text stringlengths 0 4.09k |
|---|
Title: Beyond backpropagation: implicit gradients for bilevel optimization Abstract: This paper reviews gradient-based techniques to solve bilevel optimization problems. Bilevel optimization is a general way to frame the learning of systems that are implicitly defined through a quantity that they minimize. This charact... |
Title: Probabilistic learning constrained by realizations using a weak formulation of Fourier transform of probability measures Abstract: This paper deals with the taking into account a given set of realizations as constraints in the Kullback-Leibler minimum principle, which is used as a probabilistic learning algorith... |
Title: PTFlash : A deep learning framework for isothermal two-phase equilibrium calculations Abstract: Phase equilibrium calculations are an essential part of numerical simulations of multi-component multi-phase flow in porous media, accounting for the largest share of the computational time. In this work, we introduce... |
Title: Federated Learning with Noisy User Feedback Abstract: Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. This has led to growing concerns over user privacy, since human interaction data typically needs to be transmitted to the clou... |
Title: Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery Abstract: The integration of the modern Machine Learning (ML) models into remote sensing and agriculture has expanded the scope of the application of satellite images in the agriculture doma... |
Title: LPGNet: Link Private Graph Networks for Node Classification Abstract: Classification tasks on labeled graph-structured data have many important applications ranging from social recommendation to financial modeling. Deep neural networks are increasingly being used for node classification on graphs, wherein nodes ... |
Title: Controlled Dropout for Uncertainty Estimation Abstract: Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictio... |
Title: Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances Abstract: Each utterance in multi-turn empathetic dialogues has features such as emotion, keywords, and utterance-level meaning. Feature transitions between utterances occur naturally. However, existing approaches fail... |
Title: Longitudinal cardio-respiratory fitness prediction through free-living wearable sensors Abstract: Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate response to a st... |
Title: SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning Abstract: In this work, we introduce a new perspective for learning transferable content in multi-task imitation learning. Humans are able to transfer skills and knowledge. If we can cycle to work and drive to the store, we can also cycl... |
Title: Fast Rate Generalization Error Bounds: Variations on a Theme Abstract: A recent line of works, initiated by Russo and Xu, has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the relevant works, the convergence rate of the expected generalizatio... |
Title: How to Minimize the Weighted Sum AoI in Multi-Source Status Update Systems: OMA or NOMA? Abstract: In this paper, the minimization of the weighted sum average age of information (AoI) in a multi-source status update communication system is studied. Multiple independent sources send update packets to a common des... |
Title: Investigating and Explaining the Frequency Bias in Image Classification Abstract: CNNs exhibit many behaviors different from humans, one of which is the capability of employing high-frequency components. This paper discusses the frequency bias phenomenon in image classification tasks: the high-frequency componen... |
Title: Geodesics, Non-linearities and the Archive of Novelty Search Abstract: The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short note focuses on the effects of the archive on exploration. Experi... |
Title: Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data Abstract: Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical con... |
Title: The NT-Xent loss upper bound Abstract: Self-supervised learning is a growing paradigm in deep representation learning, showing great generalization capabilities and competitive performance in low-labeled data regimes. The SimCLR framework proposes the NT-Xent loss for contrastive representation learning. The obj... |
Title: Green Accelerated Hoeffding Tree Abstract: State-of-the-art machine learning solutions mainly focus on creating highly accurate models without constraints on hardware resources. Stream mining algorithms are designed to run on resource-constrained devices, thus a focus on low power and energy and memory-efficient... |
Title: On boundary conditions parametrized by analytic functions Abstract: Computer algebra can answer various questions about partial differential equations using symbolic algorithms. However, the inclusion of data into equations is rare in computer algebra. Therefore, recently, computer algebra models have been combi... |
Title: Imperceptible Backdoor Attack: From Input Space to Feature Representation Abstract: Backdoor attacks are rapidly emerging threats to deep neural networks (DNNs). In the backdoor attack scenario, attackers usually implant the backdoor into the target model by manipulating the training dataset or training process.... |
Title: Scalable computation of prediction intervals for neural networks via matrix sketching Abstract: Accounting for the uncertainty in the predictions of modern neural networks is a challenging and important task in many domains. Existing algorithms for uncertainty estimation require modifying the model architecture ... |
Title: Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation Abstract: Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applyin... |
Title: Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals Abstract: Channel estimation is a critical task in intelligent reflecting surface (IRS)-assisted wireless systems due to the uncertainties imposed by environment dynamics and rapid changes in the IRS configuration. To deal wi... |
Title: Perseus: A Simple High-Order Regularization Method for Variational Inequalities Abstract: This paper settles an open and challenging question pertaining to the design of simple high-order regularization methods for solving smooth and monotone variational inequalities (VIs). A VI involves finding $x^\star \in \ma... |
