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Title: AiSocrates: Towards Answering Ethical Quandary Questions Abstract: Considerable advancements have been made in various NLP tasks based on the impressive power of large pre-trained language models (LLMs). These results have inspired efforts to understand the limits of LLMs so as to evaluate how far we are from ac... |
Title: Controlling chaotic itinerancy in laser dynamics for reinforcement learning Abstract: Photonic artificial intelligence has attracted considerable interest in accelerating machine learning; however, the unique optical properties have not been fully utilized for achieving higher-order functionalities. Chaotic itin... |
Title: GPN: A Joint Structural Learning Framework for Graph Neural Networks Abstract: Graph neural networks (GNNs) have been applied into a variety of graph tasks. Most existing work of GNNs is based on the assumption that the given graph data is optimal, while it is inevitable that there exists missing or incomplete e... |
Title: Subgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system Abstract: In recent years, sensors from smart consumer devices have shown great diagnostic potential in movement disorders. In this context, data modalities such as electronic questionnaires, hand movement and voice captures... |
Title: Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction Abstract: Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainl... |
Title: Near out-of-distribution detection for low-resolution radar micro-Doppler signatures Abstract: Near out-of-distribution detection (OOD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OOD use case for radar targets detection ... |
Title: Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs Abstract: We propose a generative model for text generation, which exhibits disentangled latent representations of syntax and semantics. Contrary to previous work, this model does not need syntactic infor... |
Title: Minimal Neural Network Models for Permutation Invariant Agents Abstract: Organisms in nature have evolved to exhibit flexibility in face of changes to the environment and/or to themselves. Artificial neural networks (ANNs) have proven useful for controlling of artificial agents acting in environments. However, m... |
Title: Ensemble Clustering via Co-association Matrix Self-enhancement Abstract: Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according t... |
Title: Feature and Instance Joint Selection: A Reinforcement Learning Perspective Abstract: Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards the joint selection conducts feature/instance se... |
Title: Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches Abstract: Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applica... |
Title: Comments on: "Hybrid Semiparametric Bayesian Networks" Abstract: Invited discussion on the paper "Hybrid Semiparametric Bayesian Networks" by David Atienza, Pedro Larranaga and Concha Bielza (TEST, 2022). |
Title: Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets Abstract: Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used for early Alzheimer's Disease (AD) detection. Methods: To interpret eXtreme Gradient Boosting (XG... |
Title: NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension Abstract: NER has been traditionally formulated as a sequence labeling task. However, there has been recent trend in posing NER as a machine reading comprehension task (Wang et al., 2020; Mengge et al., 2020), where e... |
Title: Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation Abstract: Non-contrast computed tomography (NCCT) is commonly acquired for lung cancer screening, assessment of general abdominal pain or suspected renal stones, trauma evaluation, and many other indications. However, the... |
Title: E-Mail Assistant -- Automation of E-Mail Handling and Management using Robotic Process Automation Abstract: In this paper, a workflow for designing a bot using Robotic Process Automation (RPA), associated with Artificial Intelligence (AI) that is used for information extraction, classification, etc., is proposed... |
Title: Training Uncertainty-Aware Classifiers with Conformalized Deep Learning Abstract: Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities. In particular, they tend to b... |
Title: Distinction Maximization Loss: Efficiently Improving Classification Accuracy, Uncertainty Estimation, and Out-of-Distribution Detection Simply Replacing the Loss and Calibrating Abstract: Building robust deterministic neural networks remains a challenge. On the one hand, some approaches improve out-of-distributi... |
Title: Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio Abstract: Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models that describes an observed sequence with dynamic latent variables and a static latent variable. The former en... |
Title: Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs Abstract: The optimization with orthogonality has been shown useful in training deep neural networks (DNNs). To impose orthogonality on DNNs, both computational efficiency and stability are important. However, existing metho... |
Title: Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control Abstract: This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the superposition property of the wireless medium, and performs model aggregation over the air for free. Thus, it can greatly reduce the commu... |
Title: A Survey of Risk-Aware Multi-Armed Bandits Abstract: In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aw... |
Title: Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images Abstract: Trained using only image class label, deep weakly supervised methods allow image classification and ROI segmentation for interpretability. Despite their success on natural images, they fa... |
Title: Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound Abstract: Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e.g., graph classification. The Gromov-Wasserstein (GW) discrepancy formulates a coupling between... |
Title: Cross-domain Few-shot Meta-learning Using Stacking Abstract: Cross-domain few-shot meta-learning (CDFSML) addresses learning problems where knowledge needs to be transferred from several source domains into an instance-scarce target domain with an explicitly different input distribution. Recently published CDFSM... |
Title: Privacy-Preserving Distributed Machine Learning Made Faster Abstract: With the development of machine learning, it is difficult for a single server to process all the data. So machine learning tasks need to be spread across multiple servers, turning the centralized machine learning into a distributed one. Howeve... |
