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Title: Rule-based Evolutionary Bayesian Learning Abstract: In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty quantification and (ii) rule-based systems for the incorporation of expert knowl... |
Title: ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data Abstract: Unstructured point clouds with varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR). Predicting a scalar response bas... |
Title: OUR-GAN: One-shot Ultra-high-Resolution Generative Adversarial Networks Abstract: We propose OUR-GAN, the first one-shot ultra-high-resolution (UHR) image synthesis framework that generates non-repetitive images with 4K or higher resolution from a single training image. OUR-GAN generates a visually coherent imag... |
Title: Differential equation and probability inspired graph neural networks for latent variable learning Abstract: Probabilistic theory and differential equation are powerful tools for the interpretability and guidance of the design of machine learning models, especially for illuminating the mathematical motivation of ... |
Title: Magnitude-aware Probabilistic Speaker Embeddings Abstract: Recently, hyperspherical embeddings have established themselves as a dominant technique for face and voice recognition. Specifically, Euclidean space vector embeddings are learned to encode person-specific information in their direction while ignoring th... |
Title: How and what to learn:The modes of machine learning Abstract: We proposal a new approach, namely the weight pathway analysis (WPA), to study the mechanism of multilayer neural networks. The weight pathways linking neurons longitudinally from input neurons to output neurons are considered as the basic units of a ... |
Title: The Causal Marginal Polytope for Bounding Treatment Effects Abstract: Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model. Nevertheless, we can ask for partial identification, which usually boils down to finding upper and lower bounds of a causal quantity of... |
Title: Hyperbolic Graph Neural Networks: A Review of Methods and Applications Abstract: Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability. In spite of the remarkable achievements, the performance o... |
Title: Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation Abstract: We study the convergence of the actor-critic algorithm with nonlinear function approximation under a nonconvex-nonconcave primal-dual formulation. Stochastic gradient descent ascent is applied with an adapt... |
Title: A new face database simultaneously acquired in visible, near infrared and thermal spectrum Abstract: In this paper we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four dif... |
Title: On the relevance of bandwidth extension for speaker identification Abstract: In this paper we discuss the relevance of bandwidth extension for speaker identification tasks. Mainly we want to study if it is possible to recognize voices that have been bandwith extended. For this purpose, we created two different d... |
Title: N-dimensional nonlinear prediction with MLP Abstract: In this paper we propose a Non-Linear Predictive Vector quantizer (PVQ) for speech coding, based on Multi-Layer Perceptrons. With this scheme we have improved the results of our previous ADPCM coder with nonlinear prediction, and we have reduced the bit rate ... |
Title: Unfolding collective AIS transmission behavior for vessel movement modeling on irregular timing data using noise-robust neural networks Abstract: This paper aims to model the Automatic Identification System (AIS) message transmission behavior through neural networks for forecasting the upcoming AIS messages' con... |
Title: A Real-World Implementation of Unbiased Lift-based Bidding System Abstract: In display ad auctions of Real-Time Bid-ding (RTB), a typical Demand-Side Platform (DSP)bids based on the predicted probability of click and conversion right after an ad impression. Recent studies find such a strategy is suboptimal and p... |
Title: Simulating Network Paths with Recurrent Buffering Units Abstract: Simulating physical network paths (e.g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking. We seek a model that generates end-to-end packet delay values in response to the time-varying load offered by a se... |
Title: Wastewater Pipe Rating Model Using Natural Language Processing Abstract: Closed-circuit video (CCTV) inspection has been the most popular technique for visually evaluating the interior status of pipelines in recent decades. Certified inspectors prepare the pipe repair document based on the CCTV inspection. The t... |
Title: Time Series Analysis of Blockchain-Based Cryptocurrency Price Changes Abstract: In this paper we apply neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price. This paper's code contains Jupyter notebooks, one o... |
Title: Numeric Lyndon-based feature embedding of sequencing reads for machine learning approaches Abstract: Feature embedding methods have been proposed in literature to represent sequences as numeric vectors to be used in some bioinformatics investigations, such as family classification and protein structure predictio... |
Title: Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity Abstract: Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment. To counter the insufficient coverage and sample scarcity of many of... |
