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Title: Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks Abstract: The minimum cost multicut problem is the NP-hard/APX-hard combinatorial optimization problem of partitioning a real-valued edge-weighted graph such as to minimize the total cost of the partition... |
Title: Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete Abstract: We consider the algorithmic problem of finding the optimal weights and biases for a two-layer fully connected neural network to fit a given set of data points. This problem is known as empirical risk minimization in the machine le... |
Title: Synthetic Graph Generation to Benchmark Graph Learning Abstract: Graph learning algorithms have attained state-of-the-art performance on many graph analysis tasks such as node classification, link prediction, and clustering. It has, however, become hard to track the field's burgeoning progress. One reason is due... |
Title: Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS Abstract: Nowadays, with the rising number of sensors in sectors such as healthcare and industry, the problem of multivariate time series classification (MTSC) is getting increasingly relevant and is a prime target fo... |
Title: Aligned Weight Regularizers for Pruning Pretrained Neural Networks Abstract: While various avenues of research have been explored for iterative pruning, little is known what effect pruning has on zero-shot test performance and its potential implications on the choice of pruning criteria. This pruning setup is pa... |
Title: Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck Abstract: Recent advances in sophisticated synthetic speech generated from text-to-speech (TTS) or voice conversion (VC) systems cause threats to the existing automatic speaker verification (ASV) systems. Since such synthetic speech is... |
Title: Re-examining Distillation For Continual Object Detection Abstract: Training models continually to detect and classify objects, from new classes and new domains, remains an open problem. In this work, we conduct a thorough analysis of why and how object detection models forget catastrophically. We focus on distil... |
Title: SAM-kNN Regressor for Online Learning in Water Distribution Networks Abstract: Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to consumers. In order to guarantee a working network at ... |
Title: Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding Abstract: Grounding temporal video segments described in natural language queries effectively and efficiently is a crucial capability needed in vision-and-language fields. In this paper, we deal with the fast video temporal ground... |
Title: SHiFT: An Efficient, Flexible Search Engine for Transfer Learning Abstract: Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enables practitioners and researchers to unleash the potential ... |
Title: Value Gradient weighted Model-Based Reinforcement Learning Abstract: Model-based reinforcement learning (MBRL) is a sample efficient technique to obtain control policies, yet unavoidable modeling errors often lead performance deterioration. The model in MBRL is often solely fitted to reconstruct dynamics, state ... |
Title: A single Long Short-Term Memory network for enhancing the prediction of path-dependent plasticity with material heterogeneity and anisotropy Abstract: This study presents the applicability of conventional deep recurrent neural networks (RNN) to predict path-dependent plasticity associated with material heterogen... |
Title: On scientific understanding with artificial intelligence Abstract: Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracle would revolutionize science and technology as we know them. Howe... |
Title: Event Log Sampling for Predictive Monitoring Abstract: Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predicti... |
Title: Feasibility of nowcasting SDG indicators: a comprehensive survey Abstract: The 2030 Agenda and accompanying Sustainable Development Goals (SDGs) are vital in guiding national and global policy. However, many of the SDG indicators used to measure progress toward those goals suffer from long publication lags. Nowc... |
Title: Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques Abstract: Dengue fever is a vector-borne disease mostly endemic to tropical and subtropical countries that affect millions every year and is considered a significant burden for public health. Its geographic distr... |
Title: Satellite Monitoring of Terrestrial Plastic Waste Abstract: Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste.... |
Title: FedRecAttack: Model Poisoning Attack to Federated Recommendation Abstract: Federated Recommendation (FR) has received considerable popularity and attention in the past few years. In FR, for each user, its feature vector and interaction data are kept locally on its own client thus are private to others. Without t... |
Title: Which Tricks are Important for Learning to Rank? Abstract: Nowadays, state-of-the-art learning-to-rank (LTR) methods are based on gradient-boosted decision trees (GBDT). The most well-known algorithm is LambdaMART that was proposed more than a decade ago. Recently, several other GBDT-based ranking algorithms wer... |
Title: The Group Loss++: A deeper look into group loss for deep metric learning Abstract: Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into differen... |
Title: Matrix Completion with Sparse Noisy Rows Abstract: Exact matrix completion and low rank matrix estimation problems has been studied in different underlying conditions. In this work we study exact low-rank completion under non-degenerate noise model. Non-degenerate random noise model has been previously studied b... |
Title: Survey of Matrix Completion Algorithms Abstract: Matrix completion problem has been investigated under many different conditions since Netflix announced the Netflix Prize problem. Many research work has been done in the field once it has been discovered that many real life dataset could be estimated with a low-r... |
Title: Deep Learning for Spectral Filling in Radio Frequency Applications Abstract: Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF) channels are increasingly congested with new kinds of devices, which carry unique and diverse communication needs. This poses complex challenges in modern digital c... |
Title: CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning Abstract: In a practical setting towards better generalization abilities of client models for realizing robust personalized Federated Learning (FL) systems, efficient model aggregation methods have been considered as a critical re... |