Title: Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms Abstract: While the field of Quality-Diversity (QD) has grown into a distinct branch of stochastic optimization, a few problems, in particular locomotion and navigation tasks, have become de facto standards. Are such benchmarks suf... |
Title: HumanAL: Calibrating Human Matching Beyond a Single Task Abstract: This work offers a novel view on the use of human input as labels, acknowledging that humans may err. We build a behavioral profile for human annotators which is used as a feature representation of the provided input. We show that by utilizing bl... |
Title: Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI Abstract: Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In ... |
Title: Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis Abstract: EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are similar. But EEG s... |
Title: Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus Abstract: With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most cur... |
Title: Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model Abstract: Gait phase detection with convolution neural network provides accurate classification but demands high computational cost, which inhibits real time low power on-sensor processing. This paper presents a segmentation based gait phase... |
Title: A CNN Approach for 5G mmWave Positioning Using Beamformed CSI Measurements Abstract: The advent of Artificial Intelligence (AI) has impacted all aspects of human life. One of the concrete examples of AI impact is visible in radio positioning. In this article, for the first time we utilize the power of AI by trai... |
Title: Ultra-sensitive Flexible Sponge-Sensor Array for Muscle Activities Detection and Human Limb Motion Recognition Abstract: Human limb motion tracking and recognition plays an important role in medical rehabilitation training, lower limb assistance, prosthetics design for amputees, feedback control for assistive ro... |
Title: Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures Abstract: Epilepsy is a chronic neurological disorder affecting 1\% of people worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for accurate epileptic seizure (ES... |
Title: Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification Abstract: Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In this ... |
Title: Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality Abstract: Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help p... |
Title: What Makes A Good Fisherman? Linear Regression under Self-Selection Bias Abstract: In the classical setting of self-selection, the goal is to learn $k$ models, simultaneously from observations $(x^{(i)}, y^{(i)})$ where $y^{(i)}$ is the output of one of $k$ underlying models on input $x^{(i)}$. In contrast to mi... |
Title: Synthetic Data -- what, why and how? Abstract: This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy. The article is intended for a non-technical audience, though some formal definitions have been ... |
Title: Convex Analysis at Infinity: An Introduction to Astral Space Abstract: Not all convex functions on $\mathbb{R}^n$ have finite minimizers; some can only be minimized by a sequence as it heads to infinity. In this work, we aim to develop a theory for understanding such minimizers at infinity. We study astral space... |
Title: Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design Abstract: Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empiri... |
Title: Optimal Control as Variational Inference Abstract: In this article we address the stochastic and risk sensitive optimal control problem probabilistically and decompose and solve the probabilistic models using principles from variational inference. We demonstrate how this culminates into two separate probabilisti... |
Title: Vehicle management in a modular production context using Deep Q-Learning Abstract: We investigate the feasibility of deploying Deep-Q based deep reinforcement learning agents to job-shop scheduling problems in the context of modular production facilities, using discrete event simulations for the environment. The... |
Title: The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Abstract: Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc explainability methods where a simple, h... |
Title: Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models Abstract: Recent studies increasingly adopt simulation-based machine learning (ML) models to analyze critical infrastructure system resilience. For realistic applications, these ML models consider the... |
Title: Benchmarking Econometric and Machine Learning Methodologies in Nowcasting Abstract: Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag, such as final GDP figures. Currently, there are a plethora of methodologies and approaches for practitioners to... |
Title: UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks Abstract: The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments. More recently yet still in the context of wireless networks, drones hav... |
Title: Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning Abstract: Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has use cases in chemistry, biology, and medicine. In the past decade, the ... |
Title: How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation Abstract: Reinforcement learning (RL) has been shown to be effective at learning control from experience. However, RL typically requires a large amount of online interaction with the envir... |
Title: Trainable Wavelet Neural Network for Non-Stationary Signals Abstract: This work introduces a wavelet neural network to learn a filter-bank specialized to fit non-stationary signals and improve interpretability and performance for digital signal processing. The network uses a wavelet transform as the first layer ... |
Title: Efficient Minimax Optimal Estimators For Multivariate Convex Regression Abstract: We study the computational aspects of the task of multivariate convex regression in dimension $d \geq 5$. We present the first computationally efficient minimax optimal (up to logarithmic factors) estimators for the tasks of (i) $L... |
Title: DADApy: Distance-based Analysis of DAta-manifolds in Python Abstract: DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for compa... |
