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Title: Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning Abstract: Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disab... |
Title: Combining Machine Learning and Agent-Based Modeling to Study Biomedical Systems Abstract: Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from ... |
Title: Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise Abstract: Stochastic first-order methods such as Stochastic Extragradient (SEG) or Stochastic Gradient Descent-Ascent (SGDA) for solving smooth minimax problems and, more generally, variational inequality problems (VIP) have been gai... |
Title: Weakly Supervised Representation Learning with Sparse Perturbations Abstract: The theory of representation learning aims to build methods that provably invert the data generating process with minimal domain knowledge or any source of supervision. Most prior approaches require strong distributional assumptions on... |
Title: Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Space Abstract: Machine learning classifiers have been demonstrated, both empirically and theoretically, to be robust to label noise under certain conditions -- notably the typical assumption is that label noise is independent of... |
Title: Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages Abstract: Diabetic retinopathy is an eye-related pathology creating abnormalities and causing visual impairment, proper treatment of which requires identifying irregularities. This research uses a hemorrhage detectio... |
Title: Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net Abstract: In a finite element analysis, using a large number of grids is important to obtain accurate results, but is a resource-consuming task. Aiming to real-time simulation and optimization, it is desired to... |
Title: Predictive Multiplicity in Probabilistic Classification Abstract: For a prediction task, there may exist multiple models that perform almost equally well. This multiplicity complicates how we typically develop and deploy machine learning models. We study how multiplicity affects predictions -- i.e., predictive m... |
Title: A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning Abstract: In this paper, we study a large-scale multi-agent minimax optimization problem, which models many interesting applications in statistical learning and game theory, including Generative Adversarial Networks (GANs)... |
Title: Vygotskian Autotelic Artificial Intelligence: Language and Culture Internalization for Human-Like AI Abstract: Building autonomous artificial agents able to grow open-ended repertoires of skills is one of the fundamental goals of AI. To that end, a promising developmental approach recommends the design of intrin... |
Title: Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation Abstract: General translation models often still struggle to generate accurate translations in specialized domains. To guide machine translation practitioners and characterize the effectiveness of domain adaptation methods ... |
Title: Causal Structure Learning: a Combinatorial Perspective Abstract: In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on approaches for learning directed acyclic graphs (DAGs) and various generalizations which allow for some variable... |
Title: Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning Abstract: In this work, we propose a novel ${\bf K}$ernelized ${\bf S}$tein Discrepancy-based Posterior Sampling for ${\bf RL}$ algorithm (named $\texttt{KSRL}$) which extends model-based RL based upon posteri... |
Title: Invertible Neural Networks for Graph Prediction Abstract: In this work, we address conditional generation using deep invertible neural networks. This is a type of problem where one aims to infer the most probable inputs $X$ given outcomes $Y$. We call our method \textit{invertible graph neural network} (iGNN) du... |
Title: Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation Abstract: In this paper, we study the problem of sparse mixed linear regression on an unlabeled dataset that is generated from linear measurements from two different regression parameter vectors. Since the data is... |
Title: Robust Longitudinal Control for Vehicular Autonomous Platoons Using Deep Reinforcement Learning Abstract: In the last few years, researchers have applied machine learning strategies in the context of vehicular platoons to increase the safety and efficiency of cooperative transportation. Reinforcement Learning me... |
Title: From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics* Abstract: Graphs have often been used to answer questions about the interaction between real-world entities by taking advantage of their capacity to represent complex topologies. Complex networks are known to b... |
Title: Deep Learning on Implicit Neural Datasets Abstract: Implicit neural representations (INRs) have become fast, lightweight tools for storing continuous data, but to date there is no general method for learning directly with INRs as a data representation. We introduce a principled deep learning framework for learni... |
Title: ORC: Network Group-based Knowledge Distillation using Online Role Change Abstract: In knowledge distillation, since a single, omnipotent teacher network cannot solve all problems, multiple teacher-based knowledge distillations have been studied recently. However, sometimes their improvements are not as good as e... |
Title: Uniqueness and Complexity of Inverse MDP Models Abstract: What is the action sequence aa'a" that was likely responsible for reaching state s"' (from state s) in 3 steps? Addressing such questions is important in causal reasoning and in reinforcement learning. Inverse "MDP" models p(aa'a"|ss"') can be used to ans... |
