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Title: From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlook Abstract: We review the recent programme of using machine-learning to explore the landscape of mathematical problems. With this paradigm as a model for human intuition - complementary to and in contrast with the more formalistic ... |
Title: TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding Abstract: The proliferation of deep learning applications in several areas has led to the rapid adoption of such solutions from an ever-growing number of institutions and companies. These entities' deep neural netw... |
Title: Improving Fraud Detection via Hierarchical Attention-based Graph Neural Network Abstract: Graph neural networks (GNN) have emerged as a powerful tool for fraud detection tasks, where fraudulent nodes are identified by aggregating neighbor information via different relations. To get around such detection, crafty ... |
Title: On Federated Learning with Energy Harvesting Clients Abstract: Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in this paper. The introduction of EH implies that a client's availability t... |
Title: RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph Abstract: With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users' interests and intents. Recently, prior works for us... |
Title: Online Bayesian Recommendation with No Regret Abstract: We introduce and study the online Bayesian recommendation problem for a platform, who can observe a utility-relevant state of a product, repeatedly interacting with a population of myopic users through an online recommendation mechanism. This paradigm is co... |
Title: MIONet: Learning multiple-input operators via tensor product Abstract: As an emerging paradigm in scientific machine learning, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. Several neural operators have been recently developed. However, all t... |
Title: Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation Abstract: Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke. Positron emission tomo... |
Title: Stochastic Strategic Patient Buyers: Revenue maximization using posted prices Abstract: We consider a seller faced with buyers which have the ability to delay their decision, which we call patience. Each buyer's type is composed of value and patience, and it is sampled i.i.d. from a distribution. The seller, usi... |
Title: Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering Abstract: Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may ... |
Title: Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue Reports Abstract: Issue tracking systems are used in the software industry for the facilitation of maintenance activities that keep the software robust and up to date with ever-changing industry requirements. Usually, users report is... |
Title: Adaptive Bandit Convex Optimization with Heterogeneous Curvature Abstract: We consider the problem of adversarial bandit convex optimization, that is, online learning over a sequence of arbitrary convex loss functions with only one function evaluation for each of them. While all previous works assume known and h... |
Title: Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits Abstract: We consider the problem of combining and learning over a set of adversarial bandit algorithms with the goal of adaptively tracking the best one on the fly. The CORRAL algorithm of Agarwal et al. (2017) and its vari... |
Title: From Online Optimization to PID Controllers: Mirror Descent with Momentum Abstract: We study a family of first-order methods with momentum based on mirror descent for online convex optimization, which we dub online mirror descent with momentum (OMDM). Our algorithms include as special cases gradient descent and ... |
Title: The Impact of Using Regression Models to Build Defect Classifiers Abstract: It is common practice to discretize continuous defect counts into defective and non-defective classes and use them as a target variable when building defect classifiers (discretized classifiers). However, this discretization of continuou... |
Title: Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation Abstract: Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays.... |
Title: Evolving Neural Networks with Optimal Balance between Information Flow and Connections Cost Abstract: Evolving Neural Networks (NNs) has recently seen an increasing interest as an alternative path that might be more successful. It has many advantages compared to other approaches, such as learning the architectur... |
Title: An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes Abstract: The modelling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over... |
Title: Learning long-term music representations via hierarchical contextual constraints Abstract: Learning symbolic music representations, especially disentangled representations with probabilistic interpretations, has been shown to benefit both music understanding and generation. However, most models are only applicab... |
Title: On the Convergence of Clustered Federated Learning Abstract: Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1) to learn a shared model to serve all clients with non-IID data, and 2) t... |
Title: Fairness-aware Configuration of Machine Learning Libraries Abstract: This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs. Data-driven software is increasingly applied in social-critical applications where ensuring fairness is of paramount imp... |
