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Title: Diffusion Models for Adversarial Purification Abstract: Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. These methods do not make assumptions on the form of attack and the classification model, and thus can defend pre-existing classifi... |
Title: Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths Abstract: Implicit deep learning has recently become popular in the machine learning community since these implicit models can achieve competitive performance with state-of-the-art deep networks while using significantly less mem... |
Title: $q$-Munchausen Reinforcement Learning Abstract: The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the current stochastic policy. Though significant improvement has been shown with the Boltzma... |
Title: Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization Abstract: Spiking neural network (SNN) operating with asynchronous discrete events shows higher energy efficiency. A popular approach to implement deep SNNs is ANN-SNN conversion combining both efficient training in ANNs and... |
Title: Ergodic variational flows Abstract: This work presents a new class of variational family -- ergodic variational flows -- that not only enables tractable i.i.d. sampling and density evaluation, but also comes with MCMC-like convergence guarantees. Ergodic variational flows consist of a mixture of repeated applica... |
Title: Manifold Characteristics That Predict Downstream Task Performance Abstract: Pretraining methods are typically compared by evaluating the accuracy of linear classifiers, transfer learning performance, or visually inspecting the representation manifold's (RM) lower-dimensional projections. We show that the differe... |
Title: Learning-Based sensitivity analysis and feedback design for drug delivery of mixed therapy of cancer in the presence of high model uncertainties Abstract: In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient spe... |
Title: Robust Testing in High-Dimensional Sparse Models Abstract: We consider the problem of robustly testing the norm of a high-dimensional sparse signal vector under two different observation models. In the first model, we are given $n$ i.i.d. samples from the distribution $\mathcal{N}\left(\theta,I_d\right)$ (with u... |
Title: Multi-scale Attention Flow for Probabilistic Time Series Forecasting Abstract: The probability prediction of multivariate time series is a notoriously challenging but practical task. On the one hand, the challenge is how to effectively capture the cross-series correlations between interacting time series, to ach... |
Title: KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning Abstract: In general, multiple domain cyberspace security assessments can be implemented by reasoning user's permissions. However, while existing methods include some information from the physical and soc... |
Title: The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data Abstract: Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in int... |
Title: A scalable deep learning approach for solving high-dimensional dynamic optimal transport Abstract: The dynamic formulation of optimal transport has attracted growing interests in scientific computing and machine learning, and its computation requires to solve a PDE-constrained optimization problem. The classical... |
Title: A model aggregation approach for high-dimensional large-scale optimization Abstract: Bayesian optimization (BO) has been widely used in machine learning and simulation optimization. With the increase in computational resources and storage capacities in these fields, high-dimensional and large-scale problems are ... |
Title: Wasserstein t-SNE Abstract: Scientific datasets often have hierarchical structure: for example, in surveys, individual participants (samples) might be grouped at a higher level (units) such as their geographical region. In these settings, the interest is often in exploring the structure on the unit level rather ... |
Title: Reachability Constrained Reinforcement Learning Abstract: Constrained reinforcement learning (CRL) has gained significant interest recently, since safety constraints satisfaction is critical for real-world problems. However, existing CRL methods constraining discounted cumulative costs generally lack rigorous de... |
Title: SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization Abstract: One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also known as codebook collapse. We... |
Title: Towards on-sky adaptive optics control using reinforcement learning Abstract: The direct imaging of potentially habitable Exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based extremely large telescopes. To reach this demanding science goal, the instrum... |
Title: Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies Abstract: Autonomous open-ended learning is a relevant approach in machine learning and robotics, allowing the design of artificial agents able to acquire goals and motor skills without the necessity of user assigned tasks. A crucial i... |
Title: Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine Abstract: CT and MRI are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information... |
Title: Chemical transformer compression for accelerating both training and inference of molecular modeling Abstract: Transformer models have been developed in molecular science with excellent performance in applications including quantitative structure-activity relationship (QSAR) and virtual screening (VS). Compared w... |
Title: Fundamental Laws of Binary Classification Abstract: Finding discriminant functions of minimum risk binary classification systems is a novel geometric locus problem -- that requires solving a system of fundamental locus equations of binary classification -- subject to deep-seated statistical laws. We show that a ... |
Title: Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents Abstract: In this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: ... |
Title: Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention Abstract: Many measurements in the physical sciences can be cast as counting experiments, where the number of occurrences of a physical phenomenon informs the prevalence of the phenomenon's sou... |
Title: Rethinking Reinforcement Learning based Logic Synthesis Abstract: Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process. However, through extensive experiments, we find out that the learned policy makes dec... |