Title: Hard Negative Sampling Strategies for Contrastive Representation Learning Abstract: One of the challenges in contrastive learning is the selection of appropriate \textit{hard negative} examples, in the absence of label information. Random sampling or importance sampling methods based on feature similarity often ... |
Title: Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features Abstract: Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks. However, existing metrics for quantifying th... |
Title: Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages Abstract: Automatic Speech Recognition (ASR) has increasing utility in the modern world. There are a many ASR models available for languages with large amounts of training data like English. However, low-resource languages are poor... |
Title: Positive Unlabeled Contrastive Learning Abstract: Self-supervised pretraining on unlabeled data followed by supervised finetuning on labeled data is a popular paradigm for learning from limited labeled examples. In this paper, we investigate and extend this paradigm to the classical positive unlabeled (PU) setti... |
Title: RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning Abstract: In recent years, reinforcement learning has faced several challenges in the multi-agent domain, such as the credit assignment issue. Value function factorization emerges as a promising way to handle... |
Title: Accelerated first-order methods for convex optimization with locally Lipschitz continuous gradient Abstract: In this paper we develop accelerated first-order methods for convex optimization with locally Lipschitz continuous gradient (LLCG), which is beyond the well-studied class of convex optimization with Lipsc... |
Title: Equivariant Reinforcement Learning for Quadrotor UAV Abstract: This paper presents an equivariant reinforcement learning framework for quadrotor unmanned aerial vehicles. Successful training of reinforcement learning often requires numerous interactions with the environments, which hinders its applicability espe... |
Title: Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions Abstract: Generalization is one of the most important problems in deep learning (DL). In the overparameterized regime in neural networks, there exist many low-loss solutions that fit the training data e... |
Title: Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning Abstract: We propose a unifying view to analyze the representation quality of self-supervised learning (SSL) models without access to supervised labels, while being agnostic to the architecture, learning algorithm or data ma... |
Title: Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations Abstract: Despite the plethora of post hoc model explanation methods, the basic properties and behavior of these methods and the conditions under which each one is effective are not well understood. In... |
Title: Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions Abstract: High-dimensional Partial Differential Equations (PDEs) are a popular mathematical modelling tool, with applications ranging from finance to computational chemistry. However, standard numer... |
Title: Entangled Residual Mappings Abstract: Residual mappings have been shown to perform representation learning in the first layers and iterative feature refinement in higher layers. This interplay, combined with their stabilizing effect on the gradient norms, enables them to train very deep networks. In this paper, ... |
Title: Deep Learning Architecture Based Approach For 2D-Simulation of Microwave Plasma Interaction Abstract: This paper presents a convolutional neural network (CNN)-based deep learning model, inspired from UNet with series of encoder and decoder units with skip connections, for the simulation of microwave-plasma inter... |
Title: Exponential Separations in Symmetric Neural Networks Abstract: In this work we demonstrate a novel separation between symmetric neural network architectures. Specifically, we consider the Relational Network~\parencite{santoro2017simple} architecture as a natural generalization of the DeepSets~\parencite{zaheer20... |
Title: Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares Abstract: Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails has links to the generalization error. While these studies have shed light on interesting aspects of th... |
Title: Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks Abstract: A striking observation about iterative magnitude pruning (IMP; Frankle et al. 2020) is that $\unicode{x2014}$ after just a few hundred steps of dense training $\unicode{x2014}$ the method can find a sparse sub-networ... |
Title: Sequential Permutation Testing of Random Forest Variable Importance Measures Abstract: Hypothesis testing of random forest (RF) variable importance measures (VIMP) remains the subject of ongoing research. Among recent developments, heuristic approaches to parametric testing have been proposed whose distributiona... |
Title: Decentralized Training of Foundation Models in Heterogeneous Environments Abstract: Training foundation models, such as GPT-3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months. These models are typically trained in specialized clusters featuring fast,... |
Title: Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards Abstract: Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms. Th... |
Title: Rashomon Capacity: A Metric for Predictive Multiplicity in Probabilistic Classification Abstract: Predictive multiplicity occurs when classification models with nearly indistinguishable average performances assign conflicting predictions to individual samples. When used for decision-making in applications of con... |
