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Title: Calibrating for Class Weights by Modeling Machine Learning Abstract: A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities. Our starting point is that calibration is seemingly incompatible with class weighting, a tec... |
Title: Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation Abstract: Research in machine learning fairness has historically considered a single binary demographic attribute; however, the reality is of course far more complicated. In this work... |
Title: Sentence-level Privacy for Document Embeddings Abstract: User language data can contain highly sensitive personal content. As such, it is imperative to offer users a strong and interpretable privacy guarantee when learning from their data. In this work, we propose SentDP: pure local differential privacy at the s... |
Title: Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence Abstract: Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations... |
Title: Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning Abstract: With the advent of sophisticated machine learning (ML) techniques and the promising results they yield, especially in medical applications, where they have been investigated for different tasks to enhance the dec... |
Title: A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems Abstract: We present a safety verification framework for design-time and run-time assurance of learning-based components in aviation systems. Our proposed framework integrates two novel methodologies. From the design-time ass... |
Title: Towards Optimal VPU Compiler Cost Modeling by using Neural Networks to Infer Hardware Performances Abstract: Calculating the most efficient schedule of work in a neural network compiler is a difficult task. There are many parameters to be accounted for that can positively or adversely affect that schedule depend... |
Title: A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processing Abstract: There has been significant debate in the NLP community about whether or not attention weights can be used as an explanation - a mechanism for interpreting how important each input t... |
Title: Should attention be all we need? The epistemic and ethical implications of unification in machine learning Abstract: "Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms that underpin them... |
Title: A Probabilistic Generative Model of Free Categories Abstract: Applied category theory has recently developed libraries for computing with morphisms in interesting categories, while machine learning has developed ways of learning programs in interesting languages. Taking the analogy between categories and languag... |
Title: How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations? Abstract: Model robustness is vital for the reliable deployment of machine learning models in real-world applications. Recent studies have shown that data augmentation can result in ... |
Title: Selectively Contextual Bandits Abstract: Contextual bandits are widely used in industrial personalization systems. These online learning frameworks learn a treatment assignment policy in the presence of treatment effects that vary with the observed contextual features of the users. While personalization creates ... |
Title: Towards a multi-stakeholder value-based assessment framework for algorithmic systems Abstract: In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes mostly focus on detecting harmful algorithmic biases. While these strategies have proven to be impactful, some values outlined i... |
Title: Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns Abstract: A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric di... |
Title: Image2Gif: Generating Continuous Realistic Animations with Warping NODEs Abstract: Generating smooth animations from a limited number of sequential observations has a number of applications in vision. For example, it can be used to increase number of frames per second, or generating a new trajectory only based o... |
Title: PinnerFormer: Sequence Modeling for User Representation at Pinterest Abstract: Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years. These approaches traditionally model a user's actions on a website as a sequence to predict the user's nex... |
Title: Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks Abstract: Since statistical guarantees for neural networks are usually restricted to global optima of intricate objective functions, it is not clear whether these theories really explain the performances of actual outputs of neura... |
Title: Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks Abstract: Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative in... |
Title: Insights into the origin of halo mass profiles from machine learning Abstract: The mass distribution of dark matter haloes is the result of the hierarchical growth of initial density perturbations through mass accretion and mergers. We use an interpretable machine-learning framework to provide physical insights ... |
Title: AdaCap: Adaptive Capacity control for Feed-Forward Neural Networks Abstract: The capacity of a ML model refers to the range of functions this model can approximate. It impacts both the complexity of the patterns a model can learn but also memorization, the ability of a model to fit arbitrary labels. We propose A... |
Title: Introspective Deep Metric Learning Abstract: This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods produce confident semantic distances between images regardless of the uncertainty level. However, we argue... |
Title: MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition Abstract: Automatic Facial Expression Recognition (FER) has attracted increasing attention in the last 20 years since facial expressions play a central role in human communication. Most FER methodologies utilize Deep Neural Networks (DNNs... |
Title: BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations Abstract: An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base. However, deploying a neural entity linking system for efficient real-time inference in productio... |
Title: Robustness of Humans and Machines on Object Recognition with Extreme Image Transformations Abstract: Recent neural network architectures have claimed to explain data from the human visual cortex. Their demonstrated performance is however still limited by the dependence on exploiting low-level features for solvin... |
Title: Accelerated Reinforcement Learning for Temporal Logic Control Objectives Abstract: This paper addresses the problem of learning control policies for mobile robots modeled as unknown Markov Decision Processes (MDPs) that are tasked with temporal logic missions, such as sequencing, coverage, or surveillance. The M... |
Title: Graph Neural Networks for Propositional Model Counting Abstract: Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks. Nevertheless, counting problems such as propositional model counting (#SAT) are still mostly approached with traditional solvers. Here we tackle thi... |
Title: NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality Abstract: Text to speech (TTS) has made rapid progress in both academia and industry in recent years. Some questions naturally arise that whether a TTS system can achieve human-level quality, how to define/judge that quality and how to a... |
Title: Model-Contrastive Learning for Backdoor Defense Abstract: Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Deep Neural Networks (DNNs) causing misclassification. Although there exist va... |
Title: EigenNoise: A Contrastive Prior to Warm-Start Representations Abstract: In this work, we present a naive initialization scheme for word vectors based on a dense, independent co-occurrence model and provide preliminary results that suggest it is competitive and warrants further investigation. Specifically, we dem... |
Title: Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning Abstract: Significant progress has been made on visual captioning, largely relying on pre-trained features and later fixed object detectors that serve as rich inputs to auto-regressive models. A key limitation of su... |
Title: Classification and mapping of low-statured 'shrubland' cover types in post-agricultural landscapes of the US Northeast Abstract: Context: Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northea... |
Title: Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers Abstract: Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and ... |
Title: Transfer Learning Based Efficient Traffic Prediction with Limited Training Data Abstract: Efficient prediction of internet traffic is an essential part of Self Organizing Network (SON) for ensuring proactive management. There are many existing solutions for internet traffic prediction with higher accuracy using ... |
Title: Fatigue Prediction in Outdoor Running Conditions using Audio Data Abstract: Although running is a common leisure activity and a core training regiment for several athletes, between $29\%$ and $79\%$ of runners sustain an overuse injury each year. These injuries are linked to excessive fatigue, which alters how s... |
Title: Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution Internet Traffic Prediction Abstract: Efficient prediction of internet traffic is essential for ensuring proactive management of computer networks. Nowadays, machine learning approaches show promising performance in modeling real-world complex t... |
Title: Protecting Data from all Parties: Combining FHE and DP in Federated Learning Abstract: This paper tackles the problem of ensuring training data privacy in a federated learning context. Relying on Homomorphic Encryption (HE) and Differential Privacy (DP), we propose a framework addressing threats on the privacy o... |
Title: Insights on Modelling Physiological, Appraisal, and Affective Indicators of Stress using Audio Features Abstract: Stress is a major threat to well-being that manifests in a variety of physiological and mental symptoms. Utilising speech samples collected while the subject is undergoing an induced stress episode h... |
Title: HierAttn: Effectively Learn Representations from Stage Attention and Branch Attention for Skin Lesions Diagnosis Abstract: Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin conditions and disorders. Visual features of skin lesions vary significantly bec... |
Title: Evaluating the Fairness Impact of Differentially Private Synthetic Data Abstract: Differentially private (DP) synthetic data is a promising approach to maximizing the utility of data containing sensitive information. Due to the suppression of underrepresented classes that is often required to achieve privacy, ho... |
Title: TGANet: Text-guided attention for improved polyp segmentation Abstract: Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep l... |
Title: Multi-segment preserving sampling for deep manifold sampler Abstract: Deep generative modeling for biological sequences presents a unique challenge in reconciling the bias-variance trade-off between explicit biological insight and model flexibility. The deep manifold sampler was recently proposed as a means to i... |
Title: Research on the correlation between text emotion mining and stock market based on deep learning Abstract: This paper discusses how to crawl the data of financial forums such as stock bar, and conduct emotional analysis combined with the in-depth learning model. This paper will use the Bert model to train the fin... |
Title: An Effective Scheme for Maize Disease Recognition based on Deep Networks Abstract: In the last decades, the area under cultivation of maize products has increased because of its essential role in the food cycle for humans, livestock, and poultry. Moreover, the diseases of plants impact food safety and can signif... |
Title: A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts Abstract: Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text. It is a field that is attracting attention due to the increasing accessibility of the Internet, which leads people to gen... |
Title: Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation Abstract: We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar pr... |
Title: EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization Abstract: In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck, and gradient compression is a w... |
Title: Residue-based Label Protection Mechanisms in Vertical Logistic Regression Abstract: Federated learning (FL) enables distributed participants to collaboratively learn a global model without revealing their private data to each other. Recently, vertical FL, where the participants hold the same set of samples but w... |
Title: Auto-SDE: Learning effective reduced dynamics from data-driven stochastic dynamical systems Abstract: Multiscale stochastic dynamical systems have been widely adopted to scientific and engineering problems due to their capability of depicting complex phenomena in many real world applications. This work is devote... |
Title: Verifying Integrity of Deep Ensemble Models by Lossless Black-box Watermarking with Sensitive Samples Abstract: With the widespread use of deep neural networks (DNNs) in many areas, more and more studies focus on protecting DNN models from intellectual property (IP) infringement. Many existing methods apply digi... |
Title: The Roles and Modes of Human Interactions with Automated Machine Learning Systems Abstract: As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the `how' and `why' of human-computer interaction (HCI) within these framework... |
Title: Btech thesis report on adversarial attack detection and purification of adverserially attacked images Abstract: This is Btech thesis report on detection and purification of adverserially attacked images. A deep learning model is trained on certain training examples for various tasks such as classification, regre... |
Title: Federated Multi-Armed Bandits Under Byzantine Attacks Abstract: Multi-armed bandits (MAB) is a simple reinforcement learning model where the learner controls the trade-off between exploration versus exploitation to maximize its cumulative reward. Federated multi-armed bandits (FMAB) is a recently emerging framew... |
Title: Quantum neural network autoencoder and classifier applied to an industrial case study Abstract: Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical use... |
Title: Predicting tacrolimus exposure in kidney transplanted patients using machine learning Abstract: Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid re... |
Title: Localized Adversarial Domain Generalization Abstract: Deep learning methods can struggle to handle domain shifts not seen in training data, which can cause them to not generalize well to unseen domains. This has led to research attention on domain generalization (DG), which aims to the model's generalization abi... |
Title: On Generalisability of Machine Learning-based Network Intrusion Detection Systems Abstract: Many of the proposed machine learning (ML) based network intrusion detection systems (NIDSs) achieve near perfect detection performance when evaluated on synthetic benchmark datasets. Though, there is no record of if and ... |
Title: SmoothNets: Optimizing CNN architecture design for differentially private deep learning Abstract: The arguably most widely employed algorithm to train deep neural networks with Differential Privacy is DPSGD, which requires clipping and noising of per-sample gradients. This introduces a reduction in model utility... |
Title: PS-Net: Deep Partially Separable Modelling for Dynamic Magnetic Resonance Imaging Abstract: Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. However, most of these methods represent low-rank prior by hand-crafted nuclear ... |
Title: Augmentations: An Insight into their Effectiveness on Convolution Neural Networks Abstract: Augmentations are the key factor in determining the performance of any neural network as they provide a model with a critical edge in boosting its performance. Their ability to boost a model's robustness depends on two fa... |
Title: Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images Abstract: Despite the plethora of successful Super-Resolution Reconstruction (SRR) models applied to natural images, their application to remote sensing imagery tends to produce poor results. Remote sensing imagery is oft... |
Title: Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks Abstract: We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries. This method is applied to Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays.... |
Title: Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI Abstract: Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography... |
Title: Deep Federated Anomaly Detection for Multivariate Time Series Data Abstract: Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivariate time series data are heterogeneously distributed amo... |
Title: Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models Abstract: Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visua... |
Title: Interpretable Machine Learning for Self-Service High-Risk Decision-Making Abstract: This paper contributes to interpretable machine learning via visual knowledge discovery in general line coordinates (GLC). The concepts of hyperblocks as interpretable dataset units and general line coordinates are combined to cr... |
Title: Posterior Collapse of a Linear Latent Variable Model Abstract: This work identifies the existence and cause of a type of posterior collapse that frequently occurs in the Bayesian deep learning practice. For a general linear latent variable model that includes linear variational autoencoders as a special case, we... |
Title: ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning Abstract: This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., featur... |
Title: Row-wise Accelerator for Vision Transformer Abstract: Following the success of the natural language processing, the transformer for vision applications has attracted significant attention in recent years due to its excellent performance. However, existing deep learning hardware accelerators for vision cannot exe... |
Title: A Real Time Super Resolution Accelerator with Tilted Layer Fusion Abstract: Deep learning based superresolution achieves high-quality results, but its heavy computational workload, large buffer, and high external memory bandwidth inhibit its usage in mobile devices. To solve the above issues, this paper proposes... |
Title: Hardware-Robust In-RRAM-Computing for Object Detection Abstract: In-memory computing is becoming a popular architecture for deep-learning hardware accelerators recently due to its highly parallel computing, low power, and low area cost. However, in-RRAM computing (IRC) suffered from large device variation and nu... |
Title: Predicting parametric spatiotemporal dynamics by multi-resolution PDE structure-preserved deep learning Abstract: Although recent advances in deep learning (DL) have shown a great promise for learning physics exhibiting complex spatiotemporal dynamics, the high training cost, unsatisfying extrapolability for lon... |
Title: Methodology to Create Analysis-Naive Holdout Records as well as Train and Test Records for Machine Learning Analyses in Healthcare Abstract: It is common for researchers to holdout data from a study pool to be used for external validation as well as for future research, and the same desire is true to those using... |
Title: Building Machine Translation Systems for the Next Thousand Languages Abstract: In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results in three research domains: (i) Building clean, web-mine... |
Title: A Structured Span Selector Abstract: Many natural language processing tasks, e.g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them. A typical approach to such tasks is to score all possible spans and greedily select spans for task-specific downstrea... |
Title: SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection Abstract: Detecting human stress levels and emotional states with physiological body-worn sensors is a complex task, but one with many health-related benefits. Robustness to sensor measurement noise and energy efficiency... |
Title: Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning Abstract: Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely capt... |
Title: $α$NAS: Neural Architecture Search using Property Guided Synthesis Abstract: In the past few years, neural architecture search (NAS) has become an increasingly important tool within the deep learning community. Despite the many recent successes of NAS, however, most existing approaches operate within highly stru... |
Title: Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection Abstract: Machine Learning-based supervised approaches require highly customized and fine-tuned methodologies to deliver outstanding performance. This paper presents a dataset-driven design and performance evaluation of a... |
Title: Learning to Brachiate via Simplified Model Imitation Abstract: Brachiation is the primary form of locomotion for gibbons and siamangs, in which these primates swing from tree limb to tree limb using only their arms. It is challenging to control because of the limited control authority, the required advance plann... |
Title: Investigating Generalization by Controlling Normalized Margin Abstract: Weight norm $\|w\|$ and margin $\gamma$ participate in learning theory via the normalized margin $\gamma/\|w\|$. Since standard neural net optimizers do not control normalized margin, it is hard to test whether this quantity causally relates... |
Title: N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification Abstract: Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the re... |
Title: Unsupervised Discovery and Composition of Object Light Fields Abstract: Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding. Recent efforts have tackled unsupervised discovery of object-centric neural scene representations. Howev... |
Title: Online Algorithms with Multiple Predictions Abstract: This paper studies online algorithms augmented with multiple machine-learned predictions. While online algorithms augmented with a single prediction have been extensively studied in recent years, the literature for the multiple predictions setting is sparse. ... |
Title: Federated Random Reshuffling with Compression and Variance Reduction Abstract: Random Reshuffling (RR), which is a variant of Stochastic Gradient Descent (SGD) employing sampling without replacement, is an immensely popular method for training supervised machine learning models via empirical risk minimization. D... |
Title: Dynamic categories, dynamic operads: From deep learning to prediction markets Abstract: Natural organized systems adapt to internal and external pressures and this seems to happens all the way down. Wanting to think clearly about this idea motivates our paper, and so the idea is elaborated extensively in the int... |
Title: Decentralized Stochastic Optimization with Inherent Privacy Protection Abstract: Decentralized stochastic optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data usually contain sensitive information like ... |
Title: Multimodal Semi-Supervised Learning for Text Recognition Abstract: Until recently, the number of public real-world text images was insufficient for training scene text recognizers. Therefore, most modern training methods rely on synthetic data and operate in a fully supervised manner. Nevertheless, the amount of... |
Title: Neural Program Synthesis with Query Abstract: Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privilege... |
Title: On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models Abstract: Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation. This continues to be a significant area of interest with the rise of new st... |
Title: Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework Abstract: The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a data... |
Title: Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization Abstract: We propose a new microscopy simulation system that can depict atomistic models in a micrograph visual style, similar to results of physical electron microscopy imaging. This system is scalable, able to represent s... |
Title: SeqNet: An Efficient Neural Network for Automatic Malware Detection Abstract: Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical. However, existing deep learning detection models need manual feature engineering or require... |
Title: FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction Abstract: Deep learning is an important method for molecular design and exhibits considerable ability to predict molecular properties, including physicochemical, bioactive, and ADME/T (absorption, distribution, metabolism, ... |
Title: Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants Abstract: Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortali... |
Title: Some performance considerations when using multi-armed bandit algorithms in the presence of missing data Abstract: When using multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, the simplest approach is to ignore missing outcomes and continue to sample following ... |
Title: Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods Abstract: Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive performance in continuous control tasks. However, they still suffer two nontrivial obstacles, i.e., low sample efficiency and overest... |
Title: Data-Free Adversarial Knowledge Distillation for Graph Neural Networks Abstract: Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowledge distillation (KD) for GNNs has enabled remarkab... |
Title: Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing Abstract: To achieve communication-efficient federated multitask learning (FMTL), we propose an over-the-air FMTL (OAFMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fadi... |
Title: Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs Abstract: With the representation learning capability of the deep learning models, deep embedded multi-view clustering (MVC) achieves impressive performance in many scenarios and has become increasingly popular in recent y... |
Title: Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction Abstract: Designing robust and accurate prediction models has been a viable research area since a long time. While proponents of a well-functioning market predictors believe that it is difficult to accurately predict market prices but ... |
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