Title: Model Agnostic Local Explanations of Reject Abstract: The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. While being a... |
Title: Attacking and Defending Deep Reinforcement Learning Policies Abstract: Recent studies have shown that deep reinforcement learning (DRL) policies are vulnerable to adversarial attacks, which raise concerns about applications of DRL to safety-critical systems. In this work, we adopt a principled way and study the ... |
Title: Taming Continuous Posteriors for Latent Variational Dialogue Policies Abstract: Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success. Until now, categorical poster... |
Title: Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder Abstract: Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data. Existing domain generalization techniques embark upon stationary and discrete environmen... |
Title: Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection Abstract: The healthcare landscape is moving from the reactive interventions focused on symptoms treatment to a more proactive prevention, from one-size-fits-all to personalized medicine, and from centralized... |
Title: Conditional Born machine for Monte Carlo events generation Abstract: Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as random source. So called Born machines are purely quantum models and promise to generate probability distribut... |
Title: L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data Abstract: Smartphones and wearable devices, along with Artificial Intelligence, can represent a game-changer in the pandemic control, by implementing low-cost and pervasive solutions to recognize the development of new diseases at th... |
Title: Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml Abstract: In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolu... |
Title: From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses Abstract: We propose the Bayes-UCBVI algorithm for reinforcement learning in tabular, stage-dependent, episodic Markov decision process: a natural extension of the Bayes-UCB algorithm by Kaufmann et al. (2012) for multi-armed bandits. Our meth... |
Title: Generalizing to New Tasks via One-Shot Compositional Subgoals Abstract: The ability to generalize to previously unseen tasks with little to no supervision is a key challenge in modern machine learning research. It is also a cornerstone of a future "General AI". Any artificially intelligent agent deployed in a re... |
Title: Towards Space-to-Ground Data Availability for Agriculture Monitoring Abstract: The recent advances in machine learning and the availability of free and open big Earth data (e.g., Sentinel missions), which cover large areas with high spatial and temporal resolution, have enabled many agriculture monitoring applic... |
Title: Pest presence prediction using interpretable machine learning Abstract: Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the insects in the field is crucial for effective farm interventions... |
Title: Prioritizing Corners in OoD Detectors via Symbolic String Manipulation Abstract: For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training dataset. This paper studies the problem of systematic... |
Title: Sharp Asymptotics of Self-training with Linear Classifier Abstract: Self-training (ST) is a straightforward and standard approach in semi-supervised learning, successfully applied to many machine learning problems. The performance of ST strongly depends on the supervised learning method used in the refinement st... |
Title: On the inability of Gaussian process regression to optimally learn compositional functions Abstract: We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure. To this end, we study information-theoretic lower bounds for post... |
Title: Efficient Algorithms for Planning with Participation Constraints Abstract: We consider the problem of planning with participation constraints introduced in [Zhang et al., 2022]. In this problem, a principal chooses actions in a Markov decision process, resulting in separate utilities for the principal and the ag... |
Title: JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging Abstract: Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery. These methods use a deep neural network (DNN) as an image generator to learn non-linear mapping from compressed measu... |
Title: Gradient-based Counterfactual Explanations using Tractable Probabilistic Models Abstract: Counterfactual examples are an appealing class of post-hoc explanations for machine learning models. Given input $x$ of class $y_1$, its counterfactual is a contrastive example $x^\prime$ of another class $y_0$. Current app... |
Title: The Primacy Bias in Deep Reinforcement Learning Abstract: This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of... |
Title: CurFi: An automated tool to find the best regression analysis model using curve fitting Abstract: Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually o... |
Title: GraphHD: Efficient graph classification using hyperdimensional computing Abstract: Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a ... |
Title: Federated Anomaly Detection over Distributed Data Streams Abstract: Sharing of telecommunication network data, for example, even at high aggregation levels, is nowadays highly restricted due to privacy legislation and regulations and other important ethical concerns. It leads to scattering data across institutio... |
Title: FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization Abstract: We present FactPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning: (1) We augment the sentence selection strategy of PEGASUS's (Zhang et al., 20... |
Title: Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning Abstract: We use the "map of elections" approach of Szufa et al. (AAMAS 2020) to analyze several well-known vote distributions. For each of them, we give an explicit formula or an efficient algorithm for computing its freque... |
Title: Decision Making for Hierarchical Multi-label Classification with Multidimensional Local Precision Rate Abstract: Hierarchical multi-label classification (HMC) has drawn increasing attention in the past few decades. It is applicable when hierarchical relationships among classes are available and need to be incorp... |
Title: Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment Abstract: Edge plasma turbulence is critical to the performance of magnetic confinement fusion devices. Towards better understanding edge turbulence in both theory and experiment, a custom-bui... |