Title: Bayesian Structure Learning with Generative Flow Networks Abstract: In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data. Defining such a distribution is very challenging, due to the combinatorially large sam... |
Title: Combining Modular Skills in Multitask Learning Abstract: A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume that each task is associated with a subset of latent discrete skills from a (potent... |
Title: MaMaDroid2.0 -- The Holes of Control Flow Graphs Abstract: Android malware is a continuously expanding threat to billions of mobile users around the globe. Detection systems are updated constantly to address these threats. However, a backlash takes the form of evasion attacks, in which an adversary changes malic... |
Title: Functional mixture-of-experts for classification Abstract: We develop a mixtures-of-experts (ME) approach to the multiclass classification where the predictors are univariate functions. It consists of a ME model in which both the gating network and the experts network are constructed upon multinomial logistic ac... |
Title: Background Mixup Data Augmentation for Hand and Object-in-Contact Detection Abstract: Detecting the positions of human hands and objects-in-contact (hand-object detection) in each video frame is vital for understanding human activities from videos. For training an object detector, a method called Mixup, which ov... |
Title: RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation Abstract: Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arr... |
Title: Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching Abstract: Semi-structured query systems for document-oriented databases have many real applications. One particular application that we are interested in is matching each financial ... |
Title: Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysis Abstract: Medical image analysis plays a key role in precision medicine as it allows the clinicians to identify anatomical abnormalities and it is routinely used in clinical assessmen... |
Title: A Proximal Algorithm for Sampling Abstract: We consider sampling problems with possibly non-smooth potentials (negative log-densities). In particular, we study two specific settings of sampling where the convex potential is either semi-smooth or in composite form as the sum of a smooth component and a semi-smoot... |
Title: Risk-Neutral Market Simulation Abstract: We develop a risk-neutral spot and equity option market simulator for a single underlying, under which the joint market process is a martingale. We leverage an efficient low-dimensional representation of the market which preserves no static arbitrage, and employ neural sp... |
Title: Resolving label uncertainty with implicit posterior models Abstract: We propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label. By implicitly assuming the existence of a generative model for which a differ... |
Title: SUNet: Swin Transformer UNet for Image Denoising Abstract: Image restoration is a challenging ill-posed problem which also has been a long-standing issue. In the past few years, the convolution neural networks (CNNs) almost dominated the computer vision and had achieved considerable success in different levels o... |
Title: Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 Abstract: The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities. Additionally, adversaries con... |
Title: Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment Abstract: Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains. While fitness apps are becoming popular, they lack the functionality to detect errors in workout form. De... |
Title: State-of-the-Art in the Architecture, Methods and Applications of StyleGAN Abstract: Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support ... |
Title: Robust Training under Label Noise by Over-parameterization Abstract: Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, when the training data is corrupted, it has been well-known that o... |
Title: Understanding Contrastive Learning Requires Incorporating Inductive Biases Abstract: Contrastive learning is a popular form of self-supervised learning that encourages augmentations (views) of the same input to have more similar representations compared to augmentations of different inputs. Recent attempts to th... |
Title: AI-based approach for improving the detection of blood doping in sports Abstract: Sports officials around the world are facing incredible challenges due to the unfair means of practices performed by the athletes to improve their performance in the game. It includes the intake of hormonal based drugs or transfusi... |
Title: SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design Abstract: Metasurfaces have received a lot of attentions recently due to their versatile capability in manipulating electromagnetic wave. Advanced designs to satisfy multiple objectives with non-linear constraints have motiv... |
Title: Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems Abstract: Multiscale modeling is an effective approach for investigating multiphysics systems with largely disparate size features, where models with different resolutions or heterogeneous descriptions are c... |
Title: A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs Abstract: We propose a novel robust decentralized graph clustering algorithm that is provably equivalent to the popular spectral clustering approach. Our proposed method uses the existing wave equation clustering algorithm that... |
