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Title: Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks Abstract: Deep neural networks have become an integral part of our software infrastructure and are being deployed in many widely-used and safety-critical applications. However, their integration into many systems also brings with it the vuln... |
Title: Neural Collapse Inspired Attraction-Repulsion-Balanced Loss for Imbalanced Learning Abstract: Class imbalance distribution widely exists in real-world engineering. However, the mainstream optimization algorithms that seek to minimize error will trap the deep learning model in sub-optimums when facing extreme cla... |
Title: Table-based Fact Verification with Self-adaptive Mixture of Experts Abstract: The table-based fact verification task has recently gained widespread attention and yet remains to be a very challenging problem. It inherently requires informative reasoning over natural language together with different numerical and ... |
Title: Binary Multi Channel Morphological Neural Network Abstract: Neural networks and particularly Deep learning have been comparatively little studied from the theoretical point of view. Conversely, Mathematical Morphology is a discipline with solid theoretical foundations. We combine these domains to propose a new t... |
Title: GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning Abstract: Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only consider pair-wise interac... |
Title: EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting Abstract: Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, ti... |
Title: LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations Abstract: The problem of processing very long time-series data (e.g., a length of more than 10,000) is a long-standing research problem in machine learning. Recently, one breakthrough, called neural rough differential equat... |
Title: ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models Abstract: Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to ... |
Title: Making Progress Based on False Discoveries Abstract: We consider the question of adaptive data analysis within the framework of convex optimization. We ask how many samples are needed in order to compute $\epsilon$-accurate estimates of $O(1/\epsilon^2)$ gradients queried by gradient descent, and we provide two ... |
Title: Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift Abstract: In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological cla... |
Title: System Analysis for Responsible Design of Modern AI/ML Systems Abstract: The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years. We posit that the traditional system analysis perspective is needed when designing and implementing ML algorithms and... |
Title: Rumor Detection with Self-supervised Learning on Texts and Social Graph Abstract: Rumor detection has become an emerging and active research field in recent years. At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns ... |
Title: Compressed Empirical Measures (in finite dimensions) Abstract: We study approaches for compressing the empirical measure in the context of finite dimensional reproducing kernel Hilbert spaces (RKHSs).In this context, the empirical measure is contained within a natural convex set and can be approximated using con... |
Title: Antipatterns in Software Classification Taxonomies Abstract: Empirical results in software engineering have long started to show that findings are unlikely to be applicable to all software systems, or any domain: results need to be evaluated in specified contexts, and limited to the type of systems that they wer... |
Title: GestureLens: Visual Analysis of Gestures in Presentation Videos Abstract: Appropriate gestures can enhance message delivery and audience engagement in both daily communication and public presentations. In this paper, we contribute a visual analytic approach that assists professional public speaking coaches in im... |
Title: Feature Structure Distillation for BERT Transferring Abstract: Knowledge distillation is an approach to transfer information on representations from a teacher to a student by reducing their difference. A challenge of this approach is to reduce the flexibility of the student's representations inducing inaccurate ... |
Title: Learning heuristics for A* Abstract: Path finding in graphs is one of the most studied classes of problems in computer science. In this context, search algorithms are often extended with heuristics for a more efficient search of target nodes. In this work we combine recent advancements in Neural Algorithmic Reas... |
Title: CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex Abstract: CodexDB is an SQL processing engine whose internals can be customized via natural language instructions. CodexDB is based on OpenAI's GPT-3 Codex model which translates text into code. It is a framework on top of GPT-3 Codex that dec... |
Title: Missingness Bias in Model Debugging Abstract: Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools. However, in computer vision, pixels cannot simply be removed from an image. One thus tends to resort to heuristics such as blacking out pixels, which may in... |
Title: Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity Abstract: Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest. Instead, it would be convenient to collect ... |