Title: Representation Learning for Context-Dependent Decision-Making Abstract: Humans are capable of adjusting to changing environments flexibly and quickly. Empirical evidence has revealed that representation learning plays a crucial role in endowing humans with such a capability. Inspired by this observation, we stud... |
Title: Open Vocabulary Extreme Classification Using Generative Models Abstract: The extreme multi-label classification (XMC) task aims at tagging content with a subset of labels from an extremely large label set. The label vocabulary is typically defined in advance by domain experts and assumed to capture all necessary... |
Title: Performing Video Frame Prediction of Microbial Growth with a Recurrent Neural Network Abstract: A Recurrent Neural Network (RNN) was used to perform video frame prediction of microbial growth for a population of two mutants of Pseudomonas aeruginosa. The RNN was trained on videos of 20 frames that were acquired ... |
Title: Deep Learning of Chaotic Systems from Partially-Observed Data Abstract: Recently, a general data driven numerical framework has been developed for learning and modeling of unknown dynamical systems using fully- or partially-observed data. The method utilizes deep neural networks (DNNs) to construct a model for t... |
Title: Dimension-adaptive machine-learning-based quantum state reconstruction Abstract: We introduce an approach for performing quantum state reconstruction on systems of $n$ qubits using a machine-learning-based reconstruction system trained exclusively on $m$ qubits, where $m\geq n$. This approach removes the necessi... |
Title: Stochastic first-order methods for average-reward Markov decision processes Abstract: We study the problem of average-reward Markov decision processes (AMDPs) and develop novel first-order methods with strong theoretical guarantees for both policy evaluation and optimization. Existing on-policy evaluation method... |
Title: Algebraic Machine Learning with an Application to Chemistry Abstract: As datasets used in scientific applications become more complex, studying the geometry and topology of data has become an increasingly prevalent part of the data analysis process. This can be seen for example with the growing interest in topol... |
Title: Deep-Learned Generators of Porosity Distributions Produced During Metal Additive Manufacturing Abstract: Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. However, AM produced parts can b... |
Title: RITA: a Study on Scaling Up Generative Protein Sequence Models Abstract: In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database. Such generative models... |
Title: Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models Abstract: Bridging model-based safety and model-free reinforcement learning (RL) for dynamic robots is appealing since model-based methods are able to provide formal safety guarantees,... |
Title: Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II Abstract: Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of... |
Title: LSI: A Learned Secondary Index Structure Abstract: Learned index structures have been shown to achieve favorable lookup performance and space consumption compared to their traditional counterparts such as B-trees. However, most learned index studies have focused on the primary indexing setting, where the base da... |
Title: Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategy Abstract: Experience has shown that trading in stock and cryptocurrency markets has the potential to be highly profitable. In this light, considerable effort has been r... |
Title: Deep Learning and Synthetic Media Abstract: Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning - often subsumed colloquially under the label "deepfakes" - have a number of impressive characteristics; they are ... |
Title: Individual Fairness Guarantees for Neural Networks Abstract: We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs). In particular, we work with the $\epsilon$-$\delta$-IF formulation, which, given a NN and a similarity metric learnt from data, requires that the ... |
Title: Bias and Fairness on Multimodal Emotion Detection Algorithms Abstract: Numerous studies have shown that machine learning algorithms can latch onto protected attributes such as race and gender and generate predictions that systematically discriminate against one or more groups. To date the majority of bias and fa... |
Title: eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference Abstract: As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode. However, these GPS datasets may contain users' private information (e.g., home location), preventing m... |
Title: Visualization Guidelines for Model Performance Communication Between Data Scientists and Subject Matter Experts Abstract: Presenting the complexities of a model's performance is a communication bottleneck that threatens collaborations between data scientists and subject matter experts. Accuracy and error metrics... |
Title: Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots Abstract: Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the deployment of ML on res... |
Title: A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model Abstract: Based on the commentary data of the Shenzhen Stock Index bar on the EastMoney website from January 1, 2018 to December 31, 2019. This paper extracts the embedde... |
Title: Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks Abstract: Human language offers a powerful window into our thoughts -- we tell stories, give explanations, and express our beliefs and goals through words. Ab... |
Title: RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation Abstract: This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible. Existing solut... |
Title: RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization Abstract: This paper explores training efficient VGG-style super-resolution (SR) networks with the structural re-parameterization technique. The general pipeline of re-parameterization is to ... |
Title: Process, Bias and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning Abstract: Analog computing is attractive compared to digital computing due to its potential for achieving higher computational density and higher energy efficiency. However, unlike digital circuits, conventional analog com... |
Title: Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis Abstract: Advanced deep neural networks (DNNs), designed by either human or AutoML algorithms, are growing increasingly complex. Diverse operations are connected by complicated connectivity patterns, e.g., various types of skip c... |