Title: GCN-Transformer for short-term passenger flow prediction on holidays in urban rail transit systems Abstract: The short-term passenger flow prediction of the urban rail transit system is of great significance for traffic operation and management. The emerging deep learning-based models provide effective methods t... |
Title: Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks Abstract: Theoretical uncertainty limits our ability to extract cosmological information from baryonic fields such as the thermal Sunyaev-Zel'dovich (tSZ) effect. Being sourced by the electron pressure field, ... |
Title: The complexity of quantum support vector machines Abstract: Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such model... |
Title: Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction? Abstract: Mean-Field Control (MFC) is a powerful tool to solve Multi-Agent Reinforcement Learning (MARL) problems. Recent studies have shown that MFC can well-approximate MARL when the pop... |
Title: A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data Abstract: Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines ... |
Title: Local and Global GANs with Semantic-Aware Upsampling for Image Generation Abstract: In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is the difficulty in generating small objects and detailed local textures. To address th... |
Title: Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping Abstract: In this paper we develop a method for mapping forest mortality in the forest-tundra ecotone using satellite data from heterogeneous sensors. We use medium resolution imagery in order to provide the c... |
Title: LISA: Learning Interpretable Skill Abstractions from Language Abstract: Learning policies that effectually utilize language instructions in complex, multi-task environments is an important problem in imitation learning. While it is possible to condition on the entire language instruction directly, such an approa... |
Title: Differentiable Matrix Elements with MadJax Abstract: MadJax is a tool for generating and evaluating differentiable matrix elements of high energy scattering processes. As such, it is a step towards a differentiable programming paradigm in high energy physics that facilitates the incorporation of high energy phys... |
Title: Structure from Voltage Abstract: Effective resistance (ER) is an attractive way to interrogate the structure of graphs. It is an alternative to computing the eigen-vectors of the graph Laplacian. Graph laplacians are used to find low dimensional structures in high dimensional data. Here too, ER based analysis ha... |
Title: Robust Multi-Agent Bandits Over Undirected Graphs Abstract: We consider a multi-agent multi-armed bandit setting in which $n$ honest agents collaborate over a network to minimize regret but $m$ malicious agents can disrupt learning arbitrarily. Assuming the network is the complete graph, existing algorithms incu... |
Title: Deep Camera Pose Regression Using Pseudo-LiDAR Abstract: An accurate and robust large-scale localization system is an integral component for active areas of research such as autonomous vehicles and augmented reality. To this end, many learning algorithms have been proposed that predict 6DOF camera pose from RGB ... |
Title: Amortized Proximal Optimization Abstract: We propose a framework for online meta-optimization of parameters that govern optimization, called Amortized Proximal Optimization (APO). We first interpret various existing neural network optimizers as approximate stochastic proximal point methods which trade off the cu... |
Title: Dynamic N:M Fine-grained Structured Sparse Attention Mechanism Abstract: Transformers are becoming the mainstream solutions for various tasks like NLP and Computer vision. Despite their success, the high complexity of the attention mechanism hinders them from being applied to latency-sensitive tasks. Tremendous ... |
Title: Distributed randomized Kaczmarz for the adversarial workers Abstract: Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems. Here, we propose an iterative approach that is advers... |
Title: Estimating causal effects with optimization-based methods: A review and empirical comparison Abstract: In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate... |
Title: ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction Abstract: In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect prediction. ApacheJIT consists of clean and bug-inducing software changes in popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239 bug-inducing ... |
Title: MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment Abstract: DeepFakes are synthetic videos generated by swapping a face of an original image with the face of somebody else. In this paper, we describe our work to develop general, deep learning-based models to classify DeepFake ... |
Title: Pedagogical Demonstrations and Pragmatic Learning in Artificial Tutor-Learner Interactions Abstract: When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstration) or by giving demonstratio... |
Title: GraphWorld: Fake Graphs Bring Real Insights for GNNs Abstract: Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets are currently used to evaluate new models. This continued reliance on a handful of datasets provides minimal insight into the performance differences ... |
Title: Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline Abstract: The sensor to shooter timeline is affected by two main variables: satellite positioning and asset positioning. Speeding up satellite positioning by adding more sensors or by decreasing processing time is important only i... |