Title: PNODE: A memory-efficient neural ODE framework based on high-level adjoint differentiation Abstract: Neural ordinary differential equations (neural ODEs) have emerged as a novel network architecture that bridges dynamical systems and deep learning. However, the gradient obtained with the continuous adjoint metho... |
Title: Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees Abstract: Communication compression is a crucial technique for modern distributed learning systems to alleviate their communication bottlenecks over slower networks. Despite recent intensive studies of gradient compressio... |
Title: Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling Abstract: Restricted Boltzmann Machines are simple and powerful generative models capable of encoding any complex dataset. Despite all their advantages, in practice, trainings are often unstable, and it is hard to assess their quality beca... |
Title: Learning Soft Constraints From Constrained Expert Demonstrations Abstract: Inverse reinforcement learning (IRL) methods assume that the expert data is generated by an agent optimizing some reward function. However, in many settings, the agent may optimize a reward function subject to some constraints, where the ... |
Title: A New Security Boundary of Component Differentially Challenged XOR PUFs Against Machine Learning Modeling Attacks Abstract: Physical Unclonable Functions (PUFs) are promising security primitives for resource-constrained network nodes. The XOR Arbiter PUF (XOR PUF or XPUF) is an intensively studied PUF invented t... |
Title: Sample-Efficient Reinforcement Learning of Partially Observable Markov Games Abstract: This paper considers the challenging tasks of Multi-Agent Reinforcement Learning (MARL) under partial observability, where each agent only sees her own individual observations and actions that reveal incomplete information abo... |
Title: SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG Abstract: Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with millisecond resolution, rendering it a viable method in neuroscience and healthcare. However, its utility is limited... |
Title: Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information Abstract: There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been focused on human sensing applic... |
Title: Optimal Activation Functions for the Random Features Regression Model Abstract: The asymptotic mean squared test error and sensitivity of the Random Features Regression model (RFR) have been recently studied. We build on this work and identify in closed-form the family of Activation Functions (AFs) that minimize... |
Title: Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code Abstract: Few-shot learning with large-scale, pre-trained language models is a powerful way to answer questions about code, e.g., how to complete a given code example, or even generate code snippets from scratch. Th... |
Title: Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control Abstract: Machine-learned black-box policies are ubiquitous for nonlinear control problems. Meanwhile, crude model information is often available for these problems from, e.g., linear approximations of nonlinear dynamics. We study ... |
Title: Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning Abstract: While the empirical success of self-supervised learning (SSL) heavily relies on the usage of deep nonlinear models, many theoretical works proposed to understand SSL still focus on linear ones. In this paper, we study t... |
Title: HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning Abstract: The use of machine learning (ML) models in decision-making contexts, particularly those used in high-stakes decision-making, are fraught with issue and peril since a person - not a machine - must ultimately be held accountable for th... |
Title: Detecting Pulmonary Embolism from Computed Tomography Using Convolutional Neural Network Abstract: The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vasc... |
Title: MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data Abstract: Numerical reasoning over hybrid data containing both textual and tabular content (e.g., financial reports) has recently attracted much attention in the NLP community. However, existing question answering (QA) benchmarks o... |
Title: On the Privacy Properties of GAN-generated Samples Abstract: The privacy implications of generative adversarial networks (GANs) are a topic of great interest, leading to several recent algorithms for training GANs with privacy guarantees. By drawing connections to the generalization properties of GANs, we prove ... |
Title: Supernet Training for Federated Image Classification under System Heterogeneity Abstract: Efficient deployment of deep neural networks across many devices and resource constraints, especially on edge devices, is one of the most challenging problems in the presence of data-privacy preservation issues. Conventiona... |
Title: Adversarial Unlearning: Reducing Confidence Along Adversarial Directions Abstract: Supervised learning methods trained with maximum likelihood objectives often overfit on training data. Most regularizers that prevent overfitting look to increase confidence on additional examples (e.g., data augmentation, adversa... |
Title: Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation Abstract: Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on... |
Title: Slot Order Matters for Compositional Scene Understanding Abstract: Empowering agents with a compositional understanding of their environment is a promising next step toward solving long-horizon planning problems. On the one hand, we have seen encouraging progress on variational inference algorithms for obtaining... |
