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Title: A Regularized Implicit Policy for Offline Reinforcement Learning Abstract: Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy training vulnerable to state-action pairs far from the traini... |
Title: Generalized Optimistic Methods for Convex-Concave Saddle Point Problems Abstract: The optimistic gradient method has seen increasing popularity as an efficient first-order method for solving convex-concave saddle point problems. To analyze its iteration complexity, a recent work [arXiv:1901.08511] proposed an in... |
Title: A Variance-Reduced Stochastic Accelerated Primal Dual Algorithm Abstract: In this work, we consider strongly convex strongly concave (SCSC) saddle point (SP) problems $\min_{x\in\mathbb{R}^{d_x}}\max_{y\in\mathbb{R}^{d_y}}f(x,y)$ where $f$ is $L$-smooth, $f(.,y)$ is $\mu$-strongly convex for every $y$, and $f(x,... |
Title: Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning Abstract: Score-based algorithms that learn the structure of Bayesian networks can be used for both exact and approximate solutions. While approximate learning scales better with the number of variables, it can be computationall... |
Title: Selective Credit Assignment Abstract: Efficient credit assignment is essential for reinforcement learning algorithms in both prediction and control settings. We describe a unified view on temporal-difference algorithms for selective credit assignment. These selective algorithms apply weightings to quantify the c... |
Title: A History of Meta-gradient: Gradient Methods for Meta-learning Abstract: The history of meta-learning methods based on gradient descent is reviewed, focusing primarily on methods that adapt step-size (learning rate) meta-parameters. |
Title: A Barrier Certificate-based Simplex Architecture with Application to Microgrids Abstract: We present Barrier Certificate-based Simplex (BC-Simplex), a new, provably correct design for runtime assurance of continuous dynamical systems. BC-Simplex is centered around the Simplex Control Architecture, which consists... |
Title: Understanding Robust Generalization in Learning Regular Languages Abstract: A key feature of human intelligence is the ability to generalize beyond the training distribution, for instance, parsing longer sentences than seen in the past. Currently, deep neural networks struggle to generalize robustly to such shif... |
Title: Bayes-Optimal Classifiers under Group Fairness Abstract: Machine learning algorithms are becoming integrated into more and more high-stakes decision-making processes, such as in social welfare issues. Due to the need of mitigating the potentially disparate impacts from algorithmic predictions, many approaches ha... |
Title: It's Raw! Audio Generation with State-Space Models Abstract: Developing architectures suitable for modeling raw audio is a challenging problem due to the high sampling rates of audio waveforms. Standard sequence modeling approaches like RNNs and CNNs have previously been tailored to fit the demands of audio, but... |
Title: Overparametrization improves robustness against adversarial attacks: A replication study Abstract: Overparametrization has become a de facto standard in machine learning. Despite numerous efforts, our understanding of how and where overparametrization helps model accuracy and robustness is still limited. To this... |
Title: Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation Abstract: The study of Music Cognition and neural responses to music has been invaluable in understanding human emotions. Brain signals, though, manifest a highly complex structure that makes proc... |
Title: Learning to Control Partially Observed Systems with Finite Memory Abstract: We consider the reinforcement learning problem for partially observed Markov decision processes (POMDPs) with large or even countably infinite state spaces, where the controller has access to only noisy observations of the underlying con... |
Title: Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning Abstract: Gray-box hyperparameter optimization techniques have recently emerged as a promising direction for tuning Deep Learning methods. In this work, we introduce DyHPO, a method that learns to dynamically decide which configuration ... |
Title: An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification Abstract: Recent deep neural network-based device classification studies show that complex-valued neural networks (CVNNs) yield higher classification accuracy than real-valued neural networks (RVNNs). Although this improvement i... |
Title: Pseudo Numerical Methods for Diffusion Models on Manifolds Abstract: Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accel... |
Title: Clustering by the Probability Distributions from Extreme Value Theory Abstract: Clustering is an essential task to unsupervised learning. It tries to automatically separate instances into coherent subsets. As one of the most well-known clustering algorithms, k-means assigns sample points at the boundary to a uni... |
Title: Learning logic programs by discovering where not to search Abstract: The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discov... |
Title: Alternative design of DeepPDNet in the context of image restoration Abstract: This work designs an image restoration deep network relying on unfolded Chambolle-Pock primal-dual iterations. Each layer of our network is built from Chambolle-Pock iterations when specified for minimizing a sum of a $\ell_2$-norm dat... |