Title: Loss Landscape Engineering via Data Regulation on PINNs Abstract: Physics-Informed Neural Networks have shown unique utility in parameterising the solution of a well-defined partial differential equation using automatic differentiation and residual losses. Though they provide theoretical guarantees of convergenc... |
Title: Power and limitations of single-qubit native quantum neural networks Abstract: Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have been widely investigated, its theoretical foundation rema... |
Title: ST-ExpertNet: A Deep Expert Framework for Traffic Prediction Abstract: Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances. As we all know, the flow at a citywide level is in a mixed state with several basic patterns (e.g., comm... |
Title: REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics Abstract: Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces. On the other hand, surrogate deep learni... |
Title: Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity Abstract: Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation... |
Title: Functional2Structural: Cross-Modality Brain Networks Representation Learning Abstract: MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, such as brain development and disease. Graph min... |
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: 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: Neural Program Synthesis with Query Abstract: Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privilege... |
Title: Predicting tacrolimus exposure in kidney transplanted patients using machine learning Abstract: Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid re... |
Title: Btech thesis report on adversarial attack detection and purification of adverserially attacked images Abstract: This is Btech thesis report on detection and purification of adverserially attacked images. A deep learning model is trained on certain training examples for various tasks such as classification, regre... |
Title: AdaCap: Adaptive Capacity control for Feed-Forward Neural Networks Abstract: The capacity of a ML model refers to the range of functions this model can approximate. It impacts both the complexity of the patterns a model can learn but also memorization, the ability of a model to fit arbitrary labels. We propose A... |
Title: Depression Diagnosis and Forecast based on Mobile Phone Sensor Data Abstract: Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires, the passive data collected from mobile phones is easi... |
Title: A Safety Assurable Human-Inspired Perception Architecture Abstract: Although artificial intelligence-based perception (AIP) using deep neural networks (DNN) has achieved near human level performance, its well-known limitations are obstacles to the safety assurance needed in autonomous applications. These include... |
Title: Quality versus speed in energy demand prediction for district heating systems Abstract: In this paper, we consider energy demand prediction in district heating systems. Effective energy demand prediction is essential in combined heat power systems when offering electrical energy in competitive electricity market... |
Title: Privacy Enhancement for Cloud-Based Few-Shot Learning Abstract: Requiring less data for accurate models, few-shot learning has shown robustness and generality in many application domains. However, deploying few-shot models in untrusted environments may inflict privacy concerns, e.g., attacks or adversaries that ... |
Title: Simple Contrastive Graph Clustering Abstract: Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming graph convolutional operation undermine the efficiency of these methods. To solve this p... |
Title: Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction Abstract: In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSN... |
Title: Feature and Instance Joint Selection: A Reinforcement Learning Perspective Abstract: Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards the joint selection conducts feature/instance se... |
Title: Minimal Neural Network Models for Permutation Invariant Agents Abstract: Organisms in nature have evolved to exhibit flexibility in face of changes to the environment and/or to themselves. Artificial neural networks (ANNs) have proven useful for controlling of artificial agents acting in environments. However, m... |
Title: Near out-of-distribution detection for low-resolution radar micro-Doppler signatures Abstract: Near out-of-distribution detection (OOD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OOD use case for radar targets detection ... |
Title: Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training Abstract: Driving behavior monitoring plays a crucial role in managing road safety and decreasing the risk of traffic accidents. Driving behavior is affected by multiple factors like vehicle characteristics, ... |
Title: Mondrian Forest for Data Stream Classification Under Memory Constraints Abstract: Supervised learning algorithms generally assume the availability of enough memory to store their data model during the training and test phases. However, in the Internet of Things, this assumption is unrealistic when data comes in ... |
Title: ScAN: Suicide Attempt and Ideation Events Dataset Abstract: Suicide is an important public health concern and one of the leading causes of death worldwide. Suicidal behaviors, including suicide attempts (SA) and suicide ideations (SI), are leading risk factors for death by suicide. Information related to patient... |
Title: Revisiting the Updates of a Pre-trained Model for Few-shot Learning Abstract: Most of the recent few-shot learning algorithms are based on transfer learning, where a model is pre-trained using a large amount of source data, and the pre-trained model is updated using a small amount of target data afterward. In tr... |
Title: A Comprehensive Survey on Model Quantization for Deep Neural Networks Abstract: Recent advances in machine learning by deep neural networks are significant. But using these networks has been accompanied by a huge number of parameters for storage and computations that leads to an increase in the hardware cost and... |
Title: A Note on the Chernoff Bound for Random Variables in the Unit Interval Abstract: The Chernoff bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average. This bound is commonly used in statistical learning theory to upper bou... |