Title: Unsupervised Disentanglement with Tensor Product Representations on the Torus Abstract: The current methods for learning representations with auto-encoders almost exclusively employ vectors as the latent representations. In this work, we propose to employ a tensor product structure for this purpose. This way, th... |
Title: StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement Abstract: Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with t... |
Title: Metric Learning-enhanced Optimal Transport for Biochemical Regression Domain Adaptation Abstract: Generalizing knowledge beyond source domains is a crucial prerequisite for many biomedical applications such as drug design and molecular property prediction. To meet this challenge, researchers have used optimal tr... |
Title: Emotion Based Hate Speech Detection using Multimodal Learning Abstract: In recent years, monitoring hate speech and offensive language on social media platforms has become paramount due to its widespread usage among all age groups, races, and ethnicities. Consequently, there have been substantial research effort... |
Title: PQuAD: A Persian Question Answering Dataset Abstract: We present Persian Question Answering Dataset (PQuAD), a crowdsourced reading comprehension dataset on Persian Wikipedia articles. It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable. We examine va... |
Title: Feature Construction and Selection for PV Solar Power Modeling Abstract: Using solar power in the process industry can reduce greenhouse gas emissions and make the production process more sustainable. However, the intermittent nature of solar power renders its usage challenging. Building a model to predict photo... |
Title: Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach Abstract: Vital node identification is the problem of finding nodes of highest importance in complex networks. This problem has crucial applications in various contexts such as viral marketing or controlling the propagation of ... |
Title: A Geometric Understanding of Natural Gradient Abstract: While natural gradients have been widely studied from both theoretical and empirical perspectives, we argue that some fundamental theoretical issues regarding the existence of gradients in infinite dimensional function spaces remain underexplored. We addres... |
Title: The Sample Complexity of One-Hidden-Layer Neural Networks Abstract: We study norm-based uniform convergence bounds for neural networks, aiming at a tight understanding of how these are affected by the architecture and type of norm constraint, for the simple class of scalar-valued one-hidden-layer networks, and i... |
Title: Efficient Natural Gradient Descent Methods for Large-Scale Optimization Problems Abstract: We propose an efficient numerical method for computing natural gradient descent directions with respect to a generic metric in the state space. Our technique relies on representing the natural gradient direction as a solut... |
Title: Supported Policy Optimization for Offline Reinforcement Learning Abstract: Policy constraint methods to offline reinforcement learning (RL) typically utilize parameterization or regularization that constrains the policy to perform actions within the support set of the behavior policy. The elaborative designs of ... |
Title: FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations Abstract: Recent advances in generative adversarial networks have shown that it is possible to generate high-resolution and hyperrealistic images. However, the images produced by GANs are only as fair and representative as the datasets on which they... |
Title: Geometric Graph Representation Learning via Maximizing Rate Reduction Abstract: Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification. Existing graph representation learning methods (e.g., based on random walk and cont... |
Title: Beyond NaN: Resiliency of Optimization Layers in The Face of Infeasibility Abstract: Prior work has successfully incorporated optimization layers as the last layer in neural networks for various problems, thereby allowing joint learning and planning in one neural network forward pass. In this work, we identify a... |
Title: A Tech Hybrid-Recommendation Engine and Personalized Notification: An integrated tool to assist users through Recommendations (Project ATHENA) Abstract: Project ATHENA aims to develop an application to address information overload, primarily focused on Recommendation Systems (RSs) with the personalization and us... |
Title: Fine-Grained Population Mobility Data-Based Community-Level COVID-19 Prediction Model Abstract: Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geographic units. Previous works focus on l... |
Title: Flowformer: Linearizing Transformers with Conservation Flows Abstract: Transformers based on the attention mechanism have achieved impressive success in various areas. However, the attention mechanism has a quadratic complexity, significantly impeding Transformers from dealing with numerous tokens and scaling up... |
Title: On the complexity of All $\varepsilon$-Best Arms Identification Abstract: We consider the question introduced by \cite{Mason2020} of identifying all the $\varepsilon$-optimal arms in a finite stochastic multi-armed bandit with Gaussian rewards. We give two lower bounds on the sample complexity of any algorithm s... |
