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Title: "Is not the truth the truth?": Analyzing the Impact of User Validations for Bus In/Out Detection in Smartphone-based Surveys Abstract: Passenger flow allows the study of users' behavior through the public network and assists in designing new facilities and services. This flow is observed through interactions bet... |
Title: Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction and Multisensory Unsupervised Cause-effect Learning Abstract: Intelligent Transportation Systems (ITS) underpin the concept of Mobility as a Service (MaaS), which requires universal and seamless users' access across multiple public and priv... |
Title: A general framework for adaptive two-index fusion attribute weighted naive Bayes Abstract: Naive Bayes(NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independent assumption. Researchers have proposed many improved NB methods to alleviate this... |
Title: A Fair Empirical Risk Minimization with Generalized Entropy Abstract: Recently a parametric family of fairness metrics to quantify algorithmic fairness has been proposed based on generalized entropy which have been originally used in economics and public welfare. Since these metrics have several advantages such ... |
Title: Fine-grained TLS Services Classification with Reject Option Abstract: The recent success and proliferation of machine learning and deep learning have provided powerful tools, which are also utilized for encrypted traffic analysis, classification, and threat detection. These methods, neural networks in particular... |
Title: Can deep neural networks learn process model structure? An assessment framework and analysis Abstract: Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction... |
Title: Predicting the impact of treatments over time with uncertainty aware neural differential equations Abstract: Predicting the impact of treatments from observational data only still represents a majorchallenge despite recent significant advances in time series modeling. Treatment assignments are usually correlated... |
Title: Rare Gems: Finding Lottery Tickets at Initialization Abstract: Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming "train, prune, re-train" approach. Frankle & Carbin conjecture that we can avoid this by training "lottery tic... |
Title: A fair pricing model via adversarial learning Abstract: At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometr... |
Title: Learning to Merge Tokens in Vision Transformers Abstract: Transformers are widely applied to solve natural language understanding and computer vision tasks. While scaling up these architectures leads to improved performance, it often comes at the expense of much higher computational costs. In order for large-sca... |
Title: Counterfactual Explanations for Predictive Business Process Monitoring Abstract: Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of int... |
Title: Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy Abstract: Neonatal seizure detection algorithms (SDA) are approaching the benchmark of human expert annotation. Measures of algorithm generalizability and non-inferiority as well as measures o... |
Title: Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing Abstract: This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flie... |
Title: Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge Abstract: Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and ch... |
Title: Self-Training: A Survey Abstract: In recent years, semi-supervised algorithms have received a lot of interest in both academia and industry. Among the existing techniques, self-training methods have arguably received more attention in the last few years. These models are designed to search the decision boundary ... |
Title: Exploring the Unfairness of DP-SGD Across Settings Abstract: End users and regulators require private and fair artificial intelligence models, but previous work suggests these objectives may be at odds. We use the CivilComments to evaluate the impact of applying the {\em de facto} standard approach to privacy, D... |
Title: Interfering Paths in Decision Trees: A Note on Deodata Predictors Abstract: A technique for improving the prediction accuracy of decision trees is proposed. It consists in evaluating the tree's branches in parallel over multiple paths. The technique enables predictions that are more aligned with the ones generat... |
Title: Activation Functions: Dive into an optimal activation function Abstract: Activation functions have come up as one of the essential components of neural networks. The choice of adequate activation function can impact the accuracy of these methods. In this study, we experiment for finding an optimal activation fun... |
Title: Investigating the Use of One-Class Support Vector Machine for Software Defect Prediction Abstract: Early software defect identification is considered an important step towards software quality assurance. Software defect prediction aims at identifying software components that are likely to cause faults before a s... |
Title: SQuadMDS: a lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP Abstract: Multidimensional scaling is a statistical process that aims to embed high dimensional data into a lower-dimensional space; this process is often used for the purpose of data visuali... |
Title: A Transformer-based Network for Deformable Medical Image Registration Abstract: Deformable medical image registration plays an important role in clinical diagnosis and treatment. Recently, the deep learning (DL) based image registration methods have been widely investigated and showed excellent performance in co... |