Title: Developing patient-driven artificial intelligence based on personal rankings of care decision making steps Abstract: We propose and experimentally motivate a new methodology to support decision-making processes in healthcare with artificial intelligence based on personal rankings of care decision making steps th... |
Title: Learning Car Speed Using Inertial Sensors Abstract: A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ash... |
Title: Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning Abstract: Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entropy and sparse entropy. ... |
Title: An Empirical Investigation of Representation Learning for Imitation Abstract: Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment. However, collecting expert demonstrations can be expensive. Recent work in v... |
Title: Data-Driven Interpolation for Super-Scarce X-Ray Computed Tomography Abstract: We address the problem of reconstructing X-Ray tomographic images from scarce measurements by interpolating missing acquisitions using a self-supervised approach. To do so, we train shallow neural networks to combine two neighbouring ... |
Title: On the Difficulty of Defending Self-Supervised Learning against Model Extraction Abstract: Self-Supervised Learning (SSL) is an increasingly popular ML paradigm that trains models to transform complex inputs into representations without relying on explicit labels. These representations encode similarity structur... |
Title: Fast and realistic large-scale structure from machine-learning-augmented random field simulations Abstract: Producing thousands of simulations of the dark matter distribution in the Universe with increasing precision is a challenging but critical task to facilitate the exploitation of current and forthcoming cos... |
Title: Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows Abstract: While fat-tailed densities commonly arise as posterior and marginal distributions in robust models and scale mixtures, they present challenges when Gaussian-based variational inference fails to capture tail decay accurately. We first... |
Title: An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing Abstract: Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces. It is particularly useful for machine learning in resource-constrained environments, such a... |
Title: Distributed Feature Selection for High-dimensional Additive Models Abstract: Distributed statistical learning is a common strategy for handling massive data where we divide the learning task into multiple local machines and aggregate the results afterward. However, most existing work considers the case where the... |
Title: Constructing Trajectory and Predicting Estimated Time of Arrival for Long Distance Travelling Vessels: A Probability Density-based Scanning Approach Abstract: In this study, a probability density-based approach for constructing trajectories is proposed and validated through an typical use-case application: Estim... |
Title: Application of multilayer perceptron with data augmentation in nuclear physics Abstract: Neural networks have become popular in many fields of science since they serve as reliable and powerful tools. Application of the neural networks to the nuclear physics studies has also become popular in recent years because... |
Title: Deep Apprenticeship Learning for Playing Games Abstract: In the last decade, deep learning has achieved great success in machine learning tasks where the input data is represented with different levels of abstractions. Driven by the recent research in reinforcement learning using deep neural networks, we explore... |
Title: An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models Abstract: Using gradient descent (GD) with fixed or decaying step-size is standard practice in unconstrained optimization problems. However, when the loss function is only locally convex, such a step-size schedule artificially s... |
Title: CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction Abstract: Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine. Recently, promising results have been achieved by leveragi... |
Title: Continual learning on 3D point clouds with random compressed rehearsal Abstract: Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype for precise modeling of three-dimensional environmen... |
Title: $\mathscr{H}$-Consistency Estimation Error of Surrogate Loss Minimizers Abstract: We present a detailed study of estimation errors in terms of surrogate loss estimation errors. We refer to such guarantees as $\mathscr{H}$-consistency estimation error bounds, since they account for the hypothesis set $\mathscr{H}... |
Title: Partial Product Aware Machine Learning on DNA-Encoded Libraries Abstract: DNA encoded libraries (DELs) are used for rapid large-scale screening of small molecules against a protein target. These combinatorial libraries are built through several cycles of chemistry and DNA ligation, producing large sets of DNA-ta... |
Title: Automatic Error Classification and Root Cause Determination while Replaying Recorded Workload Data at SAP HANA Abstract: Capturing customer workloads of database systems to replay these workloads during internal testing can be beneficial for software quality assurance. However, we experienced that such replays c... |
Title: On Algebraic Constructions of Neural Networks with Small Weights Abstract: Neural gates compute functions based on weighted sums of the input variables. The expressive power of neural gates (number of distinct functions it can compute) depends on the weight sizes and, in general, large weights (exponential in th... |
Title: Using Embeddings for Causal Estimation of Peer Influence in Social Networks Abstract: We address the problem of using observational data to estimate peer contagion effects, the influence of treatments applied to individuals in a network on the outcomes of their neighbors. A main challenge to such estimation is t... |
Title: DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo Abstract: We propose DeepSim, a reinforcement learning environment build toolkit for ROS and Gazebo. It allows machine learning or reinforcement learning researchers to access the robotics domain and create complex and challenging cus... |
Title: Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes Abstract: In recent years cybersecurity has become a major concern in adaptation of smart applications. Specially, in smart homes where a large number of IoT devices are used having a secure and trusted mechanisms... |
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