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Title: FederatedScope: A Flexible Federated Learning Platform for Heterogeneity Abstract: Although remarkable progress has been made by the existing federated learning (FL) platforms to provide fundamental functionalities for development, these platforms cannot well tackle the challenges brought by the heterogeneity of... |
Title: Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning Abstract: Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a ... |
Title: Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making Abstract: This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support. The study reve... |
Title: Pareto Conditioned Networks Abstract: In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process. The set of optimal policies can grow exponentially with the number of objectives, and recovering all solutions requires an exhaustive exploration of the ... |
Title: Forecasting new diseases in low-data settings using transfer learning Abstract: Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first eme... |
Title: Fine-grained Noise Control for Multispeaker Speech Synthesis Abstract: A text-to-speech (TTS) model typically factorizes speech attributes such as content, speaker and prosody into disentangled representations.Recent works aim to additionally model the acoustic conditions explicitly, in order to disentangle the ... |
Title: Zero-phase angle asteroid taxonomy classification using unsupervised machine learning algorithms Abstract: We are in an era of large catalogs and, thus, statistical analysis tools for large data sets, such as machine learning, play a fundamental role. One example of such a survey is the Sloan Moving Object Catal... |
Title: Semantic Exploration from Language Abstractions and Pretrained Representations Abstract: Effective exploration is a challenge in reinforcement learning (RL). Novelty-based exploration methods can suffer in high-dimensional state spaces, such as continuous partially-observable 3D environments. We address this cha... |
Title: An approach to improving sound-based vehicle speed estimation Abstract: We consider improving the performance of a recently proposed sound-based vehicle speed estimation method. In the original method, an intermediate feature, referred to as the modified attenuation (MA), has been proposed for both vehicle detec... |
Title: Concept Drift Adaptation for CTR Prediction in Online Advertising Systems Abstract: Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying. In practical application, CTR models often serve with high-speed user-generated data streams, whose un... |
Title: Convolutional autoencoders for spatially-informed ensemble post-processing Abstract: Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks often solely rely on location-specific predictor... |
Title: Transformer-Based Self-Supervised Learning for Emotion Recognition Abstract: In order to exploit representations of time-series signals, such as physiological signals, it is essential that these representations capture relevant information from the whole signal. In this work, we propose to use a Transformer-base... |
Title: Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation Abstract: Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications. By ... |
Title: Improved Training of Physics-Informed Neural Networks with Model Ensembles Abstract: Learning the solution of partial differential equations (PDEs) with a neural network (known in the literature as a physics-informed neural network, PINN) is an attractive alternative to traditional solvers due to its elegancy, g... |
Title: Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping Abstract: One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional br... |
Title: FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector Machines Abstract: Neural Networks and related Deep Learning methods are currently at the leading edge of technologies used for classifying objects. However, they generally demand large amounts of time and data for model train... |
Title: ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks Abstract: Multiplication-less neural networks significantly reduce the time and energy cost on the hardware platform, as the compute-intensive multiplications are replaced with lightweight bit-shift operations. However, existi... |
Title: PetroGAN: A novel GAN-based approach to generate realistic, label-free petrographic datasets Abstract: Deep learning architectures have enriched data analytics in the geosciences, complementing traditional approaches to geological problems. Although deep learning applications in geosciences show encouraging sign... |
Title: IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification Abstract: Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progress... |
Title: SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges Abstract: With the advent of functional encryption, new possibilities for computation on encrypted data have arisen. Functional Encryption enables data owners to grant third-party access to perform specified comput... |
Title: Artificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental Continuity Abstract: This article presents an artificial intelligence (AI) architecture intended to simulate the human working memory system as well as the manner in which it is updated iteratively. It featur... |