Title: COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation Abstract: We consider the offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost constraints, learning o... |
Title: When Is Partially Observable Reinforcement Learning Not Scary? Abstract: Applications of Reinforcement Learning (RL), in which agents learn to make a sequence of decisions despite lacking complete information about the latent states of the controlled system, that is, they act under partial observability of the s... |
Title: Deep learning based closed-loop optimization of geothermal reservoir production Abstract: To maximize the economic benefits of geothermal energy production, it is essential to optimize geothermal reservoir management strategies, in which geologic uncertainty should be considered. In this work, we propose a close... |
Title: CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis Abstract: In the realm of unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled approach for adapting the complexity of the model ... |
Title: Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones Abstract: Nowadays, due to the widespread use of smartphones in everyday life and the improvement of computational capabilities of these devices, many complex tasks can now be deployed on them. Concerning the need for continuous monitorin... |
Title: Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments Abstract: This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algorithm gene... |
Title: Benchmarking Domain Generalization on EEG-based Emotion Recognition Abstract: Electroencephalography (EEG) based emotion recognition has demonstrated tremendous improvement in recent years. Specifically, numerous domain adaptation (DA) algorithms have been exploited in the past five years to enhance the generali... |
Title: Hybrid Transformer Network for Different Horizons-based Enriched Wind Speed Forecasting Abstract: Highly accurate different horizon-based wind speed forecasting facilitates a better modern power system. This paper proposed a novel astute hybrid wind speed forecasting model and applied it to different horizons. T... |
Title: A stochastic Stein Variational Newton method Abstract: Stein variational gradient descent (SVGD) is a general-purpose optimization-based sampling algorithm that has recently exploded in popularity, but is limited by two issues: it is known to produce biased samples, and it can be slow to converge on complicated ... |
Title: Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering Abstract: Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is causing the widespread death of ash trees across Europe. Remote sensing hyperspectral images encode ... |
Title: Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework Abstract: The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive exploration of the vast chemical space is infeasible, dis... |
Title: SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-omics Integration Abstract: Integration of multi-omics data provides opportunities for revealing biological mechanisms related to certain phenotypes. We propose a novel method of multi-omics integration called supervised deep generalize... |
Title: Learning Theory of Mind via Dynamic Traits Attribution Abstract: Machine learning of Theory of Mind (ToM) is essential to build social agents that co-live with humans and other agents. This capacity, once acquired, will help machines infer the mental states of others from observed contextual action trajectories,... |
Title: House Price Prediction Based On Deep Learning Abstract: Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than "to live and work happily, to eat and dress comfortable". Today, more than 40 years after the reform and opening, people have basically solved the problem... |
Title: PR-DAD: Phase Retrieval Using Deep Auto-Decoders Abstract: Phase retrieval is a well known ill-posed inverse problem where one tries to recover images given only the magnitude values of their Fourier transform as input. In recent years, new algorithms based on deep learning have been proposed, providing breakthr... |
Title: Optimizing Tensor Network Contraction Using Reinforcement Learning Abstract: Quantum Computing (QC) stands to revolutionize computing, but is currently still limited. To develop and test quantum algorithms today, quantum circuits are often simulated on classical computers. Simulating a complex quantum circuit re... |
Title: Sampling Strategies for Static Powergrid Models Abstract: Machine learning and computational intelligence technologies gain more and more popularity as possible solution for issues related to the power grid. One of these issues, the power flow calculation, is an iterative method to compute the voltage magnitudes... |
Title: An unsupervised approach for semantic place annotation of trajectories based on the prior probability Abstract: Semantic place annotation can provide individual semantics, which can be of great help in the field of trajectory data mining. Most existing methods rely on annotated or external data and require retra... |
Title: Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion Abstract: The Bayesian information criterion (BIC), defined as the observed data log likelihood minus a penalty term based on the sample size $N$, is a popular model selection criterion for fa... |