Title: How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India Abstract: Accountability, a requisite for responsible AI, can be facilitated through transparency mechanisms such as audits and explainability. However, prior work su... |
Title: Ranked Prioritization of Groups in Combinatorial Bandit Allocation Abstract: Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have been used to allocate limited patrol resources, but exi... |
Title: The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization Abstract: In this paper, we revisit the smooth and strongly-convex-strongly-concave minimax optimization problem. Zhang et al. (2021) and Ibrahim et al. (2020) established the lower bound $\Omega\left(\sqrt{\kappa_x\... |
Title: Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification Abstract: As part of an automated fact-checking pipeline, the claim veracity classification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-evid... |
Title: Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning Abstract: Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL... |
Title: On Distributed Adaptive Optimization with Gradient Compression Abstract: We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. O... |
Title: Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification Abstract: With the increasing prevalence of encrypted network traffic, cyber security analysts have been turning to machine learning (ML) techniques to elucidate the traffic on their networks. However, M... |
Title: Quantum Self-Attention Neural Networks for Text Classification Abstract: An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing (NLP). Although some efforts based on syntactic analysis have ope... |
Title: Blockchain-based Secure Client Selection in Federated Learning Abstract: Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server... |
Title: A simple framework for contrastive learning phases of matter Abstract: A main task in condensed-matter physics is to recognize, classify, and characterize phases of matter and the corresponding phase transitions, for which machine learning provides a new class of research tools due to the remarkable development ... |
Title: RLOP: RL Methods in Option Pricing from a Mathematical Perspective Abstract: Abstract In this work, we build two environments, namely the modified QLBS and RLOP models, from a mathematics perspective which enables RL methods in option pricing through replicating by portfolio. We implement the environment specifi... |
Title: Predicting hot electrons free energies from ground-state data Abstract: Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally-excited el... |
Title: Characterizing the Action-Generalization Gap in Deep Q-Learning Abstract: We study the action generalization ability of deep Q-learning in discrete action spaces. Generalization is crucial for efficient reinforcement learning (RL) because it allows agents to use knowledge learned from past experiences on new tas... |
Title: Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation Abstract: Deep learning (DL) is gaining popularity as a parameter estimation method for quantitative MRI. A range of competing implementations have been proposed, relying on either supervised or self-supervi... |
Title: End-to-End Multi-Person Audio/Visual Automatic Speech Recognition Abstract: Traditionally, audio-visual automatic speech recognition has been studied under the assumption that the speaking face on the visual signal is the face matching the audio. However, in a more realistic setting, when multiple faces are pote... |
Title: A Closer Look at Audio-Visual Multi-Person Speech Recognition and Active Speaker Selection Abstract: Audio-visual automatic speech recognition is a promising approach to robust ASR under noisy conditions. However, up until recently it had been traditionally studied in isolation assuming the video of a single spe... |
Title: Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction Abstract: In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSN... |
Title: Improved Meta Learning for Low Resource Speech Recognition Abstract: We propose a new meta learning based framework for low resource speech recognition that improves the previous model agnostic meta learning (MAML) approach. The MAML is a simple yet powerful meta learning approach. However, the MAML presents som... |
Title: Scream Detection in Heavy Metal Music Abstract: Harsh vocal effects such as screams or growls are far more common in heavy metal vocals than the traditionally sung vocal. This paper explores the problem of detection and classification of extreme vocal techniques in heavy metal music, specifically the identificat... |
Title: Dual Branch Prior-SegNet: CNN for Interventional CBCT using Planning Scan and Auxiliary Segmentation Loss Abstract: This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan. An additional head learns to segment int... |
Title: DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision Abstract: Following the success of supervised learning, semi-supervised learning (SSL) is now becoming increasingly popular. SSL is a family of methods, which in addition to a labeled training set, also use a sizable collection of unlabeled da... |
Title: Delayed Reinforcement Learning by Imitation Abstract: When the agent's observations or interactions are delayed, classic reinforcement learning tools usually fail. In this paper, we propose a simple yet new and efficient solution to this problem. We assume that, in the undelayed environment, an efficient policy ... |
Title: Access Trends of In-network Cache for Scientific Data Abstract: Scientific collaborations are increasingly relying on large volumes of data for their work and many of them employ tiered systems to replicate the data to their worldwide user communities. Each user in the community often selects a different subset ... |
Title: Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings Abstract: Background: To determine the ability of a commercially available deep learning system, RetCAD v.1.3.1 (Thirona, Nijmegen, The Netherlands) for the automatic detection of referable diabetic re... |
Title: CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients Abstract: Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient a... |
Title: An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers Abstract: Self-supervised learning (SSL) methods such as masked language modeling have shown massive performance gains by pretraining transformer models for a variety of natural language processing tasks. The follow-... |
