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Title: Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications Abstract: Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing and speech recognition. However, their superior performance com... |
Title: Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation Abstract: Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples. It remains an open question how such imperfect models can be used effectivel... |
Title: Can Rationalization Improve Robustness? Abstract: A growing line of work has investigated the development of neural NLP models that can produce rationales--subsets of input that can explain their model predictions. In this paper, we ask whether such rationale models can also provide robustness to adversarial att... |
Title: Online Deep Learning from Doubly-Streaming Data Abstract: This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. The challenges of this problem are tw... |
Title: Performer: A Novel PPG to ECG Reconstruction Transformer For a Digital Biomarker of Cardiovascular Disease Detection Abstract: Cardiovascular diseases (CVDs) have become the top one cause of death; three-quarters of these deaths occur in lower-income communities. Electrocardiography (ECG), an electrical measurem... |
Title: PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution Abstract: 3D neural networks are widely used in real-world applications (e.g., AR/VR headsets, self-driving cars). They are required to be fast and accurate; however, limited hardware resources on edge devices make these requirements rather challe... |
Title: Exoplanet Cartography using Convolutional Neural Networks Abstract: In the near-future, dedicated telescopes observe Earth-like exoplanets in reflected light, allowing their characterization. Because of the huge distances, every exoplanet will be a single pixel, but temporal variations in its spectral flux hold ... |
Title: Zero-Shot Logit Adjustment Abstract: Semantic-descriptor-based Generalized Zero-Shot Learning (GZSL) poses challenges in recognizing novel classes in the test phase. The development of generative models enables current GZSL techniques to probe further into the semantic-visual link, culminating in a two-stage for... |
Title: StyleGAN-Human: A Data-Centric Odyssey of Human Generation Abstract: Unconditional human image generation is an important task in vision and graphics, which enables various applications in the creative industry. Existing studies in this field mainly focus on "network engineering" such as designing new components... |
Title: Task-Induced Representation Learning Abstract: In this work, we evaluate the effectiveness of representation learning approaches for decision making in visually complex environments. Representation learning is essential for effective reinforcement learning (RL) from high-dimensional inputs. Unsupervised represen... |
Title: Skill-based Meta-Reinforcement Learning Abstract: While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real robot systems infeasible. To mitigate this issue, meta-reinforcement learning meth... |
Title: Machine learning identification of organic compounds using visible light Abstract: Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic an... |
Title: Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics Abstract: We study the problem of reinforcement learning for a task encoded by a reward machine. The task is defined over a set of properties in the environment, called atomic propositions, and represented by Boolean va... |
Title: Accelerating Machine Learning via the Weber-Fechner Law Abstract: The Weber-Fechner Law observes that human perception scales as the logarithm of the stimulus. We argue that learning algorithms for human concepts could benefit from the Weber-Fechner Law. Specifically, we impose Weber-Fechner on simple neural net... |
Title: A Novel Scalable Apache Spark Based Feature Extraction Approaches for Huge Protein Sequence and their Clustering Performance Analysis Abstract: Genome sequencing projects are rapidly increasing the number of high-dimensional protein sequence datasets. Clustering a high-dimensional protein sequence dataset using ... |
Title: Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way Abstract: Cookie banners, the pop ups that appear to collect your consent for data collection, are a tempting ground for dark patterns. Dark patterns are design elements that are used to influenc... |
Title: A Mask-Based Adversarial Defense Scheme Abstract: Adversarial attacks hamper the functionality and accuracy of Deep Neural Networks (DNNs) by meddling with subtle perturbations to their inputs.In this work, we propose a new Mask-based Adversarial Defense scheme (MAD) for DNNs to mitigate the negative effect from... |
Title: Automating Neural Architecture Design without Search Abstract: Neural structure search (NAS), as the mainstream approach to automate deep neural architecture design, has achieved much success in recent years. However, the performance estimation component adhering to NAS is often prohibitively costly, which leads... |
Title: AU-NN: ANFIS Unit Neural Network Abstract: In this paper is described the ANFIS Unit Neural Network, a deep neural network where each neuron is an independent ANFIS. Two use cases of this network are shown to test the capability of the network. (i) Classification of five imagined words. (ii) Incremental learning... |
Title: Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface Abstract: Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for mobility restoration. One major limitatio... |
Title: A Closer Look at Personalization in Federated Image Classification Abstract: Federated Learning (FL) is developed to learn a single global model across the decentralized data, while is susceptible when realizing client-specific personalization in the presence of statistical heterogeneity. However, studies focus ... |
Title: Adaptive Online Value Function Approximation with Wavelets Abstract: Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces. Linear function approximation has desirable theoretical guarantees and often requires less compute and samples than neural... |
Title: A Computational Theory of Learning Flexible Reward-Seeking Behavior with Place Cells Abstract: An important open question in computational neuroscience is how various spatially tuned neurons, such as place cells, are used to support the learning of reward-seeking behavior of an animal. Existing computational mod... |