Title: Indiscriminate Data Poisoning Attacks on Neural Networks Abstract: Data poisoning attacks, in which a malicious adversary aims to influence a model by injecting "poisoned" data into the training process, have attracted significant recent attention. In this work, we take a closer look at existing poisoning attack... |
Title: An improved central limit theorem and fast convergence rates for entropic transportation costs Abstract: We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost. This is the first result which allows for asymptotically valid ... |
Title: Sintel: A Machine Learning Framework to Extract Insights from Signals Abstract: The detection of anomalies in time series data is a critical task with many monitoring applications. Existing systems often fail to encompass an end-to-end detection process, to facilitate comparative analysis of various anomaly dete... |
Title: From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions Abstract: In commentary driving, drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understandi... |
Title: A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization Abstract: In this work we describe an Adaptive Regularization using Cubics (ARC) method for large-scale nonconvex unconstrained optimization using Limited-memory Quasi-Newton (LQN) matrices. ARC methods are a relative... |
Title: GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints Abstract: The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decisi... |
Title: Diverse Imagenet Models Transfer Better Abstract: A commonly accepted hypothesis is that models with higher accuracy on Imagenet perform better on other downstream tasks, leading to much research dedicated to optimizing Imagenet accuracy. Recently this hypothesis has been challenged by evidence showing that self... |
Title: Approximating Persistent Homology for Large Datasets Abstract: Persistent homology is an important methodology from topological data analysis which adapts theory from algebraic topology to data settings and has been successfully implemented in many applications. It produces a statistical summary in the form of a... |
Title: Multifidelity Deep Operator Networks Abstract: Operator learning for complex nonlinear operators is increasingly common in modeling physical systems. However, training machine learning methods to learn such operators requires a large amount of expensive, high-fidelity data. In this work, we present a composite D... |
Title: On the Representation Collapse of Sparse Mixture of Experts Abstract: Sparse mixture of experts provides larger model capacity while requiring a constant computational overhead. It employs the routing mechanism to distribute input tokens to the best-matched experts according to their hidden representations. Howe... |
Title: Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems Abstract: The growing complexity of Cyber-Physical Systems (CPS) and challenges in ensuring safety and security have led to the increasing use of deep learning methods for accurate and scalable anomaly detection. However,... |
Title: Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices Abstract: This study presents an Ordinal version of Residual Logit (Ordinal-ResLogit) model to investigate the ordinal responses. We integrate the standard ResLogit model into COnsistent RAnk Logits (CORAL) framework, classified as... |
Title: Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction Abstract: When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag. F... |
Title: Who Is Missing? Characterizing the Participation of Different Demographic Groups in a Korean Nationwide Daily Conversation Corpus Abstract: A conversation corpus is essential to build interactive AI applications. However, the demographic information of the participants in such corpora is largely underexplored ma... |
Title: Does Interference Exist When Training a Once-For-All Network? Abstract: The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Once trained, a subnet can be derived from the supernet (both ar... |
Title: Efficient Progressive High Dynamic Range Image Restoration via Attention and Alignment Network Abstract: HDR is an important part of computational photography technology. In this paper, we propose a lightweight neural network called Efficient Attention-and-alignment-guided Progressive Network (EAPNet) for the ch... |
Title: Reinforcement Learning with Intrinsic Affinity for Personalized Asset Management Abstract: The common purpose of applying reinforcement learning (RL) to asset management is the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other p... |
Title: K-LITE: Learning Transferable Visual Models with External Knowledge Abstract: Recent state-of-the-art computer vision systems are trained from natural language supervision, ranging from simple object category names to descriptive captions. This free form of supervision ensures high generality and usability of th... |
Title: Tight Last-Iterate Convergence of the Extragradient and the Optimistic Gradient Descent-Ascent Algorithm for Constrained Monotone Variational Inequalities Abstract: The monotone variational inequality is a central problem in mathematical programming that unifies and generalizes many important settings such as sm... |
Title: Scalable Motif Counting for Large-scale Temporal Graphs Abstract: One fundamental problem in temporal graph analysis is to count the occurrences of small connected subgraph patterns (i.e., motifs), which benefits a broad range of real-world applications, such as anomaly detection, structure prediction, and netwo... |
