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Title: All-optical graph representation learning using integrated diffractive photonic computing units Abstract: Photonic neural networks perform brain-inspired computations using photons instead of electrons that can achieve substantially improved computing performance. However, existing architectures can only handle ... |
Title: Smoothed Online Combinatorial Optimization Using Imperfect Predictions Abstract: Smoothed online combinatorial optimization considers a learner who repeatedly chooses a combinatorial decision to minimize an unknown changing cost function with a penalty on switching decisions in consecutive rounds. We study smoot... |
Title: Distributed Dynamic Safe Screening Algorithms for Sparse Regularization Abstract: Distributed optimization has been widely used as one of the most efficient approaches for model training with massive samples. However, large-scale learning problems with both massive samples and high-dimensional features widely ex... |
Title: Federated Contrastive Learning for Volumetric Medical Image Segmentation Abstract: Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federa... |
Title: Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks Abstract: Early diagnosis and intervention are clinically considered the paramount part of treating cerebral palsy (CP), so it is essential to design an efficient and interpretable automatic prediction system for CP. We high... |
Title: Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention Abstract: Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency betw... |
Title: GFCL: A GRU-based Federated Continual Learning Framework against Adversarial Attacks in IoV Abstract: The integration of ML in 5G-based Internet of Vehicles (IoV) networks has enabled intelligent transportation and smart traffic management. Nonetheless, the security against adversarial attacks is also increasing... |
Title: A Novel Splitting Criterion Inspired by Geometric Mean Metric Learning for Decision Tree Abstract: Decision tree (DT) attracts persistent research attention due to its impressive empirical performance and interpretability in numerous applications. However, the growth of traditional yet widely-used univariate dec... |
Title: Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection Abstract: Unsupervised representation learning has been extensively employed in anomaly detection, achieving impressive performance. Extracting valuable feature vectors that can remarkably improve the performance of anomaly ... |
Title: Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets Abstract: Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations. Such collected data is increasingly non-indepe... |
Title: Exploring Negatives in Contrastive Learning for Unpaired Image-to-Image Translation Abstract: Unpaired image-to-image translation aims to find a mapping between the source domain and the target domain. To alleviate the problem of the lack of supervised labels for the source images, cycle-consistency based method... |
Title: Reinforced Causal Explainer for Graph Neural Networks Abstract: Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent technique of highlighting the explanatory subgraph in the input graph, w... |
Title: Improving Self-Supervised Learning-based MOS Prediction Networks Abstract: MOS (Mean Opinion Score) is a subjective method used for the evaluation of a system's quality. Telecommunications (for voice and video), and speech synthesis systems (for generated speech) are a few of the many applications of the method.... |
Title: Industry-Academia Research Collaboration in Software Engineering: The Certus Model Abstract: Context: Research collaborations between software engineering industry and academia can provide significant benefits to both sides, including improved innovation capacity for industry, and real-world environment for moti... |
Title: $\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization Abstract: Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not util... |
Title: Dimension Reduction for time series with Variational AutoEncoders Abstract: In this work, we explore dimensionality reduction techniques for univariate and multivariate time series data. We especially conduct a comparison between wavelet decomposition and convolutional variational autoencoders for dimension redu... |
Title: Selective clustering ensemble based on kappa and F-score Abstract: Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years. Selective clustering ensemble (SCE) can further improve the ensemble performance by s... |
Title: Transformation Invariant Cancerous Tissue Classification Using Spatially Transformed DenseNet Abstract: In this work, we introduce a spatially transformed DenseNet architecture for transformation invariant classification of cancer tissue. Our architecture increases the accuracy of the base DenseNet architecture ... |
Title: Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps Abstract: Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks. As this revolution continues, the ability to explain model predictions has become a major area of interest for the NLP community. In... |
Title: Smart App Attack: Hacking Deep Learning Models in Android Apps Abstract: On-device deep learning is rapidly gaining popularity in mobile applications. Compared to offloading deep learning from smartphones to the cloud, on-device deep learning enables offline model inference while preserving user privacy. However... |