Title: Optimal Learning Rates of Deep Convolutional Neural Networks: Additive Ridge Functions Abstract: Convolutional neural networks have shown extraordinary abilities in many applications, especially those related to the classification tasks. However, for the regression problem, the abilities of convolutional structu... |
Title: Temporal Convolution Domain Adaptation Learning for Crops Growth Prediction Abstract: Existing Deep Neural Nets on crops growth prediction mostly rely on availability of a large amount of data. In practice, it is difficult to collect enough high-quality data to utilize the full potential of these deep learning m... |
Title: Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques Abstract: Deep Learning (DL) has revolutionized the capabilities of vision-based systems (VBS) in critical applications such as autonomous driving, robotic surgery, critical infrastructure surveillance, air and maritime traffi... |
Title: Tighter Expected Generalization Error Bounds via Convexity of Information Measures Abstract: Generalization error bounds are essential to understanding machine learning algorithms. This paper presents novel expected generalization error upper bounds based on the average joint distribution between the output hypo... |
Title: Towards Effective and Robust Neural Trojan Defenses via Input Filtering Abstract: Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger and targeting only one class to using multiple, inpu... |
Title: Measuring CLEVRness: Blackbox testing of Visual Reasoning Models Abstract: How can we measure the reasoning capabilities of intelligence systems? Visual question answering provides a convenient framework for testing the model's abilities by interrogating the model through questions about the scene. However, desp... |
Title: Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech Abstract: In this paper, we introduce a novel language identification system based on conformer layers. We propose an attentive temporal pooling mechanism to allow the model to carry information in long-form audi... |
Title: Attention Enables Zero Approximation Error Abstract: Deep learning models have been widely applied in various aspects of daily life. Many variant models based on deep learning structures have achieved even better performances. Attention-based architectures have become almost ubiquitous in deep learning structure... |
Title: Closing the Gap between Single-User and Multi-User VoiceFilter-Lite Abstract: VoiceFilter-Lite is a speaker-conditioned voice separation model that plays a crucial role in improving speech recognition and speaker verification by suppressing overlapping speech from non-target speakers. However, one limitation of ... |
Title: Overcoming a Theoretical Limitation of Self-Attention Abstract: Although transformers are remarkably effective for many tasks, there are some surprisingly easy-looking regular languages that they struggle with. Hahn shows that for languages where acceptance depends on a single input symbol, a transformer's class... |
Title: Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share? Abstract: In the early stages of human life, babies develop their skills by exploring different scenarios motivated by their inherent satisfaction rather than by extrinsic rewards fr... |
Title: Clarifying MCMC-based training of modern EBMs : Contrastive Divergence versus Maximum Likelihood Abstract: The Energy-Based Model (EBM) framework is a very general approach to generative modeling that tries to learn and exploit probability distributions only defined though unnormalized scores. It has risen in po... |
Title: Quantum Deep Reinforcement Learning for Robot Navigation Tasks Abstract: In this work, we utilize Quantum Deep Reinforcement Learning as method to learn navigation tasks for a simple, wheeled robot in three simulated environments of increasing complexity. We show similar performance of a parameterized quantum ci... |
Title: Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence Abstract: NDCG, namely Normalized Discounted Cumulative Gain, is a widely used ranking metric in information retrieval and machine learning. However, efficient and provable stochastic methods for maximizing NDCG ar... |
Title: Sequential asset ranking in nonstationary time series Abstract: We extend the research into cross-sectional momentum trading strategies. Our main result is our novel ranking algorithm, the naive Bayes asset ranker (nbar), which we use to select subsets of assets to trade from the S&P 500 index. We perform featur... |
Title: Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review Abstract: Advocates for Neuro-Symbolic AI (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is genera... |
Title: BERTVision -- A Parameter-Efficient Approach for Question Answering Abstract: We present a highly parameter efficient approach for Question Answering that significantly reduces the need for extended BERT fine-tuning. Our method uses information from the hidden state activations of each BERT transformer layer, wh... |
Title: Debugging Differential Privacy: A Case Study for Privacy Auditing Abstract: Differential Privacy can provide provable privacy guarantees for training data in machine learning. However, the presence of proofs does not preclude the presence of errors. Inspired by recent advances in auditing which have been used fo... |
Title: An optimal scheduled learning rate for a randomized Kaczmarz algorithm Abstract: We study how the learning rate affects the performance of a relaxed randomized Kaczmarz algorithm for solving $A x \approx b + \varepsilon$, where $A x =b$ is a consistent linear system and $\varepsilon$ has independent mean zero ra... |