Title: Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission Abstract: This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a ... |
Title: Regularization-wise double descent: Why it occurs and how to eliminate it Abstract: The risk of overparameterized models, in particular deep neural networks, is often double-descent shaped as a function of the model size. Recently, it was shown that the risk as a function of the early-stopping time can also be d... |
Title: Instant Graph Neural Networks for Dynamic Graphs Abstract: Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with mi... |
Title: Generalization for multiclass classification with overparameterized linear models Abstract: Via an overparameterized linear model with Gaussian features, we provide conditions for good generalization for multiclass classification of minimum-norm interpolating solutions in an asymptotic setting where both the num... |
Title: MetaLR: Layer-wise Learning Rate based on Meta-Learning for Adaptively Fine-tuning Medical Pre-trained Models Abstract: When applying transfer learning for medical image analysis, downstream tasks often have significant gaps with the pre-training tasks. Previous methods mainly focus on improving the transferabil... |
Title: Hybrid Models for Mixed Variables in Bayesian Optimization Abstract: We systematically describe the problem of simultaneous surrogate modeling of mixed variables (i.e., continuous, integer and categorical variables) in the Bayesian optimization (BO) context. We provide a unified hybrid model using both Monte-Car... |
Title: Fair Classification via Transformer Neural Networks: Case Study of an Educational Domain Abstract: Educational technologies nowadays increasingly use data and Machine Learning (ML) models. This gives the students, instructors, and administrators support and insights for the optimum policy. However, it is well ac... |
Title: Impact of the composition of feature extraction and class sampling in medicare fraud detection Abstract: With healthcare being critical aspect, health insurance has become an important scheme in minimizing medical expenses. Following this, the healthcare industry has seen a significant increase in fraudulent act... |
Title: Rate-Optimal Online Convex Optimization in Adaptive Linear Control Abstract: We consider the problem of controlling an unknown linear dynamical system under adversarially changing convex costs and full feedback of both the state and cost function. We present the first computationally-efficient algorithm that att... |
Title: On the Generalization of Wasserstein Robust Federated Learning Abstract: In federated learning, participating clients typically possess non-i.i.d. data, posing a significant challenge to generalization to unseen distributions. To address this, we propose a Wasserstein distributionally robust optimization scheme ... |
Title: Modeling electronic health record data using a knowledge-graph-embedded topic model Abstract: The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has be... |
Title: XPASC: Measuring Generalization in Weak Supervision Abstract: Weak supervision is leveraged in a wide range of domains and tasks due to its ability to create massive amounts of labeled data, requiring only little manual effort. Standard approaches use labeling functions to specify signals that are relevant for t... |
Title: Indirect Active Learning Abstract: Traditional models of active learning assume a learner can directly manipulate or query a covariate $X$ in order to study its relationship with a response $Y$. However, if $X$ is a feature of a complex system, it may be possible only to indirectly influence $X$ by manipulating ... |
Title: Safety Certification for Stochastic Systems via Neural Barrier Functions Abstract: Providing non-trivial certificates of safety for non-linear stochastic systems is an important open problem that limits the wider adoption of autonomous systems in safety-critical applications. One promising solution to address th... |
Title: PAC Statistical Model Checking of Mean Payoff in Discrete- and Continuous-Time MDP Abstract: Markov decision processes (MDP) and continuous-time MDP (CTMDP) are the fundamental models for non-deterministic systems with probabilistic uncertainty. Mean payoff (a.k.a. long-run average reward) is one of the most cla... |
Title: Zero-Shot Bird Species Recognition by Learning from Field Guides Abstract: We exploit field guides to learn bird species recognition, in particular zero-shot recognition of unseen species. The illustrations contained in field guides deliberately focus on discriminative properties of a species, and can serve as s... |
Title: Evaluating Transfer-based Targeted Adversarial Perturbations against Real-World Computer Vision Systems based on Human Judgments Abstract: Computer vision systems are remarkably vulnerable to adversarial perturbations. Transfer-based adversarial images are generated on one (source) system and used to attack anot... |
Title: Offline Reinforcement Learning with Causal Structured World Models Abstract: Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment. Previous model-based offline RL methods learn fully ... |
Title: Functional Connectivity Methods for EEG-based Biometrics on a Large, Heterogeneous Dataset Abstract: This study examines the utility of functional connectivity (FC) and graph-based (GB) measures with a support vector machine classifier for use in electroencephalogram (EEG) based biometrics. Although FC-based fea... |
Title: Finding Rule-Interpretable Non-Negative Data Representation Abstract: Non-negative Matrix Factorization (NMF) is an intensively used technique for obtaining parts-based, lower dimensional and non-negative representation of non-negative data. It is a popular method in different research fields. Scientists perform... |