Title: On the semantics of big Earth observation data for land classification Abstract: This paper discusses the challenges of using big Earth observation data for land classification. The approach taken is to consider pure data-driven methods to be insufficient to represent continuous change. We argue for sound theori... |
Title: Time Series Forecasting (TSF) Using Various Deep Learning Models Abstract: Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past ... |
Title: Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning Abstract: Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks. In contrast, literature on task transferability has established that the... |
Title: Can domain adaptation make object recognition work for everyone? Abstract: Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies. We investigate the ... |
Title: U-NO: U-shaped Neural Operators Abstract: Neural operators generalize classical neural networks to maps between infinite-dimensional spaces, e.g. function spaces. Prior works on neural operators proposed a series of novel architectures to learn such maps and demonstrated unprecedented success in learning solutio... |
Title: Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines Abstract: Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which training examples are 'guilty' in making the... |
Title: AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs Abstract: We present the first whiteness test for graphs, i.e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph. The statistical test aims at finding serial dependencies among close-in-time obser... |
Title: Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models Abstract: Data assimilation presents computational challenges because many high-fidelity models must be simulated. Various deep-learning-based surrogate modeling techniques have been developed ... |
Title: Graph Neural Network based Agent in Google Research Football Abstract: Deep neural networks (DNN) can approximate value functions or policies for reinforcement learning, which makes the reinforcement learning algorithms more powerful. However, some DNNs, such as convolutional neural networks (CNN), cannot extrac... |
Title: Competitive Physics Informed Networks Abstract: Physics Informed Neural Networks (PINNs) solve partial differential equations (PDEs) by representing them as neural networks. The original PINN implementation does not provide high accuracy, typically attaining about $0.1\%$ relative error. We formulate and test an... |
Title: Learning and Inference in Sparse Coding Models with Langevin Dynamics Abstract: We describe a stochastic, dynamical system capable of inference and learning in a probabilistic latent variable model. The most challenging problem in such models - sampling the posterior distribution over latent variables - is propo... |
Title: Subgroup Fairness in Graph-based Spam Detection Abstract: Fake reviews are prevalent on review websites such as Amazon and Yelp. GNN is the state-of-the-art method that can detect suspicious reviewers by exploiting the topologies of the graph connecting reviewers, reviews, and target products. However, the discr... |
Title: RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning Abstract: Reasoning about visual relationships is central to how humans interpret the visual world. This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) id... |
Title: Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff Abstract: We consider the problem of evaluating arbitrary multivariate polynomials over a massive dataset, in a distributed computing system with a master node and multiple worker nodes. Generalized Lagrange Coded Computing (GLCC... |
Title: Complete Policy Regret Bounds for Tallying Bandits Abstract: Policy regret is a well established notion of measuring the performance of an online learning algorithm against an adaptive adversary. We study restrictions on the adversary that enable efficient minimization of the \emph{complete policy regret}, which... |
Title: Realistic Evaluation of Transductive Few-Shot Learning Abstract: Transductive inference is widely used in few-shot learning, as it leverages the statistics of the unlabeled query set of a few-shot task, typically yielding substantially better performances than its inductive counterpart. The current few-shot benc... |
Title: M2N: Mesh Movement Networks for PDE Solvers Abstract: Mainstream numerical Partial Differential Equation (PDE) solvers require discretizing the physical domain using a mesh. Mesh movement methods aim to improve the accuracy of the numerical solution by increasing mesh resolution where the solution is not well-re... |
Title: Embedding Knowledge for Document Summarization: A Survey Abstract: Knowledge-aware methods have boosted a range of Natural Language Processing applications over the last decades. With the gathered momentum, knowledge recently has been pumped into enormous attention in document summarization research. Previous wo... |
Title: Bounding the Effects of Continuous Treatments for Hidden Confounders Abstract: Observational studies often seek to infer the causal effect of a treatment even though both the assigned treatment and the outcome depend on other confounding variables. An effective strategy for dealing with confounders is to estimat... |
Title: COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data Abstract: Ever since the declaration of COVID-19 as a pandemic by the World Health Organization in 2020, the world has conti... |