Title: DAME: Domain Adaptation for Matching Entities Abstract: Entity matching (EM) identifies data records that refer to the same real-world entity. Despite the effort in the past years to improve the performance in EM, the existing methods still require a huge amount of labeled data in each domain during the training... |
Title: Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence Abstract: We consider minimizing a smooth and strongly convex objective function using a stochastic Newton method. At each iteration, the algorithm is given an oracle access to a stochastic estimate of the Hessian matrix. The oracle ... |
Title: Uncertainty-based Cross-Modal Retrieval with Probabilistic Representations Abstract: Probabilistic embeddings have proven useful for capturing polysemous word meanings, as well as ambiguity in image matching. In this paper, we study the advantages of probabilistic embeddings in a cross-modal setting (i.e., text ... |
Title: A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond Abstract: Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. ... |
Title: Causality-based Neural Network Repair Abstract: Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need t... |
Title: Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics Abstract: Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related t... |
Title: Effects of Graph Convolutions in Deep Networks Abstract: Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the effects of graph convolutions in multi... |
Title: A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement Abstract: Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue. Uncertainty quantification is required for many applications, and disentangled aleatoric and epistemic uncertainties are best. In this pa... |
Title: Memory-Constrained Policy Optimization Abstract: We introduce a new constrained optimization method for policy gradient reinforcement learning, which uses two trust regions to regulate each policy update. In addition to using the proximity of one single old policy as the first trust region as done by prior works... |
Title: SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics Abstract: Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems. Howeve... |
Title: Self-supervised Learning for Sonar Image Classification Abstract: Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception ca... |
Title: Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database Abstract: Federated learning (FL) has been proposed as a method to train a model on different units without exchanging data. This offers great opportunities in the healthcare sector, where large datasets ar... |
Title: OutCast: Outdoor Single-image Relighting with Cast Shadows Abstract: We propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accounting for shading and global effects such the sun light color and... |
Title: Online Caching with no Regret: Optimistic Learning via Recommendations Abstract: The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic too... |
Title: Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection Abstract: We study the prediction of short term wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for those quantities are crucial to mitigate the negative effects of wind farm... |
Title: A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data Abstract: The variational autoencoder (VAE) is a popular deep latent variable model used to analyse high-dimensional datasets by learning a low-dimensional latent representation of the data. It simultaneously learns a generative model and... |
Title: Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation Abstract: Deep neural networks are highly susceptible to learning biases in visual data. While various methods have been proposed to mitigate such bias, the majority require explicit knowledge of the biases present in the training data in order to mi... |
Title: Adversarial Scratches: Deployable Attacks to CNN Classifiers Abstract: A growing body of work has shown that deep neural networks are susceptible to adversarial examples. These take the form of small perturbations applied to the model's input which lead to incorrect predictions. Unfortunately, most literature fo... |
Title: Case-Aware Adversarial Training Abstract: The neural network (NN) becomes one of the most heated type of models in various signal processing applications. However, NNs are extremely vulnerable to adversarial examples (AEs). To defend AEs, adversarial training (AT) is believed to be the most effective method whil... |
Title: A Probabilistic Time-Evolving Approach to Scanpath Prediction Abstract: Human visual attention is a complex phenomenon that has been studied for decades. Within it, the particular problem of scanpath prediction poses a challenge, particularly due to the inter- and intra-observer variability, among other reasons.... |
Title: Deep subspace encoders for continuous-time state-space identification Abstract: Continuous-time (CT) models have shown an improved sample efficiency during learning and enable ODE analysis methods for enhanced interpretability compared to discrete-time (DT) models. Even with numerous recent developments, the mul... |
Title: Generating 3D Molecules for Target Protein Binding Abstract: A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and effective framework, known as GraphBP, to generate 3D molecules that bind ... |