Title: Causality, Causal Discovery, and Causal Inference in Structural Engineering Abstract: Much of our experiments are designed to uncover the cause(s) and effect(s) behind a data generating mechanism (i.e., phenomenon) we happen to be interested in. Uncovering such relationships allows us to identify the true workin... |
Title: Con$^{2}$DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations Abstract: In this work, we present Con$^{2}$DA, a simple framework that extends recent advances in semi-supervised learning to the semi-supervised domain adaptation (SSDA) problem. Our framew... |
Title: Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection Abstract: The adoption of advanced deep learning (DL) architecture in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets. To this end, this work introduces the application of speech embeddings e... |
Title: DAD: Data-free Adversarial Defense at Test Time Abstract: Deep models are highly susceptible to adversarial attacks. Such attacks are carefully crafted imperceptible noises that can fool the network and can cause severe consequences when deployed. To encounter them, the model requires training data for adversari... |
Title: Coarse-to-Fine Q-attention with Learned Path Ranking Abstract: We propose Learned Path Ranking (LPR), a method that accepts an end-effector goal pose, and learns to rank a set of goal-reaching paths generated from an array of path generating methods, including: path planning, Bezier curve sampling, and a learned... |
Title: Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization Abstract: In this paper we revisit the problem of differentially private empirical risk minimization (DP-ERM) and stochastic convex optimization (DP-SCO). We show that a well-studied continuous time algorithm from stati... |
Title: Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering Abstract: Deep learning has become the mainstream methodological choice for analyzing and interpreting whole-slide digital pathology images (WSIs). It is commonly assumed that tumor regions carry most predictive informat... |
Title: Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning Abstract: In this letter, we study the energy efficiency (EE) optimisation of unmanned aerial vehicles (UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agent reinforcement learning approaches... |
Title: DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic Segmentation Abstract: Deep learning approaches achieve prominent success in 3D semantic segmentation. However, collecting densely annotated real-world 3D datasets is extremely time-consuming and expensive. Training models on synthetic data... |
Title: Modern Views of Machine Learning for Precision Psychiatry Abstract: In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine lear... |
Title: Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding Abstract: A fundamental question in designing lossy data compression schemes is how well one can do in comparison with the rate-distortion function, which describes the known theoretical limits of lossy compression. ... |
Title: SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators Abstract: We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eig... |
Title: Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting Abstract: Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts witho... |
Title: Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection Abstract: Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vast... |
Title: More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability Abstract: Structural global parameter identifiability indicates whether one can determine a parameter's value from given inputs and outputs in the absence of noise. If a given model has parameters for which there may be inf... |
Title: Stuttgart Open Relay Degradation Dataset (SOReDD) Abstract: Real-life industrial use cases for machine learning oftentimes involve heterogeneous and dynamic assets, processes and data, resulting in a need to continuously adapt the learning algorithm accordingly. Industrial transfer learning offers to lower the e... |
Title: Estimating Social Influence from Observational Data Abstract: We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The key challenge is that shared behavior between friends could be equally explained by influence or by two other co... |
Title: Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection? Abstract: Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attention. This trend has led researchers to com... |
Title: APP: Anytime Progressive Pruning Abstract: With the latest advances in deep learning, there has been a lot of focus on the online learning paradigm due to its relevance in practical settings. Although many methods have been investigated for optimal learning settings in scenarios where the data stream is continuo... |
Title: Characterizing Parametric and Convergence Stability in Nonconvex and Nonsmooth Optimizations: A Geometric Approach Abstract: We consider stability issues in minimizing a continuous (probably parameterized, nonconvex and nonsmooth) real-valued function $f$. We call a point stationary if all its possible direction... |
Title: Scalable Spike-and-Slab Abstract: Spike-and-slab priors are commonly used for Bayesian variable selection, due to their interpretability and favorable statistical properties. However, existing samplers for spike-and-slab posteriors incur prohibitive computational costs when the number of variables is large. In t... |
Title: MultiMAE: Multi-modal Multi-task Masked Autoencoders Abstract: We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optionally accept additional modalities of information in the input besides th... |
Title: End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks Abstract: We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade ... |
Title: Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks Abstract: The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionalit... |
Title: Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data Abstract: Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step furth... |
Title: Deep Image: A precious image based deep learning method for online malware detection in IoT Environment Abstract: The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Researchers in the fie... |