Title: Sample Efficiency of Data Augmentation Consistency Regularization Abstract: Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to use augmented data. In this paper, we take a step in this... |
Title: Bounding Membership Inference Abstract: Differential Privacy (DP) is the de facto standard for reasoning about the privacy guarantees of a training algorithm. Despite the empirical observation that DP reduces the vulnerability of models to existing membership inference (MI) attacks, a theoretical underpinning as... |
Title: A comparative study of in-air trajectories at short and long distances in online handwriting Abstract: Introduction Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications wher... |
Title: On-line signature verification system with failure to enroll managing Abstract: In this paper we simulate a real biometric verification system based on on-line signatures. For this purpose we have split the MCYT signature database in three subsets: one for classifier training, another for system adjustment and a... |
Title: Flat latent manifolds for music improvisation between human and machine Abstract: The use of machine learning in artistic music generation leads to controversial discussions of the quality of art, for which objective quantification is nonsensical. We therefore consider a music-generating algorithm as a counterpa... |
Title: EMOTHAW: A novel database for emotional state recognition from handwriting Abstract: The detection of negative emotions through daily activities such as handwriting is useful for promoting well-being. The spread of human-machine interfaces such as tablets makes the collection of handwriting samples easier. In th... |
Title: Exact Community Recovery over Signed Graphs Abstract: Signed graphs encode similarity and dissimilarity relationships among different entities with positive and negative edges. In this paper, we study the problem of community recovery over signed graphs generated by the signed stochastic block model (SSBM) with ... |
Title: Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature Abstract: In this research, dew point temperature (DPT) is simulated using the data-driven approach. Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized as a... |
Title: A Perceptual Measure for Evaluating the Resynthesis of Automatic Music Transcriptions Abstract: This study focuses on the perception of music performances when contextual factors, such as room acoustics and instrument, change. We propose to distinguish the concept of "performance" from the one of "interpretation... |
Title: On the Omnipresence of Spurious Local Minima in Certain Neural Network Training Problems Abstract: We study the loss landscape of training problems for deep artificial neural networks with a one-dimensional real output whose activation functions contain an affine segment and whose hidden layers have width at lea... |
Title: Effect Identification in Cluster Causal Diagrams Abstract: One pervasive task found throughout the empirical sciences is to determine the effect of interventions from non-experimental data. It is well-understood that assumptions are necessary to perform causal inferences, which are commonly articulated through c... |
Title: Clustering Edges in Directed Graphs Abstract: How do vertices exert influence in graph data? We develop a framework for edge clustering, a new method for exploratory data analysis that reveals how both vertices and edges collaboratively accomplish directed influence in graphs, especially for directed graphs. In ... |
Title: Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images Abstract: In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolut... |
Title: Systematic review of deep learning and machine learning for building energy Abstract: The building energy (BE) management has an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumpti... |
Title: Evaluating Feature Attribution Methods in the Image Domain Abstract: Feature attribution maps are a popular approach to highlight the most important pixels in an image for a given prediction of a model. Despite a recent growth in popularity and available methods, little attention is given to the objective evalua... |
Title: Partitioned Variational Inference: A Framework for Probabilistic Federated Learning Abstract: The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional algorithms for training such models often ... |
Title: On the influence of roundoff errors on the convergence of the gradient descent method with low-precision floating-point computation Abstract: The employment of stochastic rounding schemes helps prevent stagnation of convergence, due to vanishing gradient effect when implementing the gradient descent method in lo... |
Title: Solving optimization problems with Blackwell approachability Abstract: We introduce the Conic Blackwell Algorithm$^+$ (CBA$^+$) regret minimizer, a new parameter- and scale-free regret minimizer for general convex sets. CBA$^+$ is based on Blackwell approachability and attains $O(\sqrt{T})$ regret. We show how t... |
Title: Learning Stochastic Dynamics with Statistics-Informed Neural Network Abstract: We introduce a machine-learning framework named statistics-informed neural network (SINN) for learning stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic... |
Title: Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation Abstract: Convolutional Neural Networks (CNNs) with U-shaped architectures have dominated medical image segmentation, which is crucial for various clinical purposes. However, the inherent locality of convolution make... |