Title: Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning Abstract: Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments. However, due to the complexity of multi-... |
Title: SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics Abstract: Although Reinforcement Learning (RL) is effective for sequential decision-making problems under uncertainty, it still fails to thrive in real-world systems where risk or safety is a binding constraint. In this paper, we formulate... |
Title: HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation Abstract: Real-time 6D object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we pro... |
Title: Hephaestus: A large scale multitask dataset towards InSAR understanding Abstract: Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data. InSAR provides unique information on diverse geophysical processes and geology, a... |
Title: Search-based Methods for Multi-Cloud Configuration Abstract: Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in. While most cloud providers offer similar functionality, they may differ significantly in terms of performance and/or cost. A customer looking to ben... |
Title: Simplicial Attention Networks Abstract: Graph representation learning methods have mostly been limited to the modelling of node-wise interactions. Recently, there has been an increased interest in understanding how higher-order structures can be utilised to further enhance the learning abilities of graph neural ... |
Title: Noise mitigation strategies in physical feedforward neural networks Abstract: Physical neural networks are promising candidates for next generation artificial intelligence hardware. In such architectures, neurons and connections are physically realized and do not leverage digital concepts with their practically ... |
Title: Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability Abstract: In this paper, we study learning in probabilistic domains where the learner may receive incorrect labels but can improve the reliability of labels by repeatedly sampling them. In such a setting, one faces the pro... |
Title: Unsupervised Ranking and Aggregation of Label Descriptions for Zero-Shot Classifiers Abstract: Zero-shot text classifiers based on label descriptions embed an input text and a set of labels into the same space: measures such as cosine similarity can then be used to select the most similar label description to th... |
Title: Generalizing to the Future: Mitigating Entity Bias in Fake News Detection Abstract: The wide dissemination of fake news is increasingly threatening both individuals and society. Fake news detection aims to train a model on the past news and detect fake news of the future. Though great efforts have been made, exi... |
Title: Graph neural networks and attention-based CNN-LSTM for protein classification Abstract: This paper focuses on three critical problems on protein classification. Firstly, Carbohydrate-active enzyme (CAZyme) classification can help people to understand the properties of enzymes. However, one CAZyme may belong to s... |
Title: Identifying organizations receiving personal data in Android Apps Abstract: Many studies have demonstrated that mobile applications are common means to collect massive amounts of personal data. This goes unnoticed by most users, who are also unaware that many different organizations are receiving this data, even... |
Title: Backdooring Explainable Machine Learning Abstract: Explainable machine learning holds great potential for analyzing and understanding learning-based systems. These methods can, however, be manipulated to present unfaithful explanations, giving rise to powerful and stealthy adversaries. In this paper, we demonstr... |
Title: A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment Abstract: To facilitate the development of reinforcement learning (RL) based power distribution system Volt-VAR control (VVC), this paper introduces a suite of open-source datasets for RL-based VVC algorithm research that is sample e... |
Title: An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions Abstract: In this research paper, we study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history, leveraging a database of synthetic storm simulations... |
Title: UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples Abstract: A rising number of botnet families have been successfully detected using deep learning architectures. While the variety of attacks increases, these architectures should become more robust against attacks. They have b... |
Title: BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction Abstract: Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph classification is of crucial importan... |
Title: Physics-Informed Bayesian Learning of Electrohydrodynamic Polymer Jet Printing Dynamics Abstract: Calibration of highly dynamic multi-physics manufacturing processes such as electro-hydrodynamics-based additive manufacturing (AM) technologies (E-jet printing) is still performed by labor-intensive trial-and-error... |
Title: Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems Abstract: The real-world use cases of Machine Learning (ML) have exploded over the past few years. However, the current computing infrastructure is insufficient to support all real-world applications and scenarios... |
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