Title: $\mathcal{Y}$-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning Abstract: With the success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of p... |
Title: Efficient Continual Learning Ensembles in Neural Network Subspaces Abstract: A growing body of research in continual learning focuses on the catastrophic forgetting problem. While many attempts have been made to alleviate this problem, the majority of the methods assume a single model in the continual learning s... |
Title: Dissecting graph measure performance for node clustering in LFR parameter space Abstract: Graph measures that express closeness or distance between nodes can be employed for graph nodes clustering using metric clustering algorithms. There are numerous measures applicable to this task, and which one performs bett... |
Title: Sparsity Winning Twice: Better Robust Generalization from More Efficient Training Abstract: Recent studies demonstrate that deep networks, even robustified by the state-of-the-art adversarial training (AT), still suffer from large robust generalization gaps, in addition to the much more expensive training costs ... |
Title: Personalized Federated Learning with Exact Stochastic Gradient Descent Abstract: In Federated Learning (FL), datasets across clients tend to be heterogeneous or personalized, and this poses challenges to the convergence of standard FL schemes that do not account for personalization. To address this, we present a... |
Title: A Novel Framework for Brain Tumor Detection Based on Convolutional Variational Generative Models Abstract: Brain tumor detection can make the difference between life and death. Recently, deep learning-based brain tumor detection techniques have gained attention due to their higher performance. However, obtaining... |
Title: Cross-Task Knowledge Distillation in Multi-Task Recommendation Abstract: Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e.g, click, purchase) are treated as individual tasks and jointly trained with a unified model. Our key observati... |
Title: ChemTab: A Physics Guided Chemistry Modeling Framework Abstract: Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evol... |
Title: Interacting Contour Stochastic Gradient Langevin Dynamics Abstract: We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD ca... |
Title: ExAIS: Executable AI Semantics Abstract: Neural networks can be regarded as a new programming paradigm, i.e., instead of building ever-more complex programs through (often informal) logical reasoning in the programmers' mind, complex 'AI' systems are built by optimising generic neural network models with big dat... |
Title: NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks Abstract: Machine Learning (ML) techniques are increasingly adopted to tackle ever-evolving high-profile network attacks, including DDoS, botnet, and ransomware, due to their unique ability to extract complex patterns hidden i... |
Title: Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables Abstract: Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or pre... |
Title: On Optimal Early Stopping: Over-informative versus Under-informative Parametrization Abstract: Early stopping is a simple and widely used method to prevent over-training neural networks. We develop theoretical results to reveal the relationship between the optimal early stopping time and model dimension as well ... |
Title: Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorch Abstract: Linear algebra operations, which are ubiquitous in machine learning, form major performance bottlenecks. The High-Performance Computing community invests significant effort in the development of architecture-specific optimized kernels,... |
Title: Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression Abstract: We examine the necessity of interpolation in overparameterized models, that is, when achieving optimal predictive risk in machine learning problems requires (nearly) interpolating the training data. In parti... |
Title: Equivariant Graph Attention Networks for Molecular Property Prediction Abstract: Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery. Equivariant Graph Neural Networks (GNNs) can simultaneously leverage... |
Title: SOInter: A Novel Deep Energy Based Interpretation Method for Explaining Structured Output Models Abstract: We propose a novel interpretation technique to explain the behavior of structured output models, which learn mappings between an input vector to a set of output variables simultaneously. Because of the comp... |
Title: SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection Abstract: The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy. In this work, we propose a no... |
Title: Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy Abstract: Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian backfitting algorithm ... |
Title: Disentangling Autoencoders (DAE) Abstract: Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory. To the best of our knowledge, this is the first... |
Title: Mining Robust Default Configurations for Resource-constrained AutoML Abstract: Automatic machine learning (AutoML) is a key enabler of the mass deployment of the next generation of machine learning systems. A key desideratum for future ML systems is the automatic selection of models and hyperparameters. We prese... |
