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Title: Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces Abstract: The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost ... |
Title: MLSmellHound: A Context-Aware Code Analysis Tool Abstract: Meeting the rise of industry demand to incorporate machine learning (ML) components into software systems requires interdisciplinary teams contributing to a shared code base. To maintain consistency, reduce defects and ensure maintainability, developers ... |
Title: Learning Regionally Decentralized AC Optimal Power Flows with ADMM Abstract: One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e.g., wind/solar), dispatchable devices (e.g., coal/gas/nu... |
Title: Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis Abstract: Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As a re... |
Title: Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction Abstract: In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict re... |
Title: Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms Abstract: Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta resulting from compromised wall composition, structure, and function, which can lead to life-threatening dissection or rupture. Seve... |
Title: Communication Compression for Decentralized Learning with Operator Splitting Methods Abstract: In decentralized learning, operator splitting methods using a primal-dual formulation (e.g., the Edge-Consensus Learning (ECL)) has been shown to be robust to heterogeneous data and has attracted significant attention ... |
Title: Mutual Distillation Learning Network for Trajectory-User Linking Abstract: Trajectory-User Linking (TUL), which links trajectories to users who generate them, has been a challenging problem due to the sparsity in check-in mobility data. Existing methods ignore the utilization of historical data or rich contextua... |
Title: Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication Abstract: 6G wireless networks are foreseen to speed up the convergence of the physical and cyber worlds and to enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, ... |
Title: Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search Abstract: Despite the broad range of algorithms for Approximate Nearest Neighbor Search, most empirical evaluations of algorithms have focused on smaller datasets, typically of 1 million points~\citep{Benchmark}. However, de... |
Title: Silence is Sweeter Than Speech: Self-Supervised Model Using Silence to Store Speaker Information Abstract: Self-Supervised Learning (SSL) has made great strides recently. SSL speech models achieve decent performance on a wide range of downstream tasks, suggesting that they extract different aspects of informatio... |
Title: Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks Abstract: The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory. Previous studies in Semi-Sup... |
Title: Learnability of Competitive Threshold Models Abstract: Modeling the spread of social contagions is central to various applications in social computing. In this paper, we study the learnability of the competitive threshold model from a theoretical perspective. We demonstrate how competitive threshold models can b... |
Title: GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion Abstract: Low-rank tensor factorization or completion is well-studied and applied in various online settings, such as online tensor factorization (where the temporal mode grows) and online tensor completion (where incomplete slices ... |
Title: Data-Driven Approximations of Chance Constrained Programs in Nonstationary Environments Abstract: We study sample average approximations (SAA) of chance constrained programs. SAA methods typically approximate the actual distribution in the chance constraint using an empirical distribution constructed from random... |
Title: Impact of Learning Rate on Noise Resistant Property of Deep Learning Models Abstract: The interest in analog computation has grown tremendously in recent years due to its fast computation speed and excellent energy efficiency, which is very important for edge and IoT devices in the sub-watt power envelope for de... |
Title: FRC-TOuNN: Topology Optimization of Continuous Fiber Reinforced Composites using Neural Network Abstract: In this paper, we present a topology optimization (TO) framework to simultaneously optimize the matrix topology and fiber distribution of functionally graded continuous fiber-reinforced composites (FRC). Cur... |
Title: Optimal Lighting Control in Greenhouses Using Bayesian Neural Networks for Sunlight Prediction Abstract: Controlling the environmental parameters, including light in greenhouses, increases the crop yield; however, the electricity cost of supplemental lighting can be high. Therefore, the importance of applying co... |
Title: Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models Abstract: Analog hardware has become a popular choice for machine learning on resource-constrained devices recently due to its fast execution and energy efficiency. However, the inherent presence of noise in analog hardwa... |
Title: Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm Abstract: We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We focus on obtaining tight, often matching, lower and up... |
Title: Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention Abstract: The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks. To address this issue, parameter-efficient transfer le... |
Title: Odor Descriptor Understanding through Prompting Abstract: Embeddings from contemporary natural language processing (NLP) models are commonly used as numerical representations for words or sentences. However, odor descriptor words, like "leather" or "fruity", vary significantly between their commonplace usage and... |
Title: Accuracy Convergent Field Predictors Abstract: Several predictive algorithms are described. Highlighted are variants that make predictions by superposing fields associated to the training data instances. They operate seamlessly with categorical, continuous, and mixed data. Predictive accuracy convergence is also... |
Title: Quantifying and Extrapolating Data Needs in Radio Frequency Machine Learning Abstract: Understanding the relationship between training data and a model's performance once deployed is a fundamental component in the application of machine learning. While the model's deployed performance is dependent on numerous va... |
