the learner in the form of an initial hint of the optimal action. We present a novel algorithm for stochastic linear bandits that uses this ... |
Title: Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap Abstract: This work presents Spacecraft Pose Network v2 (SPNv2), a Convolutional Neural Network (CNN) for pose estimation of noncooperative spacecraft across domain gap. SPNv2 is a multi-scale, multi-task CNN which ... |
Title: Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data Abstract: In this work, we propose two novel methodologies to study temporal and morphological phenotypic effects caused by different experimental conditions using imaging data. As a proof of concept, we ... |
Title: Learning from Few Examples: A Summary of Approaches to Few-Shot Learning Abstract: Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learning solutions suffer from data hunger and extensivel... |
Title: Towards performant and reliable undersampled MR reconstruction via diffusion model sampling Abstract: Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time. To this end, current State-of-The-Art (SoTA) approaches leverage deep neural networks and supervised tra... |
Title: R\'enyi State Entropy for Exploration Acceleration in Reinforcement Learning Abstract: One of the most critical challenges in deep reinforcement learning is to maintain the long-term exploration capability of the agent. To tackle this problem, it has been recently proposed to provide intrinsic rewards for the ag... |
Title: CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification Abstract: Self-supervised representation learning of Multivariate Time Series (MTS) is a challenging task and attracts increasing research interests in recent years. Many previous works focus on th... |
Title: UENAS: A Unified Evolution-based NAS Framework Abstract: Neural architecture search (NAS) has gained significant attention for automatic network design in recent years. Previous NAS methods suffer from limited search spaces, which may lead to sub-optimal results. In this paper, we propose UENAS, an evolution-bas... |
Title: LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data Abstract: Federated learning (FL) and split learning (SL) are the two popular distributed machine learning (ML) approaches that provide some data privacy protection mechanisms. In the time-series classification problem, many researcher... |
Title: A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge Abstract: Contact tracing is a method used by public health organisations to try prevent the spread of infectious diseases in the community. Traditionally performed by manual contact tracers, more recently the use of apps have been considere... |
Title: Breast cancer detection using artificial intelligence techniques: A systematic literature review Abstract: Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer fou... |
Title: Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control Abstract: Intelligent Traffic Light Control System (ITLCS) is a typical Multi-Agent System (MAS), which comprises multiple roads and traffic lights.Constructing a model of MAS for ITLCS is the basis to alleviate traffic congestion. Ex... |
Title: Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning Abstract: In this paper, we study the cluster head detection problem of a two-level unmanned aerial vehicle (UAV) swarm network (USNET) with multiple UAV clusters, where the inherent follow strategy (IFS) of low-level follower ... |
Title: PyNET-QxQ: A Distilled PyNET for QxQ Bayer Pattern Demosaicing in CMOS Image Sensor Abstract: The deep learning-based ISP models for mobile cameras produce high-quality images comparable to the professional DSLR camera. However, many of them are computationally expensive, which may not be appropriate for mobile ... |
Title: MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent Abstract: Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sens... |
Title: Beam Search for Feature Selection Abstract: In this paper, we present and prove some consistency results about the performance of classification models using a subset of features. In addition, we propose to use beam search to perform feature selection, which can be viewed as a generalization of forward selection... |
Title: TTML: tensor trains for general supervised machine learning Abstract: This work proposes a novel general-purpose estimator for supervised machine learning (ML) based on tensor trains (TT). The estimator uses TTs to parametrize discretized functions, which are then optimized using Riemannian gradient descent unde... |
Title: New Coresets for Projective Clustering and Applications Abstract: $(j,k)$-projective clustering is the natural generalization of the family of $k$-clustering and $j$-subspace clustering problems. Given a set of points $P$ in $\mathbb{R}^d$, the goal is to find $k$ flats of dimension $j$, i.e., affine subspaces, ... |
Title: Deep Learning for Sleep Stages Classification: Modified Rectified Linear Unit Activation Function and Modified Orthogonal Weight Initialisation Abstract: Background and Aim: Each stage of sleep can affect human health, and not getting enough sleep at any stage may lead to sleep disorder like parasomnia, apnea, i... |
Title: Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine Abstract: Hex is a turn-based two-player connection game with a high branching factor, making the game arbitrarily complex with increasing board sizes. As such, top-performing algorithms for playing Hex rely on accurate evaluation... |
Title: Regularized Training of Intermediate Layers for Generative Models for Inverse Problems Abstract: Generative Adversarial Networks (GANs) have been shown to be powerful and flexible priors when solving inverse problems. One challenge of using them is overcoming representation error, the fundamental limitation of t... |
Title: A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19 Abstract: The COVID-19 pandemic has significantly impacted the tourism and hospitality sector. Public policies such as travel restrictions and stay-at-home orders had significantly affected tourist activities and service businesses... |
Title: On generative models as the basis for digital twins Abstract: A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modelling applications. T... |
Title: Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data Abstract: Data-centric AI encourages the need of cleaning and understanding of data in order to achieve trustworthy AI. Existing technologies, such as AutoML, make it easier to design and train models automatica... |