Title: Graph-adaptive Rectified Linear Unit for Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have achieved remarkable success by extending traditional convolution to learning on non-Euclidean data. The key to the GNNs is adopting the neural message-passing paradigm with two stages: aggregation and updat... |
Title: Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network Abstract: Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network em... |
Title: Reverse Back Propagation to Make Full Use of Derivative Abstract: The development of the back-propagation algorithm represents a landmark in neural networks. We provide an approach that conducts the back-propagation again to reverse the traditional back-propagation process to optimize the input loss at the input... |
Title: Off-Policy Evaluation for Large Action Spaces via Embeddings Abstract: Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems, since it enables offline evaluation of new policies using only historic log data. Unfortunately, when the number of actions is large, existing OP... |
Title: A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation Abstract: Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimen... |
Title: Goal Recognition as Reinforcement Learning Abstract: Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal. These specifications suffer from two key issues. First, encoding these dynamics requires careful design by a domain exper... |
Title: Incremental user embedding modeling for personalized text classification Abstract: Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education. Adaptive user representation learning... |
Title: Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR Abstract: Machine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for... |
Title: Optimal sizing of a holdout set for safe predictive model updating Abstract: Risk models in medical statistics and healthcare machine learning are increasingly used to guide clinical or other interventions. Should a model be updated after a guided intervention, it may lead to its own failure at making accurate p... |
Title: Neural Network Trojans Analysis and Mitigation from the Input Domain Abstract: Deep Neural Networks (DNNs) can learn Trojans (or backdoors) from benign or poisoned data, which raises security concerns of using them. By exploiting such Trojans, the adversary can add a fixed input space perturbation to any given i... |
Title: Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data Abstract: Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a model to reduce recovery unit congestion. We estimate LOS using a variety of machine learning m... |
Title: Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost Abstract: We study the problem of reinforcement learning (RL) with low (policy) switching cost - a problem well-motivated by real-life RL applications in which deployments of new policies are costly and the number of policy updates must be low... |
Title: Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments Abstract: Neural scaling laws define a predictable relationship between a model's parameter count and its performance after training in the form of a power law. However, most research to date has not explicitly inv... |
Title: Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks Abstract: This article proposes Convolutional Neural Network-based Auto Encoder (CNN-AE) to predict location-dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India,... |
Title: Local approximation of operators Abstract: Many applications, such as system identification, classification of time series, direct and inverse problems in partial differential equations, and uncertainty quantification lead to the question of approximation of a non-linear operator between metric spaces $\mathfrak... |
Title: Individual-Level Inverse Reinforcement Learning for Mean Field Games Abstract: The recent mean field game (MFG) formalism has enabled the application of inverse reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of inferring reward signals that can explain demonstrated behavio... |
Title: Distribution augmentation for low-resource expressive text-to-speech Abstract: This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data. Our goal is to increase diversity of text conditionin... |
Title: State-of-the-Art Review of Design of Experiments for Physics-Informed Deep Learning Abstract: This paper presents a comprehensive review of the design of experiments used in the surrogate models. In particular, this study demonstrates the necessity of the design of experiment schemes for the Physics-Informed Neu... |
Title: AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation Abstract: Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust deep learning model requires large, high-quality dat... |
Title: Learning from Randomly Initialized Neural Network Features Abstract: We present the surprising result that randomly initialized neural networks are good feature extractors in expectation. These random features correspond to finite-sample realizations of what we call Neural Network Prior Kernel (NNPK), which is i... |
Title: Fast algorithm for overcomplete order-3 tensor decomposition Abstract: We develop the first fast spectral algorithm to decompose a random third-order tensor over R^d of rank up to O(d^{3/2}/polylog(d)). Our algorithm only involves simple linear algebra operations and can recover all components in time O(d^{6.05}... |