Title: Neural Ordinary Differential Equations for Nonlinear System Identification Abstract: Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their predictive performance with current state... |
Title: On classification of strategic agents who can both game and improve Abstract: In this work, we consider classification of agents who can both game and improve. For example, people wishing to get a loan may be able to take some actions that increase their perceived credit-worthiness and others that also increase ... |
Title: Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach Abstract: We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from high-dimensional and noisy observations, where the datasets are assumed to be sampled fro... |
Title: Learning Neural Hamiltonian Dynamics: A Methodological Overview Abstract: The past few years have witnessed an increased interest in learning Hamiltonian dynamics in deep learning frameworks. As an inductive bias based on physical laws, Hamiltonian dynamics endow neural networks with accurate long-term predictio... |
Title: On Testability and Goodness of Fit Tests in Missing Data Models Abstract: Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acyclic graph. The validity of results using such techniques rel... |
Title: Setting Fair Incentives to Maximize Improvement Abstract: We consider the problem of helping agents improve by setting short-term goals. Given a set of target skill levels, we assume each agent will try to improve from their initial skill level to the closest target level within reach or do nothing if no target ... |
Title: Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach Abstract: The increasing market penetration of electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system. Sin... |
Title: The Concordance Index decomposition: a measure for a deeper understanding of survival prediction models Abstract: The Concordance Index (C-index) is a commonly used metric in Survival Analysis to evaluate how good a prediction model is. This paper proposes a decomposition of the C-Index into a weighted harmonic ... |
Title: Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning Abstract: Machine learning techniques including neural networks are popular tools for materials and chemical scientists with applications that may provide viable alternative methods in the analysis of structure and energetics of sy... |
Title: Performance of Distribution Regression with Doubling Measure under the seek of Closest Point Abstract: We study the distribution regression problem assuming the distribution of distributions has a doubling measure larger than one. First, we explore the geometry of any distributions that has doubling measure larg... |
Title: Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers Abstract: As technology advances, the need for safe, efficient, and collaborative human-robot-teams has become increasingly important. One of the most fundamental collaborative tasks in any setting is the object handover. Human-to-robot h... |
Title: GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks Abstract: Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational. A unique property of GCNs is that its two primary ... |
Title: On Testability of the Front-Door Model via Verma Constraints Abstract: The front-door criterion can be used to identify and compute causal effects despite the existence of unmeasured confounders between a treatment and outcome. However, the key assumptions -- (i) the existence of a variable (or set of variables)... |
Title: Do Transformers use variable binding? Abstract: Increasing the explainability of deep neural networks (DNNs) requires evaluating whether they implement symbolic computation. One central symbolic capacity is variable binding: linking an input value to an abstract variable held in system-internal memory. Prior wor... |
Title: When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee Abstract: In this paper, we propose systematic and efficient gradient-based methods for both one-way and two-way partial AUC (pAUC) maximization that are applicable to deep learning. We propose new formulations of ... |
Title: NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History Abstract: Systematic techniques to improve quality of deep neural networks (DNNs) are critical given the increasing demand for practical applications including safety-critical ones. The key challenge comes from the little cont... |
Title: Layer Adaptive Deep Neural Networks for Out-of-distribution Detection Abstract: During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels. While features at different layers could summarize the important factors of the inputs at v... |
Title: Private Frequency Estimation via Projective Geometry Abstract: In this work, we propose a new algorithm ProjectiveGeometryResponse (PGR) for locally differentially private (LDP) frequency estimation. For a universe size of $k$ and with $n$ users, our $\varepsilon$-LDP algorithm has communication cost $\lceil\log... |
Title: Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks Abstract: Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif prediction and so on. Many works have r... |
Title: Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings Abstract: In order to equip NLP systems with selective prediction capability, several task-specific approaches have been proposed. However, which approaches work best across tasks or even if they consistently... |