Title: Structural Learning of Simple Staged Trees Abstract: Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric conditiona... |
Title: Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring Abstract: Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, Motion Artifacts (MAs) severely impact the monitor... |
Title: KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios Abstract: With the expected rise in behind-the-meter solar penetration within the distribution networks, there is a need to develop time-series forecasting methods that can reliably predict the net-load, accurately quantifyi... |
Title: Art-Attack: Black-Box Adversarial Attack via Evolutionary Art Abstract: Deep neural networks (DNNs) have achieved state-of-the-art performance in many tasks but have shown extreme vulnerabilities to attacks generated by adversarial examples. Many works go with a white-box attack that assumes total access to the ... |
Title: ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches Abstract: Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires ca... |
Title: Score matching enables causal discovery of nonlinear additive noise models Abstract: This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new genera... |
Title: Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data Abstract: Integrating cross-department multi-modal data (e.g., radiological, pathological, genomic, and clinical data) is ubiquitous in brain cancer diagnosis and survival prediction. To date, such an integra... |
Title: Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems Abstract: Recent advancements in deep learning have led to drastic improvements in speech segregation models. Despite their success and growing applicability, few efforts have been made to analyze t... |
Title: Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction Abstract: Multi-agent interactions are important to model for forecasting other agents' behaviors and trajectories. At a certain time, to forecast a reasonable future trajectory, each agent needs to pay attention to the interactions with onl... |
Title: Variational Inference with Locally Enhanced Bounds for Hierarchical Models Abstract: Hierarchical models represent a challenging setting for inference algorithms. MCMC methods struggle to scale to large models with many local variables and observations, and variational inference (VI) may fail to provide accurate... |
Title: The Flag Median and FlagIRLS Abstract: Finding prototypes (e.g., mean and median) for a dataset is central to a number of common machine learning algorithms. Subspaces have been shown to provide useful, robust representations for datasets of images, videos and more. Since subspaces correspond to points on a Gras... |
Title: Reproducible Subjective Evaluation Abstract: Human perceptual studies are the gold standard for the evaluation of many research tasks in machine learning, linguistics, and psychology. However, these studies require significant time and cost to perform. As a result, many researchers use objective measures that ca... |
Title: CIDER: Exploiting Hyperspherical Embeddings for Out-of-Distribution Detection Abstract: Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent advances in representation learning give rise to developments in distance-based OOD detection, where testing samples are detected as... |
Title: Machine Learning in NextG Networks via Generative Adversarial Networks Abstract: Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we in... |
Title: Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding Abstract: Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the brain. In this work, we are in... |
Title: Contextual Networks and Unsupervised Ranking of Sentences Abstract: We construct a contextual network to represent a document with syntactic and semantic relations between word-sentence pairs, based on which we devise an unsupervised algorithm called CNATAR (Contextual Network And Text Analysis Rank) to score se... |
Title: Downstream Fairness Caveats with Synthetic Healthcare Data Abstract: This paper evaluates synthetically generated healthcare data for biases and investigates the effect of fairness mitigation techniques on utility-fairness. Privacy laws limit access to health data such as Electronic Medical Records (EMRs) to pre... |
Title: The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks Abstract: Neural networks tend to achieve better accuracy with training if they are larger -- even if the resulting models are overparameterized. Nevertheless, carefully removing such excess parameters before, during, or ... |
Title: Boilerplate Detection via Semantic Classification of TextBlocks Abstract: We present a hierarchical neural network model called SemText to detect HTML boilerplate based on a novel semantic representation of HTML tags, class names, and text blocks. We train SemText on three published datasets of news webpages and... |
Title: Multi-Agent Policy Transfer via Task Relationship Modeling Abstract: Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous work on multi-agent transfer learning accommodate teams of different sizes, heavily relying on the gen... |
Title: ReVar: Strengthening Policy Evaluation via Reduced Variance Sampling Abstract: This paper studies the problem of data collection for policy evaluation in Markov decision processes (MDPs). In policy evaluation, we are given a target policy and asked to estimate the expected cumulative reward it will obtain in an ... |
Title: Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process Abstract: The causal mechanism between climate and political violence is fraught with complex mechanisms. Current quantitative causal models rely on one or more assumptions: (1) the climate dr... |
Title: Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression Abstract: We propose two bounded comparison metrics that may be implemented to arbitrary dimensions in regression tasks. One quantifies the struct... |
Title: Multi-Agent Active Search using Detection and Location Uncertainty Abstract: Active search refers to the task of autonomous robots (agents) detecting objects of interest (targets) in a search space using decision making algorithms that adapt to the history of their observations. It has important applications in ... |