Title: On unsupervised projections and second order signals Abstract: Linear projections are widely used in the analysis of high-dimensional data. In unsupervised settings where the data harbour latent classes/clusters, the question of whether class discriminatory signals are retained under projection is crucial. In th... |
Title: Learning Object-Centered Autotelic Behaviors with Graph Neural Networks Abstract: Although humans live in an open-ended world and endlessly face new challenges, they do not have to learn from scratch each time they face the next one. Rather, they have access to a handful of previously learned skills, which they ... |
Title: The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink Abstract: Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following bes... |
Title: Metaethical Perspectives on 'Benchmarking' AI Ethics Abstract: Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research and have been developed for a variety of tasks ranging from question answering to facial recognition. An increasingly prominent research ... |
Title: SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles Abstract: A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of the ... |
Title: Machine Learning State-of-the-Art with Uncertainties Abstract: With the availability of data, hardware, software ecosystem and relevant skill sets, the machine learning community is undergoing a rapid development with new architectures and approaches appearing at high frequency every year. In this article, we co... |
Title: Towards Painless Policy Optimization for Constrained MDPs Abstract: We study policy optimization in an infinite horizon, $\gamma$-discounted constrained Markov decision process (CMDP). Our objective is to return a policy that achieves large expected reward with a small constraint violation. We consider the onlin... |
Title: Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization Abstract: In recent years, due to the wider WiFi coverage and the popularization of mobile communication devices, the technology of indoor positioning using WiFi fingerprints has been rapidly developed. Currentl... |
Title: Uniform Complexity for Text Generation Abstract: Powerful language models such as GPT-2 have shown promising results in tasks such as narrative generation which can be useful in an educational setup. These models, however, should be consistent with the linguistic properties of triggers used. For example, if the ... |
Title: Correcting Robot Plans with Natural Language Feedback Abstract: When humans design cost or goal specifications for robots, they often produce specifications that are ambiguous, underspecified, or beyond planners' ability to solve. In these cases, corrections provide a valuable tool for human-in-the-loop robot co... |
Title: Time-Adaptive Recurrent Neural Networks Abstract: Data are often sampled irregularly in time. Dealing with this using Recurrent Neural Networks (RNNs) traditionally involved ignoring the fact, feeding the time differences as additional inputs, or resampling the data. All these methods have their shortcomings. We... |
Title: Worldwide city transport typology prediction with sentence-BERT based supervised learning via Wikipedia Abstract: An overwhelming majority of the world's human population lives in urban areas and cities. Understanding a city's transportation typology is immensely valuable for planners and policy makers whose dec... |
Title: Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios Abstract: When learning to behave in a stochastic environment where safety is critical, such as driving a vehicle in traffic, it is natural for human drivers to plan fallback strategies as a bac... |
Title: A Post-Processing Tool and Feasibility Study for Three-Dimensional Imaging with Electrical Impedance Tomography During Deep Brain Stimulation Surgery Abstract: Electrical impedance tomography (EIT) is a promising technique for biomedical imaging. The strength of EIT is its ability to reconstruct images of the bo... |
Title: Rethinking Machine Learning Model Evaluation in Pathology Abstract: Machine Learning has been applied to pathology images in research and clinical practice with promising outcomes. However, standard ML models often lack the rigorous evaluation required for clinical decisions. Machine learning techniques for natu... |
Title: "FIJO": a French Insurance Soft Skill Detection Dataset Abstract: Understanding the evolution of job requirements is becoming more important for workers, companies and public organizations to follow the fast transformation of the employment market. Fortunately, recent natural language processing (NLP) approaches... |
Title: Mixture-of-experts VAEs can disregard variation in surjective multimodal data Abstract: Machine learning systems are often deployed in domains that entail data from multiple modalities, for example, phenotypic and genotypic characteristics describe patients in healthcare. Previous works have developed multimodal... |
Title: GDC- Generalized Distribution Calibration for Few-Shot Learning Abstract: Few shot learning is an important problem in machine learning as large labelled datasets take considerable time and effort to assemble. Most few-shot learning algorithms suffer from one of two limitations- they either require the design of... |