Title: Physics-informed neural networks for PDE-constrained optimization and control Abstract: A fundamental problem of science is designing optimal control policies that manipulate a given environment into producing a desired outcome. Control Physics-Informed Neural Networks simultaneously solve a given system state, ... |
Title: Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting Abstract: Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A common target of said applications is to predict the resu... |
Title: Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning Abstract: To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly... |
Title: Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment Abstract: Automatic pronunciation assessment is an important technology to help self-directed language learners. While pronunciation quality has multiple aspects including accuracy, fluency, completeness, and pro... |
Title: Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition Abstract: Recognizing human non-speech vocalizations is an important task and has broad applications such as automatic sound transcription and health condition monitoring. However, existing datasets have a relatively small number of vocal sound s... |
Title: Global Multi-modal 2D/3D Registration via Local Descriptors Learning Abstract: Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all r... |
Title: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching Abstract: Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learn... |
Title: Structure Learning in Graphical Models from Indirect Observations Abstract: This paper considers learning of the graphical structure of a $p$-dimensional random vector $X \in R^p$ using both parametric and non-parametric methods. Unlike the previous works which observe $x$ directly, we consider the indirect obse... |
Title: Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds Abstract: Traditional memory writing operations proceed one bit at a time, where e.g. an individual magnetic domain is force-flipped by a localized external field. One way to increase material storage capacity woul... |
Title: Hitting time for Markov decision process Abstract: We define the hitting time for a Markov decision process (MDP). We do not use the hitting time of the Markov process induced by the MDP because the induced chain may not have a stationary distribution. Even it has a stationary distribution, the stationary distri... |
Title: Diverse Imitation Learning via Self-Organizing Generative Models Abstract: Imitation learning is the task of replicating expert policy from demonstrations, without access to a reward function. This task becomes particularly challenging when the expert exhibits a mixture of behaviors. Prior work has introduced la... |
Title: Clustered Graph Matching for Label Recovery and Graph Classification Abstract: Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We consider matchi... |
Title: DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI Abstract: Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as ... |
Title: Norm-Scaling for Out-of-Distribution Detection Abstract: Out-of-Distribution (OoD) inputs are examples that do not belong to the true underlying distribution of the dataset. Research has shown that deep neural nets make confident mispredictions on OoD inputs. Therefore, it is critical to identify OoD inputs for ... |
Title: Online Model Compression for Federated Learning with Large Models Abstract: This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost. The proposed Online Model Compression (OMC) provides a framework that stor... |
Title: PARAFAC2$\times$N: Coupled Decomposition of Multi-modal Data with Drift in N Modes Abstract: Reliable analysis of comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC$\times$GC-TOFMS) data is considered to be a major bottleneck for its widespread application. For multiple sampl... |
Title: Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks Abstract: The Li-ion battery is a complex physicochemical system that generally takes applied current as input and terminal voltage as output. The mappings from current to voltage can be described by several ... |
Title: Fine-grained Intent Classification in the Legal Domain Abstract: A law practitioner has to go through a lot of long legal case proceedings. To understand the motivation behind the actions of different parties/individuals in a legal case, it is essential that the parts of the document that express an intent corre... |
Title: Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept Abstract: Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Due to increased wear and tear, the maintenance aspect of a wind turbine is of critical importance. Unexpec... |
Title: Unsupervised Deep Unrolled Reconstruction Using Regularization by Denoising Abstract: Deep learning methods have been successfully used in various computer vision tasks. Inspired by that success, deep learning has been explored in magnetic resonance imaging (MRI) reconstruction. In particular, integrating deep l... |
Title: Factory: Fast Contact for Robotic Assembly Abstract: Robotic assembly is one of the oldest and most challenging applications of robotics. In other areas of robotics, such as perception and grasping, simulation has rapidly accelerated research progress, particularly when combined with modern deep learning. Howeve... |
Title: Anomaly Detection in Intra-Vehicle Networks Abstract: The progression of innovation and technology and ease of inter-connectivity among networks has allowed us to evolve towards one of the promising areas, the Internet of Vehicles. Nowadays, modern vehicles are connected to a range of networks, including intra-v... |
Title: Graph Spectral Embedding using the Geodesic Betweeness Centrality Abstract: We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvester equation to capture both network structure and neighbo... |
Title: Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees Abstract: Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonst... |
Title: Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance Abstract: Domain adaptation on time-series data is often encountered in the industry but received limited attention in academia. Most of the existing domain adaptation methods for time-series data borrow th... |
Title: Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling Abstract: In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment. The goal is maximize the overall target coverage of a... |