Title: Rate-Optimal Contextual Online Matching Bandit Abstract: Two-sided online matching platforms have been employed in various markets. However, agents' preferences in present market are usually implicit and unknown and must be learned from data. With the growing availability of side information involved in the deci... |
Title: Towards Practical Physics-Informed ML Design and Evaluation for Power Grid Abstract: When applied to a real-world safety critical system like the power grid, general machine learning methods suffer from expensive training, non-physical solutions, and limited interpretability. To address these challenges for powe... |
Title: Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions Abstract: Nonlinear partial differential equations (PDEs) are used to model dynamical processes in a large number of scientific fields, ranging from finance to biology. In many applications standard local models are not su... |
Title: Automated Algorithm Selection for Radar Network Configuration Abstract: The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to ... |
Title: Variational Sparse Coding with Learned Thresholding Abstract: Sparse coding strategies have been lauded for their parsimonious representations of data that leverage low dimensional structure. However, inference of these codes typically relies on an optimization procedure with poor computational scaling in high-d... |
Title: Automatic Velocity Picking Using Unsupervised Ensemble Learning Abstract: In seismic data processing, accurate and efficient automatic velocity picking algorithms can significantly accelerate the processing, and the main branch is to use velocity spectra for velocity pickup. Recently, machine learning algorithms... |
Title: Label Adversarial Learning for Skeleton-level to Pixel-level Adjustable Vessel Segmentation Abstract: You can have your cake and eat it too. Microvessel segmentation in optical coherence tomography angiography (OCTA) images remains challenging. Skeleton-level segmentation shows clear topology but without diamete... |
Title: Search-Based Testing of Reinforcement Learning Abstract: Evaluation of deep reinforcement learning (RL) is inherently challenging. Especially the opaqueness of learned policies and the stochastic nature of both agents and environments make testing the behavior of deep RL agents difficult. We present a search-bas... |
Title: Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review Abstract: The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mo... |
Title: DL4DS -- Deep Learning for empirical DownScaling Abstract: A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution which can be prohibitive due to long model runti... |
Title: Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks Abstract: Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the ... |
Title: Determination of class-specific variables in nonparametric multiple-class classification Abstract: As technology advanced, collecting data via automatic collection devices become popular, thus we commonly face data sets with lengthy variables, especially when these data sets are collected without specific resear... |
Title: BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck Abstract: Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classi... |
Title: ConceptDistil: Model-Agnostic Distillation of Concept Explanations Abstract: Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for specific models (eg, neural networks) or data types (eg, images), and by ei... |
Title: Towards Computationally Feasible Deep Active Learning Abstract: Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many others. O... |
Title: Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review Abstract: The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy ... |
Title: Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data Abstract: Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resul... |
Title: Deep learning for spatio-temporal forecasting -- application to solar energy Abstract: This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main resea... |
Title: Number Entity Recognition Abstract: Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not accounted for distinctly in most NLP tasks, there is still an underlying amount of numeracy alread... |
Title: Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning Abstract: The amount of secure data being stored on mobile devices has grown immensely in recent years. However, the security measures protecting this data have stayed static, with few improvements being done to the vulne... |
Title: Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling Abstract: In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment. The goal is maximize the overall target coverage of a... |
Title: Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance Abstract: Domain adaptation on time-series data is often encountered in the industry but received limited attention in academia. Most of the existing domain adaptation methods for time-series data borrow th... |
Title: Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees Abstract: Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonst... |
Title: Graph Spectral Embedding using the Geodesic Betweeness Centrality Abstract: We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvester equation to capture both network structure and neighbo... |
Title: Anomaly Detection in Intra-Vehicle Networks Abstract: The progression of innovation and technology and ease of inter-connectivity among networks has allowed us to evolve towards one of the promising areas, the Internet of Vehicles. Nowadays, modern vehicles are connected to a range of networks, including intra-v... |
Title: Factory: Fast Contact for Robotic Assembly Abstract: Robotic assembly is one of the oldest and most challenging applications of robotics. In other areas of robotics, such as perception and grasping, simulation has rapidly accelerated research progress, particularly when combined with modern deep learning. Howeve... |
Title: Individualized Risk Assessment of Preoperative Opioid Use by Interpretable Neural Network Regression Abstract: Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures. Unde... |