Title: Towards Generalizable Semantic Product Search by Text Similarity Pre-training on Search Click Logs Abstract: Recently, semantic search has been successfully applied to e-commerce product search and the learned semantic space(s) for query and product encoding are expected to generalize to unseen queries or produc... |
Title: Approximate Top-$m$ Arm Identification with Heterogeneous Reward Variances Abstract: We study the effect of reward variance heterogeneity in the approximate top-$m$ arm identification setting. In this setting, the reward for the $i$-th arm follows a $\sigma^2_i$-sub-Gaussian distribution, and the agent needs to ... |
Title: Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts Abstract: Self-supervised learning (SSL), as a newly emerging unsupervised representation learning paradigm, generally follows a two-stage learning pipeline: 1) learning invariant and discr... |
Title: Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics Abstract: A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this goal, neural message passi... |
Title: Bayes Point Rule Set Learning Abstract: Interpretability is having an increasingly important role in the design of machine learning algorithms. However, interpretable methods tend to be less accurate than their black-box counterparts. Among others, DNFs (Disjunctive Normal Forms) are arguably the most interpreta... |
Title: Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information Abstract: Backdoor attacks insert malicious data into a training set so that, during inference time, it misclassifies inputs that have been patched with a backdoor trigger as the malware specified label. For backdoor attacks to bypass hu... |
Title: Multi-view graph structure learning using subspace merging on Grassmann manifold Abstract: Many successful learning algorithms have been recently developed to represent graph-structured data. For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classificati... |
Title: Maximum entropy optimal density control of discrete-time linear systems and Schr\"odinger bridges Abstract: We consider an entropy-regularized version of optimal density control of deterministic discrete-time linear systems. Entropy regularization, or a maximum entropy (MaxEnt) method for optimal control has att... |
Title: The Importance of Future Information in Credit Card Fraud Detection Abstract: Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively and avoid further frauds. Since human verification is o... |
Title: MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning Abstract: Recent years have seen a paradigm shift towards multi-task learning. This calls for memory and energy-efficient solutions for inference in a multi-task scenario. We propose an algorithm-hardware co-des... |
Title: Settling the Sample Complexity of Model-Based Offline Reinforcement Learning Abstract: This paper is concerned with offline reinforcement learning (RL), which learns using pre-collected data without further exploration. Effective offline RL would be able to accommodate distribution shift and limited data coverag... |
Title: Segmentation-Consistent Probabilistic Lesion Counting Abstract: Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable f... |
Title: Full-Spectrum Out-of-Distribution Detection Abstract: Existing out-of-distribution (OOD) detection literature clearly defines semantic shift as a sign of OOD but does not have a consensus over covariate shift. Samples experiencing covariate shift but not semantic shift are either excluded from the test set or tr... |
Title: Toward More Effective Human Evaluation for Machine Translation Abstract: Improvements in text generation technologies such as machine translation have necessitated more costly and time-consuming human evaluation procedures to ensure an accurate signal. We investigate a simple way to reduce cost by reducing the n... |
Title: Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge Abstract: Experiments remain the gold standard to establish an understanding of fire-related phenomena. A primary goal in designing tests is to uncover the data generating process (i.e., the how and why the observ... |
Title: Graph Ordering Attention Networks Abstract: Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-passing scheme, in which every node aggregates information from its neighbors using a perm... |
Title: Random Similarity Forests Abstract: The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data objects. For example, multi-omics an... |
Title: Transfer Learning for Autonomous Chatter Detection in Machining Abstract: Large-amplitude chatter vibrations are one of the most important phenomena in machining processes. It is often detrimental in cutting operations causing a poor surface finish and decreased tool life. Therefore, chatter detection using mach... |
Title: $\{\text{PF}\}^2\text{ES}$: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization Under Unknown Constraints Abstract: We present Parallel Feasible Pareto Frontier Entropy Search ($\{\text{PF}\}^2$ES) -- a novel information-theoretic acquisition function for multi-objective Ba... |
Title: A Simple Approach to Adversarial Robustness in Few-shot Image Classification Abstract: Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable to adversarial examples, posing a question rega... |
Title: Heterogeneous Acceleration Pipeline for Recommendation System Training Abstract: Recommendation systems are unique as they show a conflation of compute and memory intensity due to their deep learning and massive embedding tables. Training these models typically involve a hybrid CPU-GPU mode, where GPUs accelerat... |