Title: Deconstructing Distributions: A Pointwise Framework of Learning Abstract: In machine learning, we traditionally evaluate the performance of a single model, averaged over a collection of test inputs. In this work, we propose a new approach: we measure the performance of a collection of models when evaluated on a ... |
Title: Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation Abstract: In the past decade, Deep Learning (DL) systems have been widely deployed in various domains to facilitate our daily life. Meanwhile, it is extremely challenging to ensure the correctness of DL systems (e.g., due to their intrinsic nond... |
Title: Theoretical Analysis of Deep Neural Networks in Physical Layer Communication Abstract: Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by si... |
Title: Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods Abstract: Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental... |
Title: A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies Abstract: Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Ni\~{n}o-Southern Oscillation regarded as a major source of interannual climate vari... |
Title: RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics Abstract: Modeling stochastic traffic dynamics is critical to developing self-driving cars. Because it is difficult to develop first principle models of cars driven by humans, there is great potential for using data driven approaches in developing traf... |
Title: Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning Abstract: One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments. Recently, data augmentation techniques aiming at enhancing data diversity h... |
Title: Outlier-based Autism Detection using Longitudinal Structural MRI Abstract: Diagnosis of Autism Spectrum Disorder (ASD) using clinical evaluation (cognitive tests) is challenging due to wide variations amongst individuals. Since no effective treatment exists, prompt and reliable ASD diagnosis can enable the effec... |
Title: Learning Low Degree Hypergraphs Abstract: We study the problem of learning a hypergraph via edge detecting queries. In this problem, a learner queries subsets of vertices of a hidden hypergraph and observes whether these subsets contain an edge or not. In general, learning a hypergraph with $m$ edges of maximum ... |
Title: Transferring Adversarial Robustness Through Robust Representation Matching Abstract: With the widespread use of machine learning, concerns over its security and reliability have become prevalent. As such, many have developed defenses to harden neural networks against adversarial examples, imperceptibly perturbed... |
Title: Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection Abstract: As large-scale time-series data can easily be found in real-world applications, multivariate time-series anomaly detection has played an essential role in diverse industries. It en... |
Title: Double Thompson Sampling in Finite stochastic Games Abstract: We consider the trade-off problem between exploration and exploitation under finite discounted Markov Decision Process, where the state transition matrix of the underlying environment stays unknown. We propose a double Thompson sampling reinforcement ... |
Title: AI/ML Algorithms and Applications in VLSI Design and Technology Abstract: An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turna... |
Title: Autonomous Warehouse Robot using Deep Q-Learning Abstract: In warehouses, specialized agents need to navigate, avoid obstacles and maximize the use of space in the warehouse environment. Due to the unpredictability of these environments, reinforcement learning approaches can be applied to complete these tasks. I... |
Title: Toward more generalized Malicious URL Detection Models Abstract: This paper reveals a data bias issue that can severely affect the performance while conducting a machine learning model for malicious URL detection. We describe how such bias can be identified using interpretable machine learning techniques, and fu... |
Title: Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution Abstract: When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer -- the "head"). It is well kno... |
Title: CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories Abstract: This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments. The Curiosity-Conditioned Proximal Tra... |
Title: Multi-task Representation Learning with Stochastic Linear Bandits Abstract: We study the problem of transfer-learning in the setting of stochastic linear bandit tasks. We consider that a low dimensional linear representation is shared across the tasks, and study the benefit of learning this representation in the... |
Title: ICSML: Industrial Control Systems Machine Learning Inference Framework natively executing on IEC 61131-3 compliant devices Abstract: Industrial Control Systems (ICS) have played a catalytic role in enabling the 4th Industrial Revolution. ICS devices like Programmable Logic Controllers (PLCs), automate, monitor, ... |
Title: Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review Abstract: Governments' net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing o... |
Title: Simplified Learning of CAD Features Leveraging a Deep Residual Autoencoder Abstract: In the domain of computer vision, deep residual neural networks like EfficientNet have set new standards in terms of robustness and accuracy. One key problem underlying the training of deep neural networks is the immanent lack o... |