Title: Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search Abstract: Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants. However, for coop... |
Title: Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality Abstract: Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL). Despite the community's increasing interest, there lacks a formal theoretical formulation for the problem. In ... |
Title: Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits Abstract: This paper proposes a class of neural ordinary differential equations parametrized by provably input-to-state stable continuous-time recurrent neural networks. The model dynamics are defined... |
Title: Simultaneous Transport Evolution for Minimax Equilibria on Measures Abstract: Min-max optimization problems arise in several key machine learning setups, including adversarial learning and generative modeling. In their general form, in absence of convexity/concavity assumptions, finding pure equilibria of the un... |
Title: Learn by Challenging Yourself: Contrastive Visual Representation Learning with Hard Sample Generation Abstract: Contrastive learning (CL), a self-supervised learning approach, can effectively learn visual representations from unlabeled data. However, CL requires learning on vast quantities of diverse data to ach... |
Title: Optimizing Random Mixup with Gaussian Differential Privacy Abstract: Differentially private data release receives rising attention in machine learning community. Recently, an algorithm called DPMix is proposed to release high-dimensional data after a random mixup of degree $m$ with differential privacy. However,... |
Title: Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction Abstract: Alleviating the delayed feedback problem is of crucial importance for the conversion rate(CVR) prediction in online advertising. Previous delayed feedback modeling methods using an observation window to balance the tr... |
Title: Extracting Label-specific Key Input Features for Neural Code Intelligence Models Abstract: The code intelligence (CI) models are often black-box and do not offer any insights on the input features that they learn for making correct predictions. This opacity may lead to distrust in their prediction and hamper the... |
Title: Homogenous and Heterogenous Parallel Clustering: An Overview Abstract: Recent advances in computer architecture and networking opened the opportunity for parallelizing the clustering algorithms. This divide-and-conquer strategy often results in better results to centralized clustering with a much-improved time p... |
Title: A Survey on Machine Learning Approaches for Modelling Intuitive Physics Abstract: Research in cognitive science has provided extensive evidence of human cognitive ability in performing physical reasoning of objects from noisy perceptual inputs. Such a cognitive ability is commonly known as intuitive physics. Wit... |
Title: Splitting numerical integration for matrix completion Abstract: Low rank matrix approximation is a popular topic in machine learning. In this paper, we propose a new algorithm for this topic by minimizing the least-squares estimation over the Riemannian manifold of fixed-rank matrices. The algorithm is an adapta... |
Title: D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation Abstract: In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in hand... |
Title: Finding Dynamics Preserving Adversarial Winning Tickets Abstract: Modern deep neural networks (DNNs) are vulnerable to adversarial attacks and adversarial training has been shown to be a promising method for improving the adversarial robustness of DNNs. Pruning methods have been considered in adversarial context... |
Title: Adversarial Graph Contrastive Learning with Information Regularization Abstract: Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from images to graphs. However, most prior works are dire... |
Title: FLHub: a Federated Learning model sharing service Abstract: As easy-to-use deep learning libraries such as Tensorflow and Pytorch are popular, it has become convenient to develop machine learning models. Due to privacy issues with centralized machine learning, recently, federated learning in the distributed comp... |
Title: Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations Abstract: Real world recommendation systems influence a constantly growing set of domains. With deep networks, that now drive such systems, recommendations have been more relevant to the user's interests and tasks. However, they... |
Title: Opinions Vary? Diagnosis First! Abstract: With the advancement of deep learning techniques, an increasing number of methods have been proposed for optic disc and cup (OD/OC) segmentation from the fundus images. Clinically, OD/OC segmentation is often annotated by multiple clinical experts to mitigate the persona... |
Title: EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement Abstract: Multimodal learning has been proven to be an effective method to improve speech enhancement (SE) performance, especially in challenging situations such as low signal-to-noise ratios, speech noise, or unseen noise types. In previous studies, s... |
Title: A Comprehensive Benchmark of Deep Learning Libraries on Mobile Devices Abstract: Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms and hardware do. Unfortunately, no prior work ever d... |