Title: Unsupervised Deep Unrolled Reconstruction Using Regularization by Denoising Abstract: Deep learning methods have been successfully used in various computer vision tasks. Inspired by that success, deep learning has been explored in magnetic resonance imaging (MRI) reconstruction. In particular, integrating deep l... |
Title: Fine-grained Intent Classification in the Legal Domain Abstract: A law practitioner has to go through a lot of long legal case proceedings. To understand the motivation behind the actions of different parties/individuals in a legal case, it is essential that the parts of the document that express an intent corre... |
Title: Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks Abstract: The Li-ion battery is a complex physicochemical system that generally takes applied current as input and terminal voltage as output. The mappings from current to voltage can be described by several ... |
Title: JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization Abstract: Ultra-Wideband (UWB) is one of the key technologies empowering the Internet of Thing (IoT) concept to perform reliable, energy-efficient, and highly accurate monitoring, screening, and localization in indoor enviro... |
Title: PARAFAC2$\times$N: Coupled Decomposition of Multi-modal Data with Drift in N Modes Abstract: Reliable analysis of comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC$\times$GC-TOFMS) data is considered to be a major bottleneck for its widespread application. For multiple sampl... |
Title: Online Model Compression for Federated Learning with Large Models Abstract: This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost. The proposed Online Model Compression (OMC) provides a framework that stor... |
Title: Norm-Scaling for Out-of-Distribution Detection Abstract: Out-of-Distribution (OoD) inputs are examples that do not belong to the true underlying distribution of the dataset. Research has shown that deep neural nets make confident mispredictions on OoD inputs. Therefore, it is critical to identify OoD inputs for ... |
Title: DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI Abstract: Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as ... |
Title: Clustered Graph Matching for Label Recovery and Graph Classification Abstract: Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We consider matchi... |
Title: Diverse Imitation Learning via Self-Organizing Generative Models Abstract: Imitation learning is the task of replicating expert policy from demonstrations, without access to a reward function. This task becomes particularly challenging when the expert exhibits a mixture of behaviors. Prior work has introduced la... |
Title: Hitting time for Markov decision process Abstract: We define the hitting time for a Markov decision process (MDP). We do not use the hitting time of the Markov process induced by the MDP because the induced chain may not have a stationary distribution. Even it has a stationary distribution, the stationary distri... |
Title: Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds Abstract: Traditional memory writing operations proceed one bit at a time, where e.g. an individual magnetic domain is force-flipped by a localized external field. One way to increase material storage capacity woul... |
Title: Structure Learning in Graphical Models from Indirect Observations Abstract: This paper considers learning of the graphical structure of a $p$-dimensional random vector $X \in R^p$ using both parametric and non-parametric methods. Unlike the previous works which observe $x$ directly, we consider the indirect obse... |
Title: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching Abstract: Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learn... |
Title: Stochastic resonance neurons in artificial neural networks Abstract: Many modern applications of the artificial neural networks ensue large number of layers making traditional digital implementations increasingly complex. Optical neural networks offer parallel processing at high bandwidth, but have the challenge... |
Title: Global Multi-modal 2D/3D Registration via Local Descriptors Learning Abstract: Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all r... |
Title: Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration Abstract: Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks. Conventionally, SNNs can be obtained by converting from pre-traine... |
Title: Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition Abstract: Recognizing human non-speech vocalizations is an important task and has broad applications such as automatic sound transcription and health condition monitoring. However, existing datasets have a relatively small number of vocal sound s... |
Title: Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment Abstract: Automatic pronunciation assessment is an important technology to help self-directed language learners. While pronunciation quality has multiple aspects including accuracy, fluency, completeness, and pro... |
Title: Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning Abstract: To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly... |
Title: Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting Abstract: Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A common target of said applications is to predict the resu... |
Title: Physics-informed neural networks for PDE-constrained optimization and control Abstract: A fundamental problem of science is designing optimal control policies that manipulate a given environment into producing a desired outcome. Control Physics-Informed Neural Networks simultaneously solve a given system state, ... |
Title: Efficient Minimax Optimal Estimators For Multivariate Convex Regression Abstract: We study the computational aspects of the task of multivariate convex regression in dimension $d \geq 5$. We present the first computationally efficient minimax optimal (up to logarithmic factors) estimators for the tasks of (i) $L... |
Title: Trainable Wavelet Neural Network for Non-Stationary Signals Abstract: This work introduces a wavelet neural network to learn a filter-bank specialized to fit non-stationary signals and improve interpretability and performance for digital signal processing. The network uses a wavelet transform as the first layer ... |
Title: How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation Abstract: Reinforcement learning (RL) has been shown to be effective at learning control from experience. However, RL typically requires a large amount of online interaction with the envir... |