Title: Transferring Studies Across Embodiments: A Case Study in Confusion Detection Abstract: Human-robot studies are expensive to conduct and difficult to control, and as such researchers sometimes turn to human-avatar interaction in the hope of faster and cheaper data collection that can be transferred to the robot d... |
Title: Constraining Gaussian processes for physics-informed acoustic emission mapping Abstract: The automated localisation of damage in structures is a challenging but critical ingredient in the path towards predictive or condition-based maintenance of high value structures. The use of acoustic emission time of arrival... |
Title: Causality Learning With Wasserstein Generative Adversarial Networks Abstract: Conventional methods for causal structure learning from data face significant challenges due to combinatorial search space. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constrain... |
Title: Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem? Abstract: We propose a geometric scattering-based graph neural network (GNN) for approximating solutions of the NP-hard maximal clique (MC) problem. We construct a loss function with two terms, one which encourages the network to fin... |
Title: Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements Abstract: With the recent proliferation of artificial intelligence systems, there has been a surge in the demand for explainability of these systems. Explanations help to redu... |
Title: Canonical convolutional neural networks Abstract: We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network w... |
Title: Latent Topology Induction for Understanding Contextualized Representations Abstract: In this work, we study the representation space of contextualized embeddings and gain insight into the hidden topology of large language models. We show there exists a network of latent states that summarize linguistic propertie... |
Title: Understanding deep learning via decision boundary Abstract: This paper discovers that the neural network with lower decision boundary (DB) variability has better generalizability. Two new notions, algorithm DB variability and $(\epsilon, \eta)$-data DB variability, are proposed to measure the decision boundary v... |
Title: A Survey on Surrogate-assisted Efficient Neural Architecture Search Abstract: Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep... |
Title: A High-Performance Customer Churn Prediction System based on Self-Attention Abstract: Customer churn prediction is a challenging domain of research that contributes to customer retention strategy. The predictive performance of existing machine learning models, which are often adopted by churn communities, appear... |
Title: Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination Abstract: Graph contrastive learning (GCL) alleviates the heavy reliance on label information for graph representation learning (GRL) via self-supervised learning schemes. The core idea is to learn by ... |
Title: Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning Abstract: We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a lim... |
Title: Detecting the Severity of Major Depressive Disorder from Speech: A Novel HARD-Training Methodology Abstract: Major Depressive Disorder (MDD) is a common worldwide mental health issue with high associated socioeconomic costs. The prediction and automatic detection of MDD can, therefore, make a huge impact on soci... |
Title: Beyond Opinion Mining: Summarizing Opinions of Customer Reviews Abstract: Customer reviews are vital for making purchasing decisions in the Information Age. Such reviews can be automatically summarized to provide the user with an overview of opinions. In this tutorial, we present various aspects of opinion summa... |
Title: Truly Mesh-free Physics-Informed Neural Networks Abstract: Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural networks. Although generally viewed as being mesh-free, current approaches... |
Title: Is an encoder within reach? Abstract: The encoder network of an autoencoder is an approximation of the nearest point projection onto the manifold spanned by the decoder. A concern with this approximation is that, while the output of the encoder is always unique, the projection can possibly have infinitely many v... |
Title: Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning Abstract: Characterizing aleatoric and epistemic uncertainty on the predicted rewards can help in building reliable reinforcement learning (RL) systems. Aleatoric uncertainty results from the irreducible environment stochasticity leading... |
Title: Prescriptive maintenance with causal machine learning Abstract: Machine maintenance is a challenging operational problem, where the goal is to plan sufficient preventive maintenance to avoid machine failures and overhauls. Maintenance is often imperfect in reality and does not make the asset as good as new. Alth... |
Title: Optimal Weak to Strong Learning Abstract: The classic algorithm AdaBoost allows to convert a weak learner, that is an algorithm that produces a hypothesis which is slightly better than chance, into a strong learner, achieving arbitrarily high accuracy when given enough training data. We present a new algorithm t... |
Title: On Calibration of Graph Neural Networks for Node Classification Abstract: Graphs can model real-world, complex systems by representing entities and their interactions in terms of nodes and edges. To better exploit the graph structure, graph neural networks have been developed, which learn entity and edge embeddi... |
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