Title: Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting Abstract: Counterfactual explanations (CFEs) highlight what changes to a model's input would have changed its prediction in a particular way. CFEs have ... |
Title: Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory Abstract: In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the machine learning literature: 1) calibration between groups and 2) equality of false positiv... |
Title: Efficient Automated Deep Learning for Time Series Forecasting Abstract: Recent years have witnessed tremendously improved efficiency of Automated Machine Learning (AutoML), especially Automated Deep Learning (AutoDL) systems, but recent work focuses on tabular, image, or NLP tasks. So far, little attention has b... |
Title: Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization Abstract: This work provides the exact expression of the probability distribution of the hypervolume improvement (HVI) for bi-objective generalization of Bayesian optimization. Here, instead of a single-objective improvemen... |
Title: Analysis of convolutional neural network image classifiers in a rotationally symmetric model Abstract: Convolutional neural network image classifiers are defined and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed. Here we consider ima... |
Title: DNA data storage, sequencing data-carrying DNA Abstract: DNA is a leading candidate as the next archival storage media due to its density, durability and sustainability. To read (and write) data DNA storage exploits technology that has been developed over decades to sequence naturally occurring DNA in the life s... |
Title: Automatic Tuberculosis and COVID-19 cough classification using deep learning Abstract: We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory disease, have cough as a predominant sym... |
Title: Contrastive Supervised Distillation for Continual Representation Learning Abstract: In this paper, we propose a novel training procedure for the continual representation learning problem in which a neural network model is sequentially learned to alleviate catastrophic forgetting in visual search tasks. Our metho... |
Title: DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio Abstract: Deep learning-based speech enhancement has seen huge improvements and recently also expanded to full band audio (48 kHz). However, many approaches have a rather high computational complexity and require big tem... |
Title: Generation of non-stationary stochastic fields using Generative Adversarial Networks with limited training data Abstract: In the context of generating geological facies conditioned on observed data, samples corresponding to all possible conditions are not generally available in the training set and hence the gen... |
Title: Utilizing coarse-grained data in low-data settings for event extraction Abstract: Annotating text data for event information extraction systems is hard, expensive, and error-prone. We investigate the feasibility of integrating coarse-grained data (document or sentence labels), which is far more feasible to obtai... |
Title: A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials Abstract: There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in the real-world scenarios. To simulate the wild sc... |
Title: Making Pre-trained Language Models Good Long-tailed Learners Abstract: Prompt-tuning has shown appealing performance in few-shot classification by virtue of its capability in effectively exploiting pre-trained knowledge. This motivates us to check the hypothesis that prompt-tuning is also a promising choice for ... |
Title: CV4Code: Sourcecode Understanding via Visual Code Representations Abstract: We present CV4Code, a compact and effective computer vision method for sourcecode understanding. Our method leverages the contextual and the structural information available from the code snippet by treating each snippet as a two-dimensi... |
Title: Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis Abstract: Emerging technologies can have major economic impacts and affect strategic stability. Yet, early identification of emerging technologies remains challenging. In order to identify emerging technologies in a ... |
Title: An Introduction to Quantum Machine Learning for Engineers Abstract: In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parametrized, and th... |
Title: Hyperspectral Image Classification With Contrastive Graph Convolutional Network Abstract: Recently, Graph Convolutional Network (GCN) has been widely used in Hyperspectral Image (HSI) classification due to its satisfactory performance. However, the number of labeled pixels is very limited in HSI, and thus the av... |
Title: An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity Abstract: The leaky ReLU network with a group sparse regularization term has been widely used in the recent years. However, training such a network yields a nonsmooth nonconvex optimization problem and there exis... |
Title: Stochastic Variational Smoothed Model Checking Abstract: Model-checking for parametric stochastic models can be expressed as checking the satisfaction probability of a certain property as a function of the parameters of the model. Smoothed model checking (smMC) leverages Gaussian Processes (GP) to infer the sati... |
Title: A Survey on Fairness for Machine Learning on Graphs Abstract: Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in many real-world application domains where decisions can have a strong societal impact. However, numerous studies and papers have recently revealed that machine learn... |
Title: CVTT: Cross-Validation Through Time Abstract: The practical aspects of evaluating recommender systems is an actively discussed topic in the research community. While many current evaluation techniques bring performance down to a single-value metric as a straightforward approach for model comparison, it is based ... |
Title: AutoKE: An automatic knowledge embedding framework for scientific machine learning Abstract: Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations. However, for many engineering problems, gover... |
Title: Machine Learning to Support Triage of Children at Risk for Epileptic Seizures in the Pediatric Intensive Care Unit Abstract: Objective: Epileptic seizures are relatively common in critically-ill children admitted to the pediatric intensive care unit (PICU) and thus serve as an important target for identification... |
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