Title: FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers Abstract: As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data priva... |
Title: TRILLsson: Distilled Universal Paralinguistic Speech Representations Abstract: Recent advances in self-supervision have dramatically improved the quality of speech representations. However, deployment of state-of-the-art embedding models on devices has been restricted due to their limited public availability and... |
Title: How certain are your uncertainties? Abstract: Having a measure of uncertainty in the output of a deep learning method is useful in several ways, such as in assisting with interpretation of the outputs, helping build confidence with end users, and for improving the training and performance of the networks. Theref... |
Title: An Information-Theoretic Framework for Supervised Learning Abstract: Each year, deep learning demonstrates new and improved empirical results with deeper and wider neural networks. Meanwhile, with existing theoretical frameworks, it is difficult to analyze networks deeper than two layers without resorting to cou... |
Title: Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs Abstract: Question answering over temporal knowledge graphs (KGs) efficiently uses facts contained in a temporal KG, which records entity relations and when they occur in time, to answer natural language questions (e.g., "Who was th... |
Title: Private Convex Optimization via Exponential Mechanism Abstract: In this paper, we study private optimization problems for non-smooth convex functions $F(x)=\mathbb{E}_i f_i(x)$ on $\mathbb{R}^d$. We show that modifying the exponential mechanism by adding an $\ell_2^2$ regularizer to $F(x)$ and sampling from $\pi... |
Title: A Domain-Theoretic Framework for Robustness Analysis of Neural Networks Abstract: We present a domain-theoretic framework for validated robustness analysis of neural networks. We first analyze the global robustness of a general class of networks. Then, using the fact that, over finite-dimensional Banach spaces, ... |
Title: Automatic Depression Detection via Learning and Fusing Features from Visual Cues Abstract: Depression is one of the most prevalent mental disorders, which seriously affects one's life. Traditional depression diagnostics commonly depends on rating with scales, which can be labor-intensive and subjective. In this ... |
Title: Explainability for identification of vulnerable groups in machine learning models Abstract: If a prediction model identifies vulnerable individuals or groups, the use of that model may become an ethical issue. But can we know that this is what a model does? Machine learning fairness as a field is focused on the ... |
Title: Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise Abstract: In this paper, we introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP techniques, including bandlimited filtering and node sampling, to estimate sampl... |
Title: Differentially private training of residual networks with scale normalisation Abstract: The training of neural networks with Differentially Private Stochastic Gradient Descent offers formal Differential Privacy guarantees but introduces accuracy trade-offs. In this work, we propose to alleviate these trade-offs ... |
Title: Machine Learning for Particle Flow Reconstruction at CMS Abstract: We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruc... |
Title: Towards IID representation learning and its application on biomedical data Abstract: Due to the heterogeneity of real-world data, the widely accepted independent and identically distributed (IID) assumption has been criticized in recent studies on causality. In this paper, we argue that instead of being a questi... |
Title: Affordance Learning from Play for Sample-Efficient Policy Learning Abstract: Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal. To this end, ... |
Title: Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method Abstract: Non-affine parametric dependencies, nonlinearities and advection-dominated regimes of the model of interest can result in a slow Kolmogorov n-width decay, which precludes the realization of efficient reduced-ord... |
Title: Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization Abstract: Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attemp... |
Title: Approximating a deep reinforcement learning docking agent using linear model trees Abstract: Deep reinforcement learning has led to numerous notable results in robotics. However, deep neural networks (DNNs) are unintuitive, which makes it difficult to understand their predictions and strongly limits their potent... |
Title: Addressing Randomness in Evaluation Protocols for Out-of-Distribution Detection Abstract: Deep Neural Networks for classification behave unpredictably when confronted with inputs not stemming from the training distribution. This motivates out-of-distribution detection (OOD) mechanisms. The usual lack of prior in... |
Title: Data-efficient learning of object-centric grasp preferences Abstract: Grasping made impressive progress during the last few years thanks to deep learning. However, there are many objects for which it is not possible to choose a grasp by only looking at an RGB-D image, might it be for physical reasons (e.g., a ha... |
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