Title: Embedded Ensembles: Infinite Width Limit and Operating Regimes Abstract: A memory efficient approach to ensembling neural networks is to share most weights among the ensembled models by means of a single reference network. We refer to this strategy as Embedded Ensembling (EE); its particular examples are BatchEn... |
Title: Capturing Failures of Large Language Models via Human Cognitive Biases Abstract: Large language models generate complex, open-ended outputs: instead of outputting a single class, they can write summaries, generate dialogue, and produce working code. In order to study the reliability of these open-ended systems, ... |
Title: Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph Abstract: This paper addresses the unsupervised learning of content-style decomposed representation. We first give a definition of style and then model the content-style representation as a token-level bipartite graph. An unsupervi... |
Title: AutoIP: A United Framework to Integrate Physics into Gaussian Processes Abstract: Physics modeling is critical for modern science and engineering applications. From data science perspective, physics knowledge -- often expressed as differential equations -- is valuable in that it is highly complementary to data, ... |
Title: Physics solutions for machine learning privacy leaks Abstract: Machine learning systems are becoming more and more ubiquitous in increasingly complex areas, including cutting-edge scientific research. The opposite is also true: the interest in better understanding the inner workings of machine learning systems m... |
Title: Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition Abstract: Children's automatic speech recognition (ASR) is always difficult due to, in part, the data scarcity problem, especially for kindergarten-aged kids. When data are scarce, the model might overfit to the tr... |
Title: Cutting Some Slack for SGD with Adaptive Polyak Stepsizes Abstract: Tuning the step size of stochastic gradient descent is tedious and error prone. This has motivated the development of methods that automatically adapt the step size using readily available information. In this paper, we consider the family of SP... |
Title: Bayesian Deep Learning for Graphs Abstract: The adaptive processing of structured data is a long-standing research topic in machine learning that investigates how to automatically learn a mapping from a structured input to outputs of various nature. Recently, there has been an increasing interest in the adaptive... |
Title: Estimators of Entropy and Information via Inference in Probabilistic Models Abstract: Estimating information-theoretic quantities such as entropy and mutual information is central to many problems in statistics and machine learning, but challenging in high dimensions. This paper presents estimators of entropy vi... |
Title: On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression Abstract: Monocular depth is important in many tasks, such as 3D reconstruction and autonomous driving. Deep learning based models achieve state-of-the-art performance in this field. A set of novel approac... |
Title: Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs Abstract: Proper orthogonal decomposition (POD) allows reduced-order modeling of complex dynamical systems at a substantial level, while maintaining a high degree of accuracy in modeling the underlying dynamical systems. Advances in machine learning a... |
Title: Highly-Efficient Binary Neural Networks for Visual Place Recognition Abstract: VPR is a fundamental task for autonomous navigation as it enables a robot to localize itself in the workspace when a known location is detected. Although accuracy is an essential requirement for a VPR technique, computational and ener... |
Title: Deep Learning to advance the Eigenspace Perturbation Method for Turbulence Model Uncertainty Quantification Abstract: The Reynolds Averaged Navier Stokes (RANS) models are the most common form of model in turbulence simulations. They are used to calculate Reynolds stress tensor and give robust results for engine... |
Title: Learning to Combine Instructions in LLVM Compiler Abstract: Instruction combiner (IC) is a critical compiler optimization pass, which replaces a sequence of instructions with an equivalent and optimized instruction sequence at basic block level. There can be thousands of instruction-combining patterns which need... |
Title: Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning Abstract: Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of polariza... |
Title: Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance Abstract: In this paper, we study contrastive learning from an optimization perspective, aiming to analyze and address a fundamental issue of existing contrastive learning methods that either rely on a large b... |
Title: Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications Abstract: This paper studies stochastic optimization for a sum of compositional functions, where the inner-level function of each summand is coupled with the corresponding summation index. We refer to this family of problems as fin... |
Title: The rise of the lottery heroes: why zero-shot pruning is hard Abstract: Recent advances in deep learning optimization showed that just a subset of parameters are really necessary to successfully train a model. Potentially, such a discovery has broad impact from the theory to application; however, it is known tha... |
Title: Learning Transferable Reward for Query Object Localization with Policy Adaptation Abstract: We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal form... |