Title: MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts Abstract: Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing focus on curating ben... |
Title: Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture model Abstract: Speaker diarization has been investigated extensively as an important central task for meeting analysis. Recent trend shows that integration of end-to-end neural (EEND)-and clustering... |
Title: Benign Overfitting in Two-layer Convolutional Neural Networks Abstract: Modern neural networks often have great expressive power and can be trained to overfit the training data, while still achieving a good test performance. This phenomenon is referred to as "benign overfitting". Recently, there emerges a line o... |
Title: An Introduction to Neural Data Compression Abstract: Neural compression is the application of neural networks and other machine learning methods to data compression. While machine learning deals with many concepts closely related to compression, entering the field of neural compression can be difficult due to it... |
Title: Deduplicating Training Data Mitigates Privacy Risks in Language Models Abstract: Past work has shown that large language models are susceptible to privacy attacks, where adversaries generate sequences from a trained model and detect which sequences are memorized from the training set. In this work, we show that ... |
Title: Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization Abstract: In the sequential decision making setting, an agent aims to achieve systematic generalization over a large, possibly infinite, set of environments. Such environments are modeled as discrete Markov decision proces... |
Title: A resource-efficient deep learning framework for low-dose brain PET image reconstruction and analysis Abstract: 18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic results, which raises concerns about the poten... |
Title: Reinforcement Learning in Presence of Discrete Markovian Context Evolution Abstract: We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episo... |
Title: SAUTE RL: Almost Surely Safe Reinforcement Learning Using State Augmentation Abstract: Satisfying safety constraints almost surely (or with probability one) can be critical for deployment of Reinforcement Learning (RL) in real-life applications. For example, plane landing and take-off should ideally occur with p... |
Title: Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme Abstract: Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of futur... |
Title: Improved Aggregating and Accelerating Training Methods for Spatial Graph Neural Networks on Fraud Detection Abstract: Graph neural networks (GNNs) have been widely applied to numerous fields. A recent work which combines layered structure and residual connection proposes an improved deep architecture to extend C... |
Title: Conditional Generation Net for Medication Recommendation Abstract: Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. Currently, the recommendation is manually conducted by doctors. However, for complicated cases, like pat... |
Title: Memory Replay with Data Compression for Continual Learning Abstract: Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves the state-of-the-art (SOTA) performance. However, existing work... |
Title: Exact Statistical Inference for Time Series Similarity using Dynamic Time Warping by Selective Inference Abstract: In this paper, we study statistical inference on the similarity/distance between two time-series under uncertain environment by considering a statistical hypothesis test on the distance obtained fro... |
Title: Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction Abstract: For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the co... |
Title: Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in First Trimester 3D Ultrasound Abstract: Segmentation and spatial alignment of ultrasound (US) imaging data acquired in the in first trimester are crucial for monitoring human embryonic growth and development throughout this crucial period of l... |
Title: UnScenE: Toward Unsupervised Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data Abstract: Scenario-based testing is a promising approach to solve the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number... |
Title: An Application of Online Learning to Spacecraft Memory Dump Optimization Abstract: In this paper, we present a real-world application of online learning with expert advice to the field of Space Operations, testing our theory on real-life data coming from the Copernicus Sentinel-6 satellite. We show that in Space... |
Title: KNIFE: Kernelized-Neural Differential Entropy Estimation Abstract: Mutual Information (MI) has been widely used as a loss regularizer for training neural networks. This has been particularly effective when learn disentangled or compressed representations of high dimensional data. However, differential entropy (D... |
Title: What is Next when Sequential Prediction Meets Implicitly Hard Interaction? Abstract: Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most existing methods focus on explicitly hard inter... |
Title: Measurably Stronger Explanation Reliability via Model Canonization Abstract: While rule-based attribution methods have proven useful for providing local explanations for Deep Neural Networks, explaining modern and more varied network architectures yields new challenges in generating trustworthy explanations, sin... |
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