Title: Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability Abstract: We give the first sample complexity characterizations for outcome indistinguishability, a theoretical framework of machine learning recently introduced by Dwork, Kim, Reingold, Rothblum, and Yona (STOC 2021). In outcome in... |
Title: MetaCon: Unified Predictive Segments System with Trillion Concept Meta-Learning Abstract: Accurate understanding of users in terms of predicative segments play an essential role in the day to day operation of modern internet enterprises. Nevertheless, there are significant challenges that limit the quality of da... |
Title: MLNav: Learning to Safely Navigate on Martian Terrains Abstract: We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars. MLNav makes judicious use of machine learning to enhance the effici... |
Title: All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicators Abstract: Large-scale semantic image annotation is a significant challenge for learning-based perception systems in robotics. Current approaches often rely on human labelers, which can be expensive, or simula... |
Title: A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices Abstract: Neither deep neural networks nor symbolic AI alone have approached the kind of intelligence expressed in humans. This is mainly because neural networks are not able to decompose distinct objects from their joint representatio... |
Title: Reinforced Meta Active Learning Abstract: In stream-based active learning, the learning procedure typically has access to a stream of unlabeled data instances and must decide for each instance whether to label it and use it for training or to discard it. There are numerous active learning strategies which try to... |
Title: Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for selected applications in stock and cryptocurrency trading Abstract: We investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency trading. More specifically, we build on the... |
Title: Unsupervised Domain Adaptation across FMCW Radar Configurations Using Margin Disparity Discrepancy Abstract: Commercial radar sensing is gaining relevance and machine learning algorithms constitute one of the key components that are enabling the spread of this radio technology into areas like surveillance or hea... |
Title: Data-driven detector signal characterization with constrained bottleneck autoencoders Abstract: A common technique in high energy physics is to characterize the response of a detector by means of models tunned to data which build parametric maps from the physical parameters of the system to the expected signal o... |
Title: Representation, learning, and planning algorithms for geometric task and motion planning Abstract: We present a framework for learning to guide geometric task and motion planning (GTAMP). GTAMP is a subclass of task and motion planning in which the goal is to move multiple objects to target regions among movable... |
Title: Attention-effective multiple instance learning on weakly stem cell colony segmentation Abstract: The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for... |
Title: Speaker Identification Experiments Under Gender De-Identification Abstract: The present work is based on the COST Action IC1206 for De-identification in multimedia content. It was performed to test four algorithms of voice modifications on a speech gender recognizer to find the degree of modification of pitch wh... |
Title: HAIDA: Biometric technological therapy tools for neurorehabilitation of Cognitive Impairment Abstract: Dementia, and specially Alzheimer s disease (AD) and Mild Cognitive Impairment (MCI) are one of the most important diseases suffered by elderly population. Music therapy is one of the most widely used non-pharm... |
Title: Quantum neural networks force fields generation Abstract: Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in predicting accurate va... |
Title: Deep learning-based reconstruction of highly accelerated 3D MRI Abstract: Purpose: To accelerate brain 3D MRI scans by using a deep learning method for reconstructing images from highly-undersampled multi-coil k-space data Methods: DL-Speed, an unrolled optimization architecture with dense skip-layer connections... |
Title: SparseChem: Fast and accurate machine learning model for small molecules Abstract: SparseChem provides fast and accurate machine learning models for biochemical applications. Especially, the package supports very high-dimensional sparse inputs, e.g., millions of features and millions of compounds. It is possible... |
Title: The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems Abstract: Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where benign and malicious samples are clearly labelle... |
Title: FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction Abstract: Modern scientific research and applications very often encounter "fragmentary data" which brings big challenges to imputation and prediction. By leveraging the structure of response patterns, we propose a unified and f... |
Title: Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences Abstract: Given two object images, how can we explain their differences in terms of the underlying object properties? To address this question, we propose Align-Deform-Subtract (ADS) -- an interventional framework for explaining... |
Title: Structured Multi-task Learning for Molecular Property Prediction Abstract: Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the num... |
Title: Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition Abstract: Due to the development of machine learning and speech processing, speech emotion recognition has been a popular research topic in recent years. However, the speech data cannot be protected when it is uploaded and proce... |
Title: Non-equilibrium molecular geometries in graph neural networks Abstract: Graph neural networks have become a powerful framework for learning complex structure-property relationships and fast screening of chemical compounds. Recently proposed methods have demonstrated that using 3D geometry information of the mole... |
Title: Score-Based Generative Models for Molecule Generation Abstract: Recent advances in generative models have made exploring design spaces easier for de novo molecule generation. However, popular generative models like GANs and normalizing flows face challenges such as training instabilities due to adversarial train... |
Title: Language Model-driven Negative Sampling Abstract: Knowledge Graph Embeddings (KGEs) encode the entities and relations of a knowledge graph (KG) into a vector space with a purpose of representation learning and reasoning for an ultimate downstream task (i.e., link prediction, question answering). Since KGEs follo... |
Title: Data Representativity for Machine Learning and AI Systems Abstract: Data representativity is crucial when drawing inference from data through machine learning models. Scholars have increased focus on unraveling the bias and fairness in the models, also in relation to inherent biases in the input data. However, l... |
Title: On a linear fused Gromov-Wasserstein distance for graph structured data Abstract: We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem. Then we propose a novel distance between two graphs,... |
Title: Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain Abstract: As an essential task for the architecture, engineering, and construction (AEC) industry, information retrieval (IR) from unstructured textual data based on natural language processing (NLP) is gaining in... |
Title: Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding Abstract: With substantial recent developments in aviation technologies, Unmanned Aerial Vehicles (UAVs) are becoming increasingly integrated in commercial and military operations inte... |
Title: A Survey on Reinforcement Learning Methods in Character Animation Abstract: Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment. While learning, they repeatedly take actions based on... |
Title: P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications Abstract: The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in ... |
Title: Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences Abstract: Parallelizing Gated Recurrent Unit (GRU) networks is a challenging task, as the training procedure of GRU is inherently sequential. Prior efforts to parallelize GRU have largely focused on conventional parallelization strateg... |
Title: Regularized Deep Signed Distance Fields for Reactive Motion Generation Abstract: Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate onl... |
Title: SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters Abstract: This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumpt... |
Title: Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model Abstract: In the coming years and decades, autonomous vehicles (AVs) will become increasingly prevalent, offering new opportunities for safer and more convenient travel and potentially smarter traffic control methods exploit... |
Title: Using Statistical Models to Detect Occupancy in Buildings through Monitoring VOC, CO$_2$, and other Environmental Factors Abstract: Dynamic models of occupancy patterns have shown to be effective in optimizing building-systems operations. Previous research has relied on CO$_2$ sensors and vision-based techniques... |
Title: Machine Learning Methods in Solving the Boolean Satisfiability Problem Abstract: This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despite the great success of modern SAT solvers to solve ... |
Title: Explainable Machine Learning for Predicting Homicide Clearance in the United States Abstract: Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic ... |
Title: Autoregressive based Drift Detection Method Abstract: In the classic machine learning framework, models are trained on historical data and used to predict future values. It is assumed that the data distribution does not change over time (stationarity). However, in real-world scenarios, the data generation proces... |
Title: Dimensionality Reduction and Prioritized Exploration for Policy Search Abstract: Black-box policy optimization is a class of reinforcement learning algorithms that explores and updates the policies at the parameter level. This class of algorithms is widely applied in robotics with movement primitives or non-diff... |
Title: Geometric Optimisation on Manifolds with Applications to Deep Learning Abstract: We design and implement a Python library to help the non-expert using all these powerful tools in a way that is efficient, extensible, and simple to incorporate into the workflow of the data scientist, practitioner, and applied rese... |
Title: Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets Abstract: This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. With these simple modifications, Graphormer could attain better results o... |
Title: Machine Learning based Optimal Feedback Control for Microgrid Stabilization Abstract: Microgrids have more operational flexibilities as well as uncertainties than conventional power grids, especially when renewable energy resources are utilized. An energy storage based feedback controller can compensate undesire... |
Title: Efficient Sub-structured Knowledge Distillation Abstract: Structured prediction models aim at solving a type of problem where the output is a complex structure, rather than a single variable. Performing knowledge distillation for such models is not trivial due to their exponentially large output space. In this w... |
Title: Automatic Language Identification for Celtic Texts Abstract: Language identification is an important Natural Language Processing task. It has been thoroughly researched in the literature. However, some issues are still open. This work addresses the identification of the related low-resource languages on the exam... |
Title: Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Abstract: In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs. Large-scale edge-based collection of training data in these applications calls f... |
Title: Binary Classification Under $\ell_0$ Attacks for General Noise Distribution Abstract: Adversarial examples have recently drawn considerable attention in the field of machine learning due to the fact that small perturbations in the data can result in major performance degradation. This phenomenon is usually model... |
Title: CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering Abstract: Most semantic segmentation approaches of Hyperspectral images (HSIs) use and require preprocessing steps in the form of patching to accurately classify diversified land cover in remotely sensed images. These approaches use... |
Title: Structural & Granger CAUSALITY for IoT Digital Twin Abstract: In this foundational expository article on the application of Causality Analysis in IoT, we establish the basic theory and algorithms for estimating Structural and Granger causality factors from measured multichannel sensor data (vector timeseries). V... |
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