Title: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances Abstract: Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However... |
Title: "This is my unicorn, Fluffy": Personalizing frozen vision-language representations Abstract: Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V&L problems. However, it is unclear how they can be used for reasoning about user-specific visual conc... |
Title: Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos Abstract: We propose to forecast future hand-object interactions given an egocentric video. Instead of predicting action labels or pixels, we directly predict the hand motion trajectory and the future contact points on the next active o... |
Title: MaxViT: Multi-Axis Vision Transformer Abstract: Transformers have recently gained significant attention in the computer vision community. However, the lack of scalability of self-attention mechanisms with respect to image size has limited their wide adoption in state-of-the-art vision backbones. In this paper we... |
Title: QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration Abstract: The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries. On the contrary, although some work have proved that Quadratic Deep Neuron... |
Title: Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI Abstract: Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients. Making ... |
Title: Learning to Accelerate by the Methods of Step-size Planning Abstract: Gradient descent is slow to converge for ill-conditioned problems and non-convex problems. An important technique for acceleration is step-size adaptation. The first part of this paper contains a detailed review of step-size adaptation methods... |
Title: Heterogeneous Autoencoder Empowered by Quadratic Neurons Abstract: Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic function. Employing such a novel type of neurons offers a new perspectiv... |
Title: Forestry digital twin with machine learning in Landsat 7 data Abstract: Modeling forests using historical data allows for more accurately evolution analysis, thus providing an important basis for other studies. As a recognized and effective tool, remote sensing plays an important role in forestry analysis. We ca... |
Title: Convolutional Neural Networks for Image Spam Detection Abstract: Spam can be defined as unsolicited bulk email. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on... |
Title: Exemplar Learning for Medical Image Segmentation Abstract: Medical image annotation typically requires expert knowledge and hence incurs time-consuming and expensive data annotation costs. To reduce this burden, we propose a novel learning scenario, Exemplar Learning (EL), to explore automated learning processes... |
Title: BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster Abstract: Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for both experimentation and production deployment... |
Title: Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation: Correctly Choosing how to Choose Correctly Abstract: Aggregating successfully the choices regarding a given decision problem made by the multiple collective members into a single solution is essential for exploiting the collective's intelli... |
Title: Forward Signal Propagation Learning Abstract: We propose a new learning algorithm for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to backpropagation. In forward signal propagation learning (sigprop), there is only the forward path for learning and in... |
Title: Generalized Zero Shot Learning For Medical Image Classification Abstract: In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized ... |
Title: Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models Abstract: One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced. Training a mode... |
Title: Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access Abstract: Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks. However, the existing model-free le... |
Title: A high-order tensor completion algorithm based on Fully-Connected Tensor Network weighted optimization Abstract: Tensor completion aimes at recovering missing data, and it is one of the popular concerns in deep learning and signal processing. Among the higher-order tensor decomposition algorithms, the recently p... |
Title: Robust Stuttering Detection via Multi-task and Adversarial Learning Abstract: By automatic detection and identification of stuttering, speech pathologists can track the progression of disfluencies of persons who stutter (PWS). In this paper, we investigate the impact of multi-task (MTL) and adversarial learning ... |
Title: Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection Abstract: Convolutional neural networks have enabled significant improvements in medical image-based disease classification. It has, however, become increasingly clear that these models are suscepti... |
Title: Experimental quantum adversarial learning with programmable superconducting qubits Abstract: Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks. Yet, recent theoretical works show... |
Title: Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials Abstract: Emerging multi-material 3D printing techniques have paved the way for the rational design of metamaterials with not only complex geometries but also arbitrary distributions of multiple materials withi... |
Title: The First Principles of Deep Learning and Compression Abstract: The deep learning revolution incited by the 2012 Alexnet paper has been transformative for the field of computer vision. Many problems which were severely limited using classical solutions are now seeing unprecedented success. The rapid proliferatio... |
Title: The Fast Johnson-Lindenstrauss Transform is Even Faster Abstract: The seminal Fast Johnson-Lindenstrauss (Fast JL) transform by Ailon and Chazelle (SICOMP'09) embeds a set of $n$ points in $d$-dimensional Euclidean space into optimal $k=O(\varepsilon^{-2} \ln n)$ dimensions, while preserving all pairwise distanc... |
Title: Towards Infield Navigation: leveraging simulated data for crop row detection Abstract: Agricultural datasets for crop row detection are often bound by their limited number of images. This restricts the researchers from developing deep learning based models for precision agricultural tasks involving crop row dete... |
Title: A Unit-Consistent Tensor Completion with Applications in Recommender Systems Abstract: In this paper we introduce a new consistency-based approach for defining and solving nonnegative/positive matrix and tensor completion problems. The novelty of the framework is that instead of artificially making the problem w... |