Title: Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study Abstract: Data loss in Structural Health Monitoring (SHM) networks has recently become one of the main challenges for engineers. Therefore, a data recovery method for SHM, generally an expensive procedure, is essential. Lately, so... |
Title: Neural Processes with Stochastic Attention: Paying more attention to the context dataset Abstract: Neural processes (NPs) aim to stochastically complete unseen data points based on a given context dataset. NPs essentially leverage a given dataset as a context representation to derive a suitable identifier for a ... |
Title: Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization Abstract: A major challenge in multi-agent systems is that the system complexity grows dramatically with the number of agents as well as the size of their action spaces, which is typical in... |
Title: Breaking Fair Binary Classification with Optimal Flipping Attacks Abstract: Minimizing risk with fairness constraints is one of the popular approaches to learning a fair classifier. Recent works showed that this approach yields an unfair classifier if the training set is corrupted. In this work, we study the min... |
Title: Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit Abstract: Streamlined weirs which are a nature-inspired type of weir have gained tremendous attention among hydraulic engineers, mainly owing to their established performance w... |
Title: Deep Normed Embeddings for Patient Representation Abstract: We introduce a novel contrastive representation learning objective and a training scheme for clinical time series. Specifically, we project high dimensional E.H.R. data to a closed unit ball of low dimension, encoding geometric priors so that the origin... |
Title: Neural Graph Matching for Modification Similarity Applied to Electronic Document Comparison Abstract: In this paper, we present a novel neural graph matching approach applied to document comparison. Document comparison is a common task in the legal and financial industries. In some cases, the most important diff... |
Title: Modelling Evolutionary and Stationary User Preferences for Temporal Sets Prediction Abstract: Given a sequence of sets, where each set is associated with a timestamp and contains an arbitrary number of elements, the task of temporal sets prediction aims to predict the elements in the subsequent set. Previous stu... |
Title: A Comparative Study of Faithfulness Metrics for Model Interpretability Methods Abstract: Interpretation methods to reveal the internal reasoning processes behind machine learning models have attracted increasing attention in recent years. To quantify the extent to which the identified interpretations truly refle... |
Title: Deep Annotation of Therapeutic Working Alliance in Psychotherapy Abstract: The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment. In practice, the working alliance is estimated from a set of scoring questionnaires in an inventory that both the patient and the th... |
Title: Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems Abstract: This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting. We study a system of interconnected agents over a graph and a distribu... |
Title: Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering Abstract: Price Per Unit (PPU) is an essential information for consumers shopping on e-commerce websites when comparing products. Finding total quantity in a product is required for computing PPU, which is not alwa... |
Title: FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning Abstract: The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE h... |
Title: Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature Representation Abstract: Speech Emotion Recognition (SER) is a fundamental task to predict the emotion label from speech data. Recent works mostly focus on using convolutional neural networks~(CNNs) to learn local attention map on fixed-s... |
Title: Convolutional recurrent autoencoder network for learning underwater ocean acoustics Abstract: Underwater ocean acoustics is a complex physical phenomenon involving not only widely varying physical parameters and dynamical scales but also uncertainties in the ocean parameters. Thus, it is difficult to construct g... |
Title: Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications Abstract: Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring. In the recent past, research focused exte... |
Title: Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting Abstract: Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in producing rational and factual responses. However, to establish long-term relationships with users, the KDG model needs the capability to generat... |
Title: When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning? Abstract: Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood that offline RL is able to extract good polic... |
Title: Continual Predictive Learning from Videos Abstract: Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at all times. In practice, however, different prediction tasks may arrive sequential... |
Title: X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks Abstract: In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image. To address these tasks, we propose X-DETR, whose architect... |