Title: Enabling On-Device Smartphone GPU based Training: Lessons Learned Abstract: Deep Learning (DL) has shown impressive performance in many mobile applications. Most existing works have focused on reducing the computational and resource overheads of running Deep Neural Networks (DNN) inference on resource-constraine... |
Title: Robustness and Accuracy Could Be Reconcilable by (Proper) Definition Abstract: The trade-off between robustness and accuracy has been widely studied in the adversarial literature. Although still controversial, the prevailing view is that this trade-off is inherent, either empirically or theoretically. Thus, we d... |
Title: Model-Agnostic Augmentation for Accurate Graph Classification Abstract: Given a graph dataset, how can we augment it for accurate graph classification? Graph augmentation is an essential strategy to improve the performance of graph-based tasks, and has been widely utilized for analyzing web and social graphs. Ho... |
Title: HCMD-zero: Learning Value Aligned Mechanisms from Data Abstract: Artificial learning agents are mediating a larger and larger number of interactions among humans, firms, and organizations, and the intersection between mechanism design and machine learning has been heavily investigated in recent years. However, m... |
Title: MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning Abstract: In cooperative multi-agent tasks, a team of agents jointly interact with an environment by taking actions, receiving a team reward and observing the next state. During the interactions,... |
Title: The Good Shepherd: An Oracle Agent for Mechanism Design Abstract: From social networks to traffic routing, artificial learning agents are playing a central role in modern institutions. We must therefore understand how to leverage these systems to foster outcomes and behaviors that align with our own values and a... |
Title: Inferring Lexicographically-Ordered Rewards from Preferences Abstract: Modeling the preferences of agents over a set of alternatives is a principal concern in many areas. The dominant approach has been to find a single reward/utility function with the property that alternatives yielding higher rewards are prefer... |
Title: 1-WL Expressiveness Is (Almost) All You Need Abstract: It has been shown that a message passing neural networks (MPNNs), a popular family of neural networks for graph-structured data, are at most as expressive as the first-order Weisfeiler-Leman (1-WL) graph isomorphism test, which has motivated the development ... |
Title: Diffusion Causal Models for Counterfactual Estimation Abstract: We consider the task of counterfactual estimation from observational imaging data given a known causal structure. In particular, quantifying the causal effect of interventions for high-dimensional data with neural networks remains an open challenge.... |
Title: Path of Destruction: Learning an Iterative Level Generator Using a Small Dataset Abstract: We propose a new procedural content generation method which learns iterative level generators from a dataset of existing levels. The Path of Destruction method, as we call it, views level generation as repair; levels are c... |
Title: OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images Abstract: Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce data... |
Title: Low-Dimensional High-Fidelity Kinetic Models for NOX Formation by a Compute Intensification Method Abstract: A novel compute intensification methodology to the construction of low-dimensional, high-fidelity "compact" kinetic models for NOX formation is designed and demonstrated. The method adapts the data intens... |
Title: Permutation Predictions for Non-Clairvoyant Scheduling Abstract: In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a priori unknown processing requirements with the objective to minimize the total (weighted) completion time. We revisit this well-studied problem in a r... |
Title: Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning Abstract: Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previo... |
Title: Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy Abstract: Differentially private GNNs (Graph Neural Networks) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy. In particular, a recent study p... |
Title: Tracking environmental policy changes in the Brazilian Federal Official Gazette Abstract: Even though most of its energy generation comes from renewable sources, Brazil is one of the largest emitters of greenhouse gases in the world, due to intense farming and deforestation of biomes such as the Amazon Rainfores... |
Title: Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation Abstract: Model-based methods for recommender systems have been studied extensively for years. Modern recommender systems usually resort to 1) representation learning models which define user-item preference as the ... |
Title: Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization Abstract: Cross-modal retrieval aims to search for data with similar semantic meanings across different content modalities. However, cross-modal retrieval requires huge amounts of storage and retrieval time since it needs to process data in... |
Title: Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation Abstract: The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviat... |