Title: Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings Abstract: The fault diagnosis of rolling bearings is a critical technique to realize predictive maintenance for mechanical condition monitoring. In real industrial systems, the main challenges for the fault diagnosis of rolling bearings pertain to... |
Title: Graphical Residual Flows Abstract: Graphical flows add further structure to normalizing flows by encoding non-trivial variable dependencies. Previous graphical flow models have focused primarily on a single flow direction: the normalizing direction for density estimation, or the generative direction for inferenc... |
Title: SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks Abstract: Initial work on variational autoencoders assumed independent latent variables with simple distributions. Subsequent work has explored incorporating more complex distributions and dependency structures: including normalizing flows... |
Title: On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning Abstract: Humans are able to identify and categorize novel compositions of known concepts. The task in Compositional Zero-Shot learning (CZSL) is to learn composition of primitive concepts, i.e. objects and states, in such... |
Title: Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users Abstract: Risk assessment is a substantial problem for financial institutions that has been extensively studied both for its methodological richness and its various practical applications. With the expansion of i... |
Title: Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging Abstract: Photoacoustic imaging (PAI) can image high-resolution structures of clinical interest such as vascularity in cancerous tumor monitoring. When imaging human subjects, geometric restrictions force limited-view data retrieval causing... |
Title: Graph Auto-Encoders for Network Completion Abstract: Completing a graph means inferring the missing nodes and edges from a partially observed network. Different methods have been proposed to solve this problem, but none of them employed the pattern similarity of parts of the graph. In this paper, we propose a mo... |
Title: Real or Virtual: A Video Conferencing Background Manipulation-Detection System Abstract: Recently, the popularity and wide use of the last-generation video conferencing technologies created an exponential growth in its market size. Such technology allows participants in different geographic regions to have a vir... |
Title: Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification Abstract: Vessel navigation is influenced by various factors, such as dynamic environmental factors that change over time or static features such as vessel type or depth of the ocean. These dynamic and static navigation... |
Title: GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU Abstract: The work for predicting drug and target affinity(DTA) is crucial for drug development and repurposing. In this work, we propose a novel method called GDGRU-DTA to predict the binding affinity between drugs and targets, which... |
Title: Data Uncertainty without Prediction Models Abstract: Data acquisition processes for machine learning are often costly. To construct a high-performance prediction model with fewer data, a degree of difficulty in prediction is often deployed as the acquisition function in adding a new data point. The degree of dif... |
Title: Mapping Research Trajectories Abstract: Steadily growing amounts of information, such as annually published scientific papers, have become so large that they elude an extensive manual analysis. Hence, to maintain an overview, automated methods for the mapping and visualization of knowledge domains are necessary ... |
Title: Multi-objective Pointer Network for Combinatorial Optimization Abstract: Multi-objective combinatorial optimization problems (MOCOPs), one type of complex optimization problems, widely exist in various real applications. Although meta-heuristics have been successfully applied to address MOCOPs, the calculation t... |
Title: Evolutionary latent space search for driving human portrait generation Abstract: This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a ... |
Title: ProCST: Boosting Semantic Segmentation using Progressive Cyclic Style-Transfer Abstract: Using synthetic data for training neural networks that achieve good performance on real-world data is an important task as it has the potential to reduce the need for costly data annotation. Yet, a network that is trained on... |
Title: Reinforcement Teaching Abstract: Meta-learning strives to learn about and improve a student's machine learning algorithm. However, existing meta-learning methods either only work with differentiable algorithms or are hand-crafted to improve one specific component of an algorithm. We develop a unifying meta-learn... |
Title: Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection Abstract: We introduce a new setting, optimize-and-estimate structured bandits. Here, a policy must select a batch of arms, each characterized by its own context, that would allow it... |
Title: Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence Abstract: One of the core problems in mean-field control and mean-field games is to solve the corresponding McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs).... |
Title: On-demand compute reduction with stochastic wav2vec 2.0 Abstract: Squeeze and Efficient Wav2vec (SEW) is a recently proposed architecture that squeezes the input to the transformer encoder for compute efficient pre-training and inference with wav2vec 2.0 (W2V2) models. In this work, we propose stochastic compres... |
Title: Discrete-Continuous Smoothing and Mapping Abstract: We describe a general approach to smoothing and mapping with a class of discrete-continuous factor graphs commonly encountered in robotics applications. While there are openly available tools providing flexible and easy-to-use interfaces for specifying and solv... |
Title: Robust Dual-Graph Regularized Moving Object Detection Abstract: Moving object detection and its associated background-foreground separation have been widely used in a lot of applications, including computer vision, transportation and surveillance. Due to the presence of the static background, a video can be natu... |
Title: Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials Abstract: Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic molecules. These ... |