Title: Malware Analysis with Symbolic Execution and Graph Kernel Abstract: Malware analysis techniques are divided into static and dynamic analysis. Both techniques can be bypassed by circumvention techniques such as obfuscation. In a series of works, the authors have promoted the use of symbolic executions combined wi... |
Title: NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks Abstract: Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been developed to this end, state-of-the-art AI sys... |
Title: Local Random Feature Approximations of the Gaussian Kernel Abstract: A fundamental drawback of kernel-based statistical models is their limited scalability to large data sets, which requires resorting to approximations. In this work, we focus on the popular Gaussian kernel and on techniques to linearize kernel-b... |
Title: Positive Feature Values Prioritized Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Hierarchical Feature Spaces Abstract: The Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE-TAN) classifier is a semi-naive Bayesian model that learns a type of hierarchical redundanc... |
Title: An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning Abstract: Communication is crucial in multi-agent reinforcement learning when agents are not able to observe the full state of the environment. The most common approach to allow learned communication between ... |
Title: A Robust Learning Rule for Soft-Bounded Memristive Synapses Competitive with Supervised Learning in Standard Spiking Neural Networks Abstract: Memristive devices are a class of circuit elements that shows great promise as future building block for brain-inspired computing. One influential view in theoretical neu... |
Title: Medusa: Universal Feature Learning via Attentional Multitasking Abstract: Recent approaches to multi-task learning (MTL) have focused on modelling connections between tasks at the decoder level. This leads to a tight coupling between tasks, which need retraining if a new task is inserted or removed. We argue tha... |
Title: Unsupervised Anomaly and Change Detection with Multivariate Gaussianization Abstract: Anomaly detection is a field of intense research. Identifying low probability events in data/images is a challenging problem given the high-dimensionality of the data, especially when no (or little) information about the anomal... |
Title: Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model Abstract: Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutio... |
Title: Hierarchical Quality-Diversity for Online Damage Recovery Abstract: Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-trained skills can enable robots to adapt to unforeseen mechanical dam... |
Title: PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing Risks Abstract: Time-to-event analysis (survival analysis) is used when the outcome or the response of interest is the time until a pre-specified event occurs. Time-to-event data are sometimes discrete either because time ... |
Title: On Top-$k$ Selection from $m$-wise Partial Rankings via Borda Counting Abstract: We analyze the performance of the Borda counting algorithm in a non-parametric model. The algorithm needs to utilize probabilistic rankings of the items within $m$-sized subsets to accurately determine which items are the overall to... |
Title: Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning Abstract: Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elasticity value, which neglects information such as t... |
Title: BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis Abstract: Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications. This is partly due to the transparency associated with the underpinning ledgers, wh... |
Title: A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes Abstract: Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are cru... |
Title: CyNER: A Python Library for Cybersecurity Named Entity Recognition Abstract: Open Cyber threat intelligence (OpenCTI) information is available in an unstructured format from heterogeneous sources on the Internet. We present CyNER, an open-source python library for cybersecurity named entity recognition (NER). Cy... |
Title: Backdoor Attack against NLP models with Robustness-Aware Perturbation defense Abstract: Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), such that the attacked model performs well on benign samples, whereas its prediction will be maliciously changed if the hidden backdoor is act... |
Title: Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks Abstract: Deep neural networks achieve remarkable performance in multiple fields. However, after proper training they suffer from an inherent vulnerability against adversarial examples (AEs). In this work we shed light on inner r... |
Title: Membership-Mappings for Practical Secure Distributed Deep Learning Abstract: This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption. The impracticality issue of secure machine (deep) learning wit... |
Title: GORDA: Graph-based ORientation Distribution Analysis of SLI scatterometry Patterns of Nerve Fibres Abstract: Scattered Light Imaging (SLI) is a novel approach for microscopically revealing the fibre architecture of unstained brain sections. The measurements are obtained by illuminating brain sections from differ... |
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