Title: VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation Abstract: Sparsity of user-to-item rating data becomes one of challenging issues in the recommender systems, which severely deteriorates the recommendation performance. Fortunately, context-aware recommender systems can allevi... |
Title: Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate Abstract: Inhomogeneities in the aortic wall can lead to localized stress accumulations, possibly initiating dissection. In many cases, a dissection results from pathological change... |
Title: A Self-Supervised Descriptor for Image Copy Detection Abstract: Image copy detection is an important task for content moderation. We introduce SSCD, a model that builds on a recent self-supervised contrastive training objective. We adapt this method to the copy detection task by changing the architecture and tra... |
Title: End-to-End High Accuracy License Plate Recognition Based on Depthwise Separable Convolution Networks Abstract: Automatic license plate recognition plays a crucial role in modern transportation systems such as for traffic monitoring and vehicle violation detection. In real-world scenarios, license plate recogniti... |
Title: ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables Abstract: In many anomaly detection tasks, where anomalous data rarely appear and are difficult to collect, training using only normal data is important. Although it is possible to manually create anomalous data using prior knowled... |
Title: A Review of Emerging Research Directions in Abstract Visual Reasoning Abstract: Abstract Visual Reasoning (AVR) problems are commonly used to approximate human intelligence. They test the ability of applying previously gained knowledge, experience and skills in a completely new setting, which makes them particul... |
Title: Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition Abstract: Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech in recent decades, accurate recognition of dysarthric and elderly speech remains highly challenging... |
Title: Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge Abstract: Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning. The majority of t... |
Title: Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches Abstract: Biometric data, such as face images, are often associated with sensitive information (e.g medical, financial, personal government records). Hence, a data breach in a system st... |
Title: VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning Abstract: We propose a simple but powerful data-driven framework for solving highly challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major obstacles in taking a data-driven approach, and present a suite of desi... |
Title: Manage risks in complex engagements by leveraging organization-wide knowledge using Machine Learning Abstract: One of the ways for organizations to continuously get better at executing projects is to learn from their past experience. In large organizations, the different accounts and business units often work in... |
Title: Artificial Intelligence for the Metaverse: A Survey Abstract: Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of... |
Title: Integration of knowledge and data in machine learning Abstract: Scientific research's mandate is to comprehend and explore the world, as well as to improve it based on experience and knowledge. Knowledge embedding and knowledge discovery are two significant methods of integrating knowledge and data. Through know... |
Title: UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations Abstract: Because of disaster, terrestrial base stations (TBS) would be partly crashed. Some user equipments (UE) would be unserved. Deploying unmanned aerial vehicles (UAV) as aerial base stati... |
Title: Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework Abstract: Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents wit... |
Title: Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization Abstract: Human intervention is an effective way to inject human knowledge into the training loop of reinforcement learning, which can bring fast learning and ensured training safety. Given the very limited budget of human intervention, ... |
Title: Statistical Relational Artificial Intelligence with Relative Frequencies: A Contribution to Modelling and Transfer Learning across Domain Sizes Abstract: Dependencies on the relative frequency of a state in the domain are common when modelling probabilistic dependencies on relational data. For instance, the like... |
Title: L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment Abstract: The L3DAS22 Challenge is aimed at encouraging the development of machine learning strategies for 3D speech enhancement and 3D sound localization and detection in office-like environments. This challenge improves and extends the t... |
Title: Same Cause; Different Effects in the Brain Abstract: To study information processing in the brain, neuroscientists manipulate experimental stimuli while recording participant brain activity. They can then use encoding models to find out which brain "zone" (e.g. which region of interest, volume pixel or electroph... |
Title: Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations Abstract: One of the principal uses of physical-space sensors in public safety applications is the detection of unsafe conditions (e.g., release of poisonous gases, weapons in airports, tainted food). However, current de... |
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