Title: Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning Abstract: Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has use cases in chemistry, biology, and medicine. In the past decade, the ... |
Title: UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks Abstract: The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments. More recently yet still in the context of wireless networks, drones hav... |
Title: Benchmarking Econometric and Machine Learning Methodologies in Nowcasting Abstract: Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag, such as final GDP figures. Currently, there are a plethora of methodologies and approaches for practitioners to... |
Title: Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models Abstract: Recent studies increasingly adopt simulation-based machine learning (ML) models to analyze critical infrastructure system resilience. For realistic applications, these ML models consider the... |
Title: The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Abstract: Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc explainability methods where a simple, h... |
Title: Vehicle management in a modular production context using Deep Q-Learning Abstract: We investigate the feasibility of deploying Deep-Q based deep reinforcement learning agents to job-shop scheduling problems in the context of modular production facilities, using discrete event simulations for the environment. The... |
Title: Optimal Control as Variational Inference Abstract: In this article we address the stochastic and risk sensitive optimal control problem probabilistically and decompose and solve the probabilistic models using principles from variational inference. We demonstrate how this culminates into two separate probabilisti... |
Title: Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design Abstract: Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empiri... |
Title: Convex Analysis at Infinity: An Introduction to Astral Space Abstract: Not all convex functions on $\mathbb{R}^n$ have finite minimizers; some can only be minimized by a sequence as it heads to infinity. In this work, we aim to develop a theory for understanding such minimizers at infinity. We study astral space... |
Title: Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches Abstract: This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions into machine learning (ML) that draw from a range of non-computing disciplines, including ... |
Title: Synthetic Data -- what, why and how? Abstract: This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy. The article is intended for a non-technical audience, though some formal definitions have been ... |
Title: What Makes A Good Fisherman? Linear Regression under Self-Selection Bias Abstract: In the classical setting of self-selection, the goal is to learn $k$ models, simultaneously from observations $(x^{(i)}, y^{(i)})$ where $y^{(i)}$ is the output of one of $k$ underlying models on input $x^{(i)}$. In contrast to mi... |
Title: HumanAL: Calibrating Human Matching Beyond a Single Task Abstract: This work offers a novel view on the use of human input as labels, acknowledging that humans may err. We build a behavioral profile for human annotators which is used as a feature representation of the provided input. We show that by utilizing bl... |
Title: Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms Abstract: While the field of Quality-Diversity (QD) has grown into a distinct branch of stochastic optimization, a few problems, in particular locomotion and navigation tasks, have become de facto standards. Are such benchmarks suf... |
Title: Perseus: A Simple High-Order Regularization Method for Variational Inequalities Abstract: This paper settles an open and challenging question pertaining to the design of simple high-order regularization methods for solving smooth and monotone variational inequalities (VIs). A VI involves finding $x^\star \in \ma... |
Title: Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals Abstract: Channel estimation is a critical task in intelligent reflecting surface (IRS)-assisted wireless systems due to the uncertainties imposed by environment dynamics and rapid changes in the IRS configuration. To deal wi... |
Title: Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation Abstract: Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applyin... |
Title: Scalable computation of prediction intervals for neural networks via matrix sketching Abstract: Accounting for the uncertainty in the predictions of modern neural networks is a challenging and important task in many domains. Existing algorithms for uncertainty estimation require modifying the model architecture ... |
Title: Imperceptible Backdoor Attack: From Input Space to Feature Representation Abstract: Backdoor attacks are rapidly emerging threats to deep neural networks (DNNs). In the backdoor attack scenario, attackers usually implant the backdoor into the target model by manipulating the training dataset or training process.... |
Title: On boundary conditions parametrized by analytic functions Abstract: Computer algebra can answer various questions about partial differential equations using symbolic algorithms. However, the inclusion of data into equations is rare in computer algebra. Therefore, recently, computer algebra models have been combi... |
Title: Green Accelerated Hoeffding Tree Abstract: State-of-the-art machine learning solutions mainly focus on creating highly accurate models without constraints on hardware resources. Stream mining algorithms are designed to run on resource-constrained devices, thus a focus on low power and energy and memory-efficient... |
Title: The NT-Xent loss upper bound Abstract: Self-supervised learning is a growing paradigm in deep representation learning, showing great generalization capabilities and competitive performance in low-labeled data regimes. The SimCLR framework proposes the NT-Xent loss for contrastive representation learning. The obj... |
Title: Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data Abstract: Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical con... |
Title: Geodesics, Non-linearities and the Archive of Novelty Search Abstract: The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short note focuses on the effects of the archive on exploration. Experi... |
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