Title: Number Entity Recognition Abstract: Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not accounted for distinctly in most NLP tasks, there is still an underlying amount of numeracy alread... |
Title: Deep learning for spatio-temporal forecasting -- application to solar energy Abstract: This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main resea... |
Title: Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data Abstract: Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resul... |
Title: Towards Computationally Feasible Deep Active Learning Abstract: Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many others. O... |
Title: ConceptDistil: Model-Agnostic Distillation of Concept Explanations Abstract: Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for specific models (eg, neural networks) or data types (eg, images), and by ei... |
Title: BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck Abstract: Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classi... |
Title: Determination of class-specific variables in nonparametric multiple-class classification Abstract: As technology advanced, collecting data via automatic collection devices become popular, thus we commonly face data sets with lengthy variables, especially when these data sets are collected without specific resear... |
Title: Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks Abstract: Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the ... |
Title: Label Adversarial Learning for Skeleton-level to Pixel-level Adjustable Vessel Segmentation Abstract: You can have your cake and eat it too. Microvessel segmentation in optical coherence tomography angiography (OCTA) images remains challenging. Skeleton-level segmentation shows clear topology but without diamete... |
Title: Variational Sparse Coding with Learned Thresholding Abstract: Sparse coding strategies have been lauded for their parsimonious representations of data that leverage low dimensional structure. However, inference of these codes typically relies on an optimization procedure with poor computational scaling in high-d... |
Title: Automated Algorithm Selection for Radar Network Configuration Abstract: The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to ... |
Title: Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions Abstract: Nonlinear partial differential equations (PDEs) are used to model dynamical processes in a large number of scientific fields, ranging from finance to biology. In many applications standard local models are not su... |
Title: Towards Practical Physics-Informed ML Design and Evaluation for Power Grid Abstract: When applied to a real-world safety critical system like the power grid, general machine learning methods suffer from expensive training, non-physical solutions, and limited interpretability. To address these challenges for powe... |
Title: Rate-Optimal Contextual Online Matching Bandit Abstract: Two-sided online matching platforms have been employed in various markets. However, agents' preferences in present market are usually implicit and unknown and must be learned from data. With the growing availability of side information involved in the deci... |
Title: Quantifying and Extrapolating Data Needs in Radio Frequency Machine Learning Abstract: Understanding the relationship between training data and a model's performance once deployed is a fundamental component in the application of machine learning. While the model's deployed performance is dependent on numerous va... |
Title: Accuracy Convergent Field Predictors Abstract: Several predictive algorithms are described. Highlighted are variants that make predictions by superposing fields associated to the training data instances. They operate seamlessly with categorical, continuous, and mixed data. Predictive accuracy convergence is also... |
Title: Odor Descriptor Understanding through Prompting Abstract: Embeddings from contemporary natural language processing (NLP) models are commonly used as numerical representations for words or sentences. However, odor descriptor words, like "leather" or "fruity", vary significantly between their commonplace usage and... |
Title: Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention Abstract: The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks. To address this issue, parameter-efficient transfer le... |
Title: Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm Abstract: We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We focus on obtaining tight, often matching, lower and up... |
Title: Optimal Lighting Control in Greenhouses Using Bayesian Neural Networks for Sunlight Prediction Abstract: Controlling the environmental parameters, including light in greenhouses, increases the crop yield; however, the electricity cost of supplemental lighting can be high. Therefore, the importance of applying co... |
Title: FRC-TOuNN: Topology Optimization of Continuous Fiber Reinforced Composites using Neural Network Abstract: In this paper, we present a topology optimization (TO) framework to simultaneously optimize the matrix topology and fiber distribution of functionally graded continuous fiber-reinforced composites (FRC). Cur... |
Title: Data-Driven Approximations of Chance Constrained Programs in Nonstationary Environments Abstract: We study sample average approximations (SAA) of chance constrained programs. SAA methods typically approximate the actual distribution in the chance constraint using an empirical distribution constructed from random... |
Title: GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion Abstract: Low-rank tensor factorization or completion is well-studied and applied in various online settings, such as online tensor factorization (where the temporal mode grows) and online tensor completion (where incomplete slices ... |
Title: Learnability of Competitive Threshold Models Abstract: Modeling the spread of social contagions is central to various applications in social computing. In this paper, we study the learnability of the competitive threshold model from a theoretical perspective. We demonstrate how competitive threshold models can b... |
Title: Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks Abstract: The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory. Previous studies in Semi-Sup... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.