Title: Exploiting Problem Structure in Deep Declarative Networks: Two Case Studies Abstract: Deep declarative networks and other recent related works have shown how to differentiate the solution map of a (continuous) parametrized optimization problem, opening up the possibility of embedding mathematical optimization pr... |
Title: Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration Abstract: Diverse data augmentation strategies are a natural approach to improving robustness in computer vision models against unforeseen shifts in data distribution. However, the ability to tailor such strategies to inoculate a mod... |
Title: Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement Abstract: Malicious accounts spreading misinformation has led to widespread false and misleading narratives in recent times, especially during the COVID-19 pandemic, and social media platforms struggle ... |
Title: Microgrid Day-Ahead Scheduling Considering Neural Network based Battery Degradation Model Abstract: Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation. Degradation is un-preventable for batteries such as the most popular Lithium-ion battery (LiB). The m... |
Title: Optimal channel selection with discrete QCQP Abstract: Reducing the high computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments. We first show the greedy approach of recent channel pruning methods ignores the inherent quadratic coupli... |
Title: Standard Deviation-Based Quantization for Deep Neural Networks Abstract: Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices. Inspired by existing methods, we propose a new framework to learn the quan... |
Title: BagPipe: Accelerating Deep Recommendation Model Training Abstract: Deep learning based recommendation models (DLRM) are widely used in several business critical applications. Training such recommendation models efficiently is challenging primarily because they consist of billions of embedding-based parameters wh... |
Title: Thompson Sampling with Unrestricted Delays Abstract: We investigate properties of Thompson Sampling in the stochastic multi-armed bandit problem with delayed feedback. In a setting with i.i.d delays, we establish to our knowledge the first regret bounds for Thompson Sampling with arbitrary delay distributions, i... |
Title: Understanding Adversarial Robustness from Feature Maps of Convolutional Layers Abstract: The adversarial robustness of a neural network mainly relies on two factors, one is the feature representation capacity of the network, and the other is its resistance ability to perturbations. In this paper, we study the an... |
Title: Learning Invariant Weights in Neural Networks Abstract: Assumptions about invariances or symmetries in data can significantly increase the predictive power of statistical models. Many commonly used models in machine learning are constraint to respect certain symmetries in the data, such as translation equivarian... |
Title: On Learning and Testing of Counterfactual Fairness through Data Preprocessing Abstract: Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness int... |
Title: Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networks Abstract: We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron (MLP), for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuou... |
Title: Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach Abstract: Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data. With respect to ethical guidelines, FL is promising regarding privacy, but needs to excel vis-\`a-vis ... |
Title: Ensemble Method for Estimating Individualized Treatment Effects Abstract: In many medical and business applications, researchers are interested in estimating individualized treatment effects using data from a randomized experiment. For example in medical applications, doctors learn the treatment effects from cli... |
Title: Directed Graph Auto-Encoders Abstract: We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns pairs of interpretable latent representations for the nodes of directed graphs, and uses param... |
Title: MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs Abstract: Ventricular arrhythmias (VA) are the main causes of sudden cardiac death. Developing machine learning methods for detecting VA based on electrocardiograms (ECGs) can help sav... |
Title: Human-Centered Concept Explanations for Neural Networks Abstract: Understanding complex machine learning models such as deep neural networks with explanations is crucial in various applications. Many explanations stem from the model perspective, and may not necessarily effectively communicate why the model is ma... |
Title: Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN Abstract: Depression is increasingly impacting individuals both physically and psychologically worldwide. It has become a global major public health problem and attracts attention from v... |
Title: Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection Abstract: We present an end-to-end differentiable neural network architecture to perform anomaly detection in multivariate time series by incorporating a Sequential Probability Ratio Test on the prediction residual. The architecture is a casca... |
Title: Learning ECG Representations based on Manipulated Temporal-Spatial Reverse Detection Abstract: Learning representations from electrocardiogram (ECG) serves as a fundamental step for many downstream machine learning-based ECG analysis tasks. However, the learning process is always restricted by lack of high-quali... |
Title: Bidding Agent Design in the LinkedIn Ad Marketplace Abstract: We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces. It optimizes solely for the buyer's interest and is agnostic to the auction mechanism imposed by the seller. As a result, the frame... |
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