Title: Achieving Long-Term Fairness in Sequential Decision Making Abstract: In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool f... |
Title: Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning Abstract: In the past years, machine learning (ML) has become a popular approach to support self-adaptation. While ML techniques enable dealing with several problems in self-adaptation, such as scalable decision-making, they are also subje... |
Title: Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging Abstract: Objective: Estimation of the global optima of multiple model parameters is valuable in imaging to form a reliable diagnostic image. Given non convexity of the objective function, it is challenging to avoid from di... |
Title: Compliance Checking with NLI: Privacy Policies vs. Regulations Abstract: A privacy policy is a document that states how a company intends to handle and manage their customers' personal data. One of the problems that arises with these privacy policies is that their content might violate data privacy regulations. ... |
Title: Probabilistic Embeddings with Laplacian Graph Priors Abstract: We introduce probabilistic embeddings using Laplacian priors (PELP). The proposed model enables incorporating graph side-information into static word embeddings. We theoretically show that the model unifies several previously proposed embedding metho... |
Title: Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks Abstract: In the last few years, de novo molecular design using machine learning has made great technical progress but its practical deployment has not been as successful. This is mostly owing to the cost ... |
Title: Multilingual Abusiveness Identification on Code-Mixed Social Media Text Abstract: Social Media platforms have been seeing adoption and growth in their usage over time. This growth has been further accelerated with the lockdown in the past year when people's interaction, conversation, and expression were limited ... |
Title: Automatic Text Summarization Methods: A Comprehensive Review Abstract: One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading. Simplifying the relevant information in the form of a summary will assist many people because the material on any to... |
Title: Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model Abstract: Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk.... |
Title: Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation Abstract: Spatial audio methods are gaining a growing interest due to the spread of immersive audio experiences and applications, such as virtual and augmented reality. For these purposes, 3D audio signals are often acquired throu... |
Title: A Data-Driven Framework for Identifying Investment Opportunities in Private Equity Abstract: The core activity of a Private Equity (PE) firm is to invest into companies in order to provide the investors with profit, usually within 4-7 years. To invest into a company or not is typically done manually by looking a... |
Title: A Survey on Graph Representation Learning Methods Abstract: Graphs representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especi... |
Title: Models and Mechanisms for Fairness in Location Data Processing Abstract: Location data use has become pervasive in the last decade due to the advent of mobile apps, as well as novel areas such as smart health, smart cities, etc. At the same time, significant concerns have surfaced with respect to fairness in dat... |
Title: Policy Learning with Competing Agents Abstract: Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to such policies, competition arises, complicating the estimation of the effect of the po... |
Title: MonoTrack: Shuttle trajectory reconstruction from monocular badminton video Abstract: Trajectory estimation is a fundamental component of racket sport analytics, as the trajectory contains information not only about the winning and losing of each point, but also how it was won or lost. In sports such as badminto... |
Title: Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection Abstract: Anomaly detection has many important applications, such as monitoring industrial equipment. Despite recent advances in anomaly detection with deep-learning methods, it is unclear how existing solutions would perform u... |
Title: An Exploration of Active Learning for Affective Digital Phenotyping Abstract: Some of the most severe bottlenecks preventing widespread development of machine learning models for human behavior include a dearth of labeled training data and difficulty of acquiring high quality labels. Active learning is a paradig... |
Title: Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data Abstract: In many real-world machine learning applications, samples belong to a set of domains e.g., for product reviews each review belongs to a product category. In this paper, we study multi-domain imbalanced learning (MIL), the scen... |
Title: Online No-regret Model-Based Meta RL for Personalized Navigation Abstract: The interaction between a vehicle navigation system and the driver of the vehicle can be formulated as a model-based reinforcement learning problem, where the navigation systems (agent) must quickly adapt to the characteristics of the dri... |
Title: Nonlocal optimization of binary neural networks Abstract: We explore training Binary Neural Networks (BNNs) as a discrete variable inference problem over a factor graph. We study the behaviour of this conversion in an under-parameterized BNN setting and propose stochastic versions of Belief Propagation (BP) and ... |
Title: Fault-Tolerant Deep Learning: A Hierarchical Perspective Abstract: With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications such as autonomous driving and robotics. In this context, reliabili... |
Title: Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control Abstract: Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems (CPS) using the vast amount of data available by ... |
Title: GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning Abstract: Penetration testing (PT) is an efficient network testing and vulnerability mining tool by simulating a hacker's attack for valuable information applied in some areas. Compared with manual PT, int... |
Title: Bimodal Distributed Binarized Neural Networks Abstract: Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precision cou... |
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