Title: Estimating and Penalizing Induced Preference Shifts in Recommender Systems Abstract: The content that a recommender system (RS) shows to users influences them. Therefore, when choosing which recommender to deploy, one is implicitly also choosing to induce specific internal states in users. Even more, systems tra... |
Title: Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System Abstract: In ophthalmology, intravitreal operative medication therapy (IVOM) is widespread treatment for diseases such as the age-related macular degeneration (AMD), the diabetic macular edema (DME), as well as th... |
Title: End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning Abstract: To enable heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that is capable of mining the... |
Title: When adversarial examples are excusable Abstract: Neural networks work remarkably well in practice and theoretically they can be universal approximators. However, they still make mistakes and a specific type of them called adversarial errors seem inexcusable to humans. In this work, we analyze both test errors a... |
Title: Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit Abstract: Vehicle routing problems (VRPs) can be divided into two major categories: offline VRPs, which consider a given set of trip requests to be served, and online VRPs, which consider requests a... |
Title: gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification Abstract: A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical s... |
Title: Theoretical Understanding of the Information Flow on Continual Learning Performance Abstract: Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data sequentially. CL performance evaluates the model's ability to continually learn and solve new problems with incremental... |
Title: Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs Abstract: DNN models across many domains continue to grow in size, resulting in high resource requirements for effective training, and unpalatable (and often unaffordable) costs for organizations and research labs across scales.... |
Title: BATS: Best Action Trajectory Stitching Abstract: The problem of offline reinforcement learning focuses on learning a good policy from a log of environment interactions. Past efforts for developing algorithms in this area have revolved around introducing constraints to online reinforcement learning algorithms to ... |
Title: Boundary Smoothing for Named Entity Recognition Abstract: Neural named entity recognition (NER) models may easily encounter the over-confidence issue, which degrades the performance and calibration. Inspired by label smoothing and driven by the ambiguity of boundary annotation in NER engineering, we propose boun... |
Title: Information Fusion: Scaling Subspace-Driven Approaches Abstract: In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Network (CNN) formalism. Upon unfolding the set of subspaces constituting ... |
Title: Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction Abstract: Temporal knowledge prediction is a crucial task for the event early warning that has gained increasing attention in recent years, which aims to predict the future facts by using relevant historical facts on the temporal knowledge ... |
Title: Causal Reasoning Meets Visual Representation Learning: A Prospective Study Abstract: Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing. Due to the emergence of... |
Title: ISTRBoost: Importance Sampling Transfer Regression using Boosting Abstract: Current Instance Transfer Learning (ITL) methodologies use domain adaptation and sub-space transformation to achieve successful transfer learning. However, these methodologies, in their processes, sometimes overfit on the target dataset ... |
Title: Thompson Sampling for Bandit Learning in Matching Markets Abstract: The problem of two-sided matching markets has a wide range of real-world applications and has been extensively studied in the literature. A line of recent works have focused on the problem setting where the preferences of one-side market partici... |
Title: Know Thy Student: Interactive Learning with Gaussian Processes Abstract: Learning often involves interaction between multiple agents. Human teacher-student settings best illustrate how interactions result in efficient knowledge passing where the teacher constructs a curriculum based on their students' abilities.... |
Title: One-pass additive-error subset selection for $\ell_{p}$ subspace approximation Abstract: We consider the problem of subset selection for $\ell_{p}$ subspace approximation, that is, to efficiently find a \emph{small} subset of data points such that solving the problem optimally for this subset gives a good approx... |
Title: AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images Abstract: Various deep learning methods have been proposed to segment breast lesion from ultrasound images. However, similar intensity distributions, variable tumor morphology and blurred boundaries present challenges for b... |
Title: Time Series Prediction by Multi-task GPR with Spatiotemporal Information Transformation Abstract: Making an accurate prediction of an unknown system only from a short-term time series is difficult due to the lack of sufficient information, especially in a multi-step-ahead manner. However, a high-dimensional shor... |
Title: A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity Abstract: In this work, we present a deep neural network architecture that can efficiently approximate classical elasto-plastic constitutive relations. The network is enriched with crucial physics aspects of classical el... |
Title: PyGOD: A Python Library for Graph Outlier Detection Abstract: PyGOD is an open-source Python library for detecting outliers on graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for node-, edge-, subgraph-, and graph-level outlier detection, und... |
Title: Convergence of neural networks to Gaussian mixture distribution Abstract: We give a proof that, under relatively mild conditions, fully-connected feed-forward deep random neural networks converge to a Gaussian mixture distribution as only the width of the last hidden layer goes to infinity. We conducted experime... |
Title: Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure Abstract: Clustering-based speaker diarization has stood firm as one of the major approaches in reality, despite recent development in end-to-end diarization. However, clustering methods have not been explored extensi... |
Title: Bias-Variance Decompositions for Margin Losses Abstract: We introduce a novel bias-variance decomposition for a range of strictