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Title: Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data Abstract: The foremost challenge to causal inference with real-world data is to handle the imbalance in the covariates with respect to different treatment options, caused by treatment selection bias. To addre... |
Title: Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms Abstract: Policy evaluation based on A/B testing has attracted considerable interest in digital marketing, but such evaluation in ride-sourcing platforms (e.g., Uber and Didi) is not well studied primarily due to the c... |
Title: Confident Neural Network Regression with Bootstrapped Deep Ensembles Abstract: With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. In this paper we present a computationally cheap extension of Deep Ensembles for a regression setting... |
Title: Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction Abstract: The precise estimate of remaining useful life (RUL) is vital for the prognostic analysis and predictive maintenance that can significantly reduce failure rate and maint... |
Title: A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients Abstract: We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN)... |
Title: MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate Abstract: The fluctuation effect of gradient expectation and variance caused by parameter update between consecutive iterations is neglected or confusing by current mainstream gradient optimization algorithms.Using ... |
Title: Transformation Coding: Simple Objectives for Equivariant Representations Abstract: We present a simple non-generative approach to deep representation learning that seeks equivariant deep embedding through simple objectives. In contrast to existing equivariant networks, our transformation coding approach does not... |
Title: Letters of the Alphabet: Discovering Natural Feature Sets Abstract: Deep learning networks find intricate features in large datasets using the backpropagation algorithm. This algorithm repeatedly adjusts the network connections.' weights and examining the "hidden" nodes behavior between the input and output laye... |
Title: EF-Train: Enable Efficient On-device CNN Training on FPGA Through Data Reshaping for Online Adaptation or Personalization Abstract: Conventionally, DNN models are trained once in the cloud and deployed in edge devices such as cars, robots, or unmanned aerial vehicles (UAVs) for real-time inference. However, ther... |
Title: A Survey of Vision-Language Pre-Trained Models Abstract: As Transformer evolved, pre-trained models have advanced at a breakneck pace in recent years. They have dominated the mainstream techniques in natural language processing (NLP) and computer vision (CV). How to adapt pre-training to the field of Vision-and-... |
Title: An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems Abstract: Express systems play important roles in modern major cities. Couriers serving for the express system pick up packages in certain areas of interest (AOI) during a specific time. However, future p... |
Title: Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning Abstract: Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages such as decentralization and privacy protection of raw data. However, there has been few research... |
Title: Single-Leg Revenue Management with Advice Abstract: Single-leg revenue management is a foundational problem of revenue management that has been particularly impactful in the airline and hotel industry: Given $n$ units of a resource, e.g. flight seats, and a stream of sequentially-arriving customers segmented by ... |
Title: Recognizing Concepts and Recognizing Musical Themes. A Quantum Semantic Analysis Abstract: How are abstract concepts and musical themes recognized on the basis of some previous experience? It is interesting to compare the different behaviors of human and of artificial intelligences with respect to this problem. ... |
Title: Gradient Based Activations for Accurate Bias-Free Learning Abstract: Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial learning. A discriminator is used to identify the bias attributes su... |
Title: Convex Loss Functions for Contextual Pricing with Observational Posted-Price Data Abstract: We study an off-policy contextual pricing problem where the seller has access to samples of prices which customers were previously offered, whether they purchased at that price, and auxiliary features describing the custo... |
Title: Subtyping brain diseases from imaging data Abstract: The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been two areas in which ... |
Title: Provably convergent quasistatic dynamics for mean-field two-player zero-sum games Abstract: In this paper, we study the problem of finding mixed Nash equilibrium for mean-field two-player zero-sum games. Solving this problem requires optimizing over two probability distributions. We consider a quasistatic Wasser... |
Title: Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations Abstract: In variational inference (VI), the marginal log-likelihood is estimated using the standard evidence lower bound (ELBO), or improved versions as the importance weighted ELBO (IWELBO). We propose the multiple importance sa... |
Title: Adaptive Cut Selection in Mixed-Integer Linear Programming Abstract: Cut selection is a subroutine used in all modern mixed-integer linear programming solvers with the goal of selecting a subset of generated cuts that induce optimal solver performance. These solvers have millions of parameter combinations, and s... |
Title: A framework for spatial heat risk assessment using a generalized similarity measure Abstract: In this study, we develop a novel framework to assess health risks due to heat hazards across various localities (zip codes) across the state of Maryland with the help of two commonly used indicators i.e. exposure and v... |
Title: Improving Classification Model Performance on Chest X-Rays through Lung Segmentation Abstract: Chest radiography is an effective screening tool for diagnosing pulmonary diseases. In computer-aided diagnosis, extracting the relevant region of interest, i.e., isolating the lung region of each radiography image, ca... |
Title: Wavebender GAN: An architecture for phonetically meaningful speech manipulation Abstract: Deep learning has revolutionised synthetic speech quality. However, it has thus far delivered little value to the speech science community. The new methods do not meet the controllability demands that practitioners in this ... |
Title: The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land Extraction from High-Resolution Remote Sensing Image Abstract: Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This report introduces our solution to the iFLYTEK challenge 2021 c... |
Title: Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE) Abstract: Artificial Intelligence (AI) development is inherently iterative and experimental. Over the course of normal development, especially with the advent of automated AI, hundreds or thousands of experiments are gene... |
Title: Temporal Subtyping of Alzheimer's Disease Using Medical Conditions Preceding Alzheimer's Disease Onset in Electronic Health Records Abstract: Subtyping of Alzheimer's disease (AD) can facilitate diagnosis, treatment, prognosis and disease management. It can also support the testing of new prevention and treatmen... |
Title: Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks Abstract: Many complex systems are composed of interacting parts, and the underlying laws are usually simple and universal. While graph neural networks provide a useful relational inductive bias for modeling such systems, generalizati... |
Title: Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret Abstract: We show that a version of the generalised information ratio of Lattimore and Gyorgy (2020) determines the asymptotic minimax regret for all finite-action partial monitoring games provided that (a) the standard definition o... |
Title: Statistical and Spatio-temporal Hand Gesture Features for Sign Language Recognition using the Leap Motion Sensor Abstract: In modern society, people should not be identified based on their disability, rather, it is environments that can disable people with impairments. Improvements to automatic Sign Language Rec... |
Title: Computing Multiple Image Reconstructions with a Single Hypernetwork Abstract: Deep learning based techniques achieve state-of-the-art results in a wide range of image reconstruction tasks like compressed sensing. These methods almost always have hyperparameters, such as the weight coefficients that balance the d... |
Title: Generating Synthetic Mobility Networks with Generative Adversarial Networks Abstract: The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In ... |
Title: Differentially Private Estimation of Heterogeneous Causal Effects Abstract: Estimating heterogeneous treatment effects in domains such as healthcare or social science often involves sensitive data where protecting privacy is important. We introduce a general meta-algorithm for estimating conditional average trea... |
Title: Approximate gradient ascent methods for distortion risk measures Abstract: We propose approximate gradient ascent algorithms for risk-sensitive reinforcement learning control problem in on-policy as well as off-policy settings. We consider episodic Markov decision processes, and model the risk using distortion r... |
Title: Wastewater Pipe Condition Rating Model Using K- Nearest Neighbors Abstract: Risk-based assessment in pipe condition mainly focuses on prioritizing the most critical assets by evaluating the risk of pipe failure. This paper's goal is to classify a comprehensive pipe rating model which is obtained based on a serie... |
Title: Reward-Free Policy Space Compression for Reinforcement Learning Abstract: In reinforcement learning, we encode the potential behaviors of an agent interacting with an environment into an infinite set of policies, the policy space, typically represented by a family of parametric functions. Dealing with such a pol... |
Title: StickyLand: Breaking the Linear Presentation of Computational Notebooks Abstract: How can we better organize code in computational notebooks? Notebooks have become a popular tool among data scientists, as they seamlessly weave text and code together, supporting users to rapidly iterate and document code experime... |
Title: Counterfactual Phenotyping with Censored Time-to-Events Abstract: Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring. Counterfactual reasoning in such scen... |
Title: Efficient and Differentiable Conformal Prediction with General Function Classes Abstract: Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties for learned prediction sets are \emph{valid ... |
Title: ReorientBot: Learning Object Reorientation for Specific-Posed Placement Abstract: Robots need the capability of placing objects in arbitrary, specific poses to rearrange the world and achieve various valuable tasks. Object reorientation plays a crucial role in this as objects may not initially be oriented such t... |
Title: Message passing all the way up Abstract: The message passing framework is the foundation of the immense success enjoyed by graph neural networks (GNNs) in recent years. In spite of its elegance, there exist many problems it provably cannot solve over given input graphs. This has led to a surge of research on goi... |
Title: Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks Abstract: Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Collaborative end-edge-cloud computing for deep learning provides a range of ... |
Title: A duality connecting neural network and cosmological dynamics Abstract: We demonstrate that the dynamics of neural networks trained with gradient descent and the dynamics of scalar fields in a flat, vacuum energy dominated Universe are structurally profoundly related. This duality provides the framework for syne... |
Title: Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks Abstract: Multiple Instance Learning (MIL) is a weakly supervised learning problem where the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised learning where each instance is assumed to be independ... |
Title: Continual Auxiliary Task Learning Abstract: Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems. A variety of off-policy learning algorithms have been developed to learn such predictions, but as yet there is little work on how to ada... |
Title: ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users Abstract: Recent advances have enabled automatic sound recognition systems for deaf and hard of hearing (DHH) users on mobile devices. However, these tools use pre-trained, generic sound recognition models, which ... |
Title: FlowSense: Monitoring Airflow in Building Ventilation Systems Using Audio Sensing Abstract: Proper indoor ventilation through buildings' heating, ventilation, and air conditioning (HVAC) systems has become an increasing public health concern that significantly impacts individuals' health and safety at home, work... |
Title: Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statistics Abstract: We propose an extended generalization of the pseudo Huber loss formulation. We show that using the log-exp transform together with the logistic function, we can create a loss which combines the desirable proper... |
Title: Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study Abstract: Many supervised machine learning frameworks have been proposed for disease classification using functional magnetic resonance imaging (fMRI) data, producing important biomarkers. More recently, data pooling has flourished, mak... |
Title: Parallel MCMC Without Embarrassing Failures Abstract: Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run in parallel on (sub)posteriors defined on data partitions. Then, a server combin... |
Title: Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet Abstract: Neural speech synthesis models can synthesize high quality speech but typically require a high computational complexity to do so. In previous work, we introduced LPCNet, which uses linear prediction to significantly reduce the ... |
Title: Multi-fidelity reinforcement learning framework for shape optimization Abstract: Deep reinforcement learning (DRL) is a promising outer-loop intelligence paradigm which can deploy problem solving strategies for complex tasks. Consequently, DRL has been utilized for several scientific applications, specifically i... |
Title: A New Generation of Perspective API: Efficient Multilingual Character-level Transformers Abstract: On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that enable a safer internet is an imp... |
Title: r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation Abstract: Grapheme-to-phoneme (G2P) conversion is the process of converting the written form of words to their pronunciations. It has an important role for text-to... |
Title: Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection Abstract: The goal of federated learning (FL) is to train one global model by aggregating model parameters updated independently on edge devices without accessing users' private data. However, FL is susceptible to backdoor at... |
Title: Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks Abstract: Deep neural networks (DNNs) are increasingly being used in a variety of traditional radiofrequency (RF) problems. Previous work has shown that while DNN classifiers are typically more accurate than tr... |
Title: Differentially Private Regression with Unbounded Covariates Abstract: We provide computationally efficient, differentially private algorithms for the classical regression settings of Least Squares Fitting, Binary Regression and Linear Regression with unbounded covariates. Prior to our work, privacy constraints i... |
Title: Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications Abstract: Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational p... |
Title: Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning Abstract: Indiscriminate data poisoning attacks are quite effective against supervised learning. However, not much is known about their impact on unsupervised contrastive learning (CL). This paper is the first to consider indiscriminate data p... |
Title: Label-Smoothed Backdoor Attack Abstract: By injecting a small number of poisoned samples into the training set, backdoor attacks aim to make the victim model produce designed outputs on any input injected with pre-designed backdoors. In order to achieve a high attack success rate using as few poisoned training s... |
Title: Study of Feature Importance for Quantum Machine Learning Models Abstract: Predictor importance is a crucial part of data preprocessing pipelines in classical and quantum machine learning (QML). This work presents the first study of its kind in which feature importance for QML models has been explored and contras... |
Title: Constant matters: Fine-grained Complexity of Differentially Private Continual Observation Abstract: We study fine-grained error bounds for differentially private algorithms for averaging and counting under continual observation. Our main insight is that the factorization mechanism when using lower-triangular mat... |
Title: Functional Parcellation of fMRI data using multistage k-means clustering Abstract: Purpose: Functional Magnetic Resonance Imaging (fMRI) data acquired through resting-state studies have been used to obtain information about the spontaneous activations inside the brain. One of the approaches for analysis and inte... |
Title: FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators Abstract: FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\circ}$ resoluti... |
Title: Early Stage Diabetes Prediction via Extreme Learning Machine Abstract: Diabetes is one of the chronic diseases that has been discovered for decades. However, several cases are diagnosed in their late stages. Every one in eleven of the world's adult population has diabetes. Forty-six percent of people with diabet... |
Title: Differentiable and Learnable Robot Models Abstract: Building differentiable simulations of physical processes has recently received an increasing amount of attention. Specifically, some efforts develop differentiable robotic physics engines motivated by the computational benefits of merging rigid body simulation... |
Title: No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling Abstract: For each of $T$ time steps, $m$ experts report probability distributions over $n$ outcomes; we wish to learn to aggregate these forecasts in a way that attains a no-regret guarantee. We focus on the fundamental and practical aggr... |
Title: Arbitrary Shape Text Detection using Transformers Abstract: Recent text detection frameworks require several handcrafted components such as anchor generation, non-maximum suppression (NMS), or multiple processing stages (e.g. label generation) to detect arbitrarily shaped text images. In contrast, we propose an ... |
Title: Model2Detector: Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps Abstract: Out-of-distribution detection is an important capability that has long eluded vanilla neural networks. Deep Neural networks (DNNs) tend to generate over-confident predictions when pre... |
Title: MLProxy: SLA-Aware Reverse Proxy for Machine Learning Inference Serving on Serverless Computing Platforms Abstract: Serving machine learning inference workloads on the cloud is still a challenging task on the production level. Optimal configuration of the inference workload to meet SLA requirements while optimiz... |
Title: A Bayesian Deep Learning Approach to Near-Term Climate Prediction Abstract: Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate pred... |
Title: Exploring Edge Disentanglement for Node Classification Abstract: Edges in real-world graphs are typically formed by a variety of factors and carry diverse relation semantics. For example, connections in a social network could indicate friendship, being colleagues, or living in the same neighborhood. However, the... |
Title: Learning Neural Networks under Input-Output Specifications Abstract: In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors. Our strategy is to find an inner approximation of the set of admissible policy parameters, which ... |
Title: Performance Modeling of Metric-Based Serverless Computing Platforms Abstract: Analytical performance models are very effective in ensuring the quality of service and cost of service deployment remain desirable under different conditions and workloads. While various analytical performance models have been propose... |
Title: Blockchain Framework for Artificial Intelligence Computation Abstract: Blockchain is an essentially distributed database recording all transactions or digital events among participating parties. Each transaction in the records is approved and verified by consensus of the participants in the system that requires ... |
Title: Margin-distancing for safe model explanation Abstract: The growing use of machine learning models in consequential settings has highlighted an important and seemingly irreconcilable tension between transparency and vulnerability to gaming. While this has sparked sizable debate in legal literature, there has been... |
Title: Designing Decision Support Systems for Emergency Response: Challenges and Opportunities Abstract: Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about... |
Title: NetRCA: An Effective Network Fault Cause Localization Algorithm Abstract: Localizing the root cause of network faults is crucial to network operation and maintenance. However, due to the complicated network architectures and wireless environments, as well as limited labeled data, accurately localizing the true r... |
Title: ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints Abstract: Robotic navigation has been approached as a problem of 3D reconstruction and planning, as well as an end-to-end learning problem. However, long-range navigation requires both planning and reasoning about local traversability, as well... |
Title: Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms Abstract: We consider the problem of quantizing a linear model learned from measurements $\mathbf{X} = \mathbf{W}\boldsymbol{\theta} + \mathbf{v}$. The model is constrained to be representable using only $dB$-bit... |
Title: Better Modelling Out-of-Distribution Regression on Distributed Acoustic Sensor Data Using Anchored Hidden State Mixup Abstract: Generalizing the application of machine learning models to situations where the statistical distribution of training and test data are different has been a complex problem. Our contribu... |
Title: Neural Generalised AutoRegressive Conditional Heteroskedasticity Abstract: We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. Neural GARCH is a neural network adaptation of the GARCH 1,1 model in the univariate case, and the diagonal BEKK 1,1 model in th... |
Title: Deep Recurrent Modelling of Granger Causality with Latent Confounding Abstract: Inferring causal relationships in observational time series data is an important task when interventions cannot be performed. Granger causality is a popular framework to infer potential causal mechanisms between different time series... |
Title: LPF-Defense: 3D Adversarial Defense based on Frequency Analysis Abstract: Although 3D point cloud classification has recently been widely deployed in different application scenarios, it is still very vulnerable to adversarial attacks. This increases the importance of robust training of 3D models in the face of a... |
Title: Classification of Computer Aided Engineering (CAE) Parts Using Graph Convolutional Networks Abstract: CAE engineers work with hundreds of parts spread across multiple body models. A Graph Convolutional Network (GCN) was used to develop a CAE parts classifier. As many as 866 distinct parts from a representative b... |
Title: Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring Abstract: In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multim... |
Title: Reinforcement Learning in Practice: Opportunities and Challenges Abstract: This article is a gentle discussion about the field of reinforcement learning in practice, about opportunities and challenges, touching a broad range of topics, with perspectives and without technical details. The article is based on both... |
Title: Exploring Classic Quantitative Strategies Abstract: The goal of this paper is to debunk and dispel the magic behind the black-box quantitative strategies. It aims to build a solid foundation on how and why the techniques work. This manuscript crystallizes this knowledge by deriving from simple intuitions, the ma... |
Title: Web of Scholars: A Scholar Knowledge Graph Abstract: In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provid... |
Title: Multivariate Quantile Function Forecaster Abstract: We propose Multivariate Quantile Function Forecaster (MQF$^2$), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting. Prior approaches are either autoregressive... |
Title: The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices Abstract: Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Fairness concerns gradually emerge in many applications, such as face re... |
Title: Absolute Zero-Shot Learning Abstract: Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i.e., training a classifier with zero real data. The key innovation is to involve a teacher model as the data safeguard to guide the A... |
Title: Efficient CDF Approximations for Normalizing Flows Abstract: Normalizing flows model a complex target distribution in terms of a bijective transform operating on a simple base distribution. As such, they enable tractable computation of a number of important statistical quantities, particularly likelihoods and sa... |
Title: Reinforcement Learning from Demonstrations by Novel Interactive Expert and Application to Automatic Berthing Control Systems for Unmanned Surface Vessel Abstract: In this paper, two novel practical methods of Reinforcement Learning from Demonstration (RLfD) are developed and applied to automatic berthing control... |
Title: Training Adaptive Reconstruction Networks for Inverse Problems Abstract: Neural networks are full of promises for the resolution of ill-posed inverse problems. In particular, physics informed learning approaches already seem to progressively gradually replace carefully hand-crafted reconstruction algorithms, for... |
Title: Energy-efficient Training of Distributed DNNs in the Mobile-edge-cloud Continuum Abstract: We address distributed machine learning in multi-tier (e.g., mobile-edge-cloud) networks where a heterogeneous set of nodes cooperate to perform a learning task. Due to the presence of multiple data sources and computation... |
Title: Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting Abstract: Transformer-based methods have shown great potential in long-term time series forecasting. However, most of these methods adopt the standard point-wise self-attention mechanism, which not ... |
Title: Deepfake Detection for Facial Images with Facemasks Abstract: Hyper-realistic face image generation and manipulation have givenrise to numerous unethical social issues, e.g., invasion of privacy,threat of security, and malicious political maneuvering, which re-sulted in the development of recent deepfake detecti... |
Title: Deep Graph Learning for Anomalous Citation Detection Abstract: Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, i.e., anomaly detection in a citation network. Citati... |
Title: Exploratory Methods for Relation Discovery in Archival Data Abstract: In this article we propose a holistic approach to discover relations in art historical communities and enrich historians' biographies and archival descriptions with graph patterns relevant to art historiographic enquiry. We use exploratory dat... |
Title: FastRPB: a Scalable Relative Positional Encoding for Long Sequence Tasks Abstract: Transformers achieve remarkable performance in various domains, including NLP, CV, audio processing, and graph analysis. However, they do not scale well on long sequence tasks due to their quadratic complexity w.r.t. the inputs le... |
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