Title: Graph Neural Network-based Early Bearing Fault Detection Abstract: Early detection of faults is of importance to avoid catastrophic accidents and ensure safe operation of machinery. A novel graph neural network-based fault detection method is proposed to build a bridge between AI and real-world running mechanica... |
Title: Lesion Localization in OCT by Semi-Supervised Object Detection Abstract: Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the retina, namely retinal lesions, can be identified. Automated... |
Title: Piecewise-Linear Activations or Analytic Activation Functions: Which Produce More Expressive Neural Networks? Abstract: Many currently available universal approximation theorems affirm that deep feedforward networks defined using any suitable activation function can approximate any integrable function locally in... |
Title: Towards the Semantic Weak Generalization Problem in Generative Zero-Shot Learning: Ante-hoc and Post-hoc Abstract: In this paper, we present a simple and effective strategy lowering the previously unexplored factors that limit the performance ceiling of generative Zero-Shot Learning (ZSL). We begin by formally d... |
Title: Computing the Collection of Good Models for Rule Lists Abstract: Since the seminal paper by Breiman in 2001, who pointed out a potential harm of prediction multiplicities from the view of explainable AI, global analysis of a collection of all good models, also known as a `Rashomon set,' has been attracted much a... |
Title: Improved far-field speech recognition using Joint Variational Autoencoder Abstract: Automatic Speech Recognition (ASR) systems suffer considerably when source speech is corrupted with noise or room impulse responses (RIR). Typically, speech enhancement is applied in both mismatched and matched scenario training ... |
Title: Satellite Image Time Series Analysis for Big Earth Observation Data Abstract: The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in ... |
Title: The Multiscale Structure of Neural Network Loss Functions: The Effect on Optimization and Origin Abstract: Local quadratic approximation has been extensively used to study the optimization of neural network loss functions around the minimum. Though, it usually holds in a very small neighborhood of the minimum, a... |
Title: Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions Abstract: Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers do not have the knowledge to select appropriate crops and fertilizer... |
Title: Deep Learning for Medical Image Registration: A Comprehensive Review Abstract: Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. Thi... |
Title: Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution Abstract: Most real-world domains can be formulated as multi-agent (MA) systems. Intentionality sharing agents can solve more complex tasks by col... |
Title: An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models Abstract: Nowadays, the interpretation of why a machine learning (ML) model makes certain inferences is as crucial as the accuracy of such inferences. Some ML models like the... |
Title: Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages Abstract: The rise in the number of social media users has led to an increase in the hateful content posted online. In countries like India, where multiple languages are spoken, these abhorrent posts are from an unusual blend of code-switched ... |
Title: Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity Abstract: Nowadays, we are more and more reliant on Deep Learning (DL) models and thus it is essential to safeguard the security of these systems. This paper explores the security issues in Deep Learning and an... |
Title: Learning Symmetric Embeddings for Equivariant World Models Abstract: Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples related by transformations. However, characterizing how transformations act on input data is often difficult, li... |
Title: Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine Abstract: The extreme learning machine (ELM) method can yield highly accurate solutions to linear/nonlinear partial differential equations (PDEs), but requires the last hidden layer of the neural network... |
Title: Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction Abstract: Speech emotion recognition systems have high prediction latency because of the high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measuremen... |
Title: Efficient Neural Neighborhood Search for Pickup and Delivery Problems Abstract: We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of fe... |
Title: Accelerated Multiplicative Weights Update Avoids Saddle Points almost always Abstract: We consider non-convex optimization problems with constraint that is a product of simplices. A commonly used algorithm in solving this type of problem is the Multiplicative Weights Update (MWU), an algorithm that is widely use... |
Title: Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games Abstract: In this paper we establish efficient and \emph{uncoupled} learning dynamics so that, when employed by all players in a general-sum multiplayer game, the \emph{swap regret} of each player after $T$ repetitions of the game is b... |
Title: Riemannian Hamiltonian methods for min-max optimization on manifolds Abstract: In this paper, we study the min-max optimization problems on Riemannian manifolds. We introduce a Riemannian Hamiltonian function, minimization of which serves as a proxy for solving the original min-max problems. Under the Riemannian... |
Title: Trusted Multi-View Classification with Dynamic Evidential Fusion Abstract: Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks. Although effective, it is also crucial to ensure the reli... |
Title: Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers Abstract: Unsupervised semantic segmentation aims to discover groupings within and across images that capture object and view-invariance of a category without external supervision. Grouping naturally has lev... |
Title: Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition Abstract: Face recognition for visible light (VIS) images achieve high accuracy thanks to the recent development of deep learning. However, heterogeneous face recognition (HFR), which is a face matching in different domains, i... |
Title: Imitation Learning from Observations under Transition Model Disparity Abstract: Learning to perform tasks by leveraging a dataset of expert observations, also known as imitation learning from observations (ILO), is an important paradigm for learning skills without access to the expert reward function or the expe... |
Title: IMDeception: Grouped Information Distilling Super-Resolution Network Abstract: Single-Image-Super-Resolution (SISR) is a classical computer vision problem that has benefited from the recent advancements in deep learning methods, especially the advancements of convolutional neural networks (CNN). Although state-o... |
Title: Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods Abstract: In recent years, a growing number of deep model-based reinforcement learning (RL) methods have been introduced. The interest in deep model-based RL is not surprising, given its many potential benefits, such as higher sample eff... |
Title: Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer Abstract: The unmanned aerial vehicle (UAV) plays an vital role in various applications such as delivery, military mission, disaster rescue, communication, etc., due to its flexibility and versatility. This pa... |
Title: AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer Abstract: Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and ma... |
Title: Quantifying Unknown Quantum Entanglement via a Hybrid Quantum-Classical Machine Learning Framework Abstract: Quantifying unknown quantum entanglement experimentally is a difficult task, but also becomes more and more necessary because of the fast development of quantum engineering. Machine learning provides prac... |
Title: Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing Abstract: We consider the problem of reconstructing rank-one matrices from random linear measurements, a task that appears in a variety of problems in signal processing, statistics, and machine learning. In this paper, we focus o... |
Title: Faculty Distillation with Optimal Transport Abstract: The outpouring of various pre-trained models empowers knowledge distillation~(KD) by providing abundant teacher resources. Meanwhile, exploring the massive model repository to select a suitable teacher and further extracting its knowledge become daunting chal... |
Title: LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback Abstract: Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and... |
Title: Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction Abstract: Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and lik... |
Title: Determinantal Point Process Likelihoods for Sequential Recommendation Abstract: Sequential recommendation is a popular task in academic research and close to real-world application scenarios, where the goal is to predict the next action(s) of the user based on his/her previous sequence of actions. In the trainin... |
Title: Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks Abstract: Discontinuing ad creatives at an appropriate time is one of the most important ad operations that can have a significant impact on sales. Such operational support for ineffective ads has been less explored than ... |
Title: Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation Abstract: Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot. Notice that, e-commerce platforms usually have multiple entrances fo... |
Title: Addressing Leakage in Self-Supervised Contextualized Code Retrieval Abstract: We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program. Our approach facilitates a large-scale self-supervised contrastive training by splitting source code randomly into ... |
Title: A Simple Structure For Building A Robust Model Abstract: As deep learning applications, especially programs of computer vision, are increasingly deployed in our lives, we have to think more urgently about the security of these applications.One effective way to improve the security of deep learning models is to p... |
Title: Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach Abstract: Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships ... |
Title: Machine learning of the well known things Abstract: Machine learning (ML) in its current form implies that an answer to any problem can be well approximated by a function of a very peculiar form: a specially adjusted iteration of Heavyside theta-functions. It is natural to ask if the answers to the questions, wh... |
Title: Integrating Prior Knowledge in Post-hoc Explanations Abstract: In the field of eXplainable Artificial Intelligence (XAI), post-hoc interpretability methods aim at explaining to a user the predictions of a trained decision model. Integrating prior knowledge into such interpretability methods aims at improving the... |
Title: Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks Abstract: It is promising to solve linear inverse problems by unfolding iterative algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep neural networks (DNNs) with learnable parameters. However, ex... |
Title: Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset Abstract: A variety of methods exist to explain image classification models. However, whether they provide any benefit to users over simply comparing various inputs and the model's respective predictions remains unclear. We conducted... |
Title: Algorithms and Theory for Supervised Gradual Domain Adaptation Abstract: The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of supervised gradual domain adaptation, where labeled data fr... |
Title: A Survey on Word Meta-Embedding Learning Abstract: Meta-embedding (ME) learning is an emerging approach that attempts to learn more accurate word embeddings given existing (source) word embeddings as the sole input. Due to their ability to incorporate semantics from multiple source embeddings in a compact manner... |
Title: HyperNCA: Growing Developmental Networks with Neural Cellular Automata Abstract: In contrast to deep reinforcement learning agents, biological neural networks are grown through a self-organized developmental process. Here we propose a new hypernetwork approach to grow artificial neural networks based on neural c... |
Title: Data-driven prediction and control of extreme events in a chaotic flow Abstract: An extreme event is a sudden and violent change in the state of a nonlinear system. In fluid dynamics, extreme events can have adverse effects on the system's optimal design and operability, which calls for accurate methods for thei... |
Title: Deep electric field predictions by drift-reduced Braginskii theory with plasma-neutral interactions based upon experimental images of boundary turbulence Abstract: We present 2-dimensional turbulent electric field calculations via physics-informed deep learning consistent with (i) drift-reduced Braginskii theory... |
Title: Tac2Pose: Tactile Object Pose Estimation from the First Touch Abstract: In this paper, we present Tac2Pose, an object-specific approach to tactile pose estimation from the first touch for known objects. Given the object geometry, we learn a tailored perception model in simulation that estimates a probability dis... |
Title: Masked Image Modeling Advances 3D Medical Image Analysis Abstract: Recently, masked image modeling (MIM) has gained considerable attention due to its capacity to learn from vast amounts of unlabeled data and has been demonstrated to be effective on a wide variety of vision tasks involving natural images. Meanwhi... |
Title: Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning Abstract: Life and physical sciences have always been quick to adopt the latest advances in machine learning to accelerate scientific discovery. Examples of this are cell segmentation or cancer detection. Nevertheless... |
Title: On the Performance of Machine Learning Methods for Breakthrough Curve Prediction Abstract: Reactive flows are important part of numerous technical and environmental processes. Often monitoring the flow and species concentrations within the domain is not possible or is expensive, in contrast, outlet concentration... |
Title: KnowAugNet: Multi-Source Medical Knowledge Augmented Medication Prediction Network with Multi-Level Graph Contrastive Learning Abstract: Predicting medications is a crucial task in many intelligent healthcare systems. It can assist doctors in making informed medication decisions for patients according to electro... |
Title: Stability Preserving Data-driven Models With Latent Dynamics Abstract: In this paper, we introduce a data-driven modeling approach for dynamics problems with latent variables. The state-space of the proposed model includes artificial latent variables, in addition to observed variables that can be fitted to a giv... |
Title: A feasibility study proposal of the predictive model to enable the prediction of population susceptibility to COVID-19 by analysis of vaccine utilization for advising deployment of a booster dose Abstract: With the present highly infectious dominant SARS-CoV-2 strain of B1.1.529 or Omicron spreading around the g... |
Title: Adversarial Attention for Human Motion Synthesis Abstract: Analysing human motions is a core topic of interest for many disciplines, from Human-Computer Interaction, to entertainment, Virtual Reality and healthcare. Deep learning has achieved impressive results in capturing human pose in real-time. On the other ... |
Title: LightDefectNet: A Highly Compact Deep Anti-Aliased Attention Condenser Neural Network Architecture for Light Guide Plate Surface Defect Detection Abstract: Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV disp... |
Title: CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection Abstract: Photovoltaic cells are electronic devices that convert light energy to electricity, forming the backbone of solar energy harvesting systems. An essential step in the manufactu... |
Title: Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders Abstract: We investigate the potential of adaptive blind equalizers based on variational inference for carrier recovery in optical communications. These equalizers are based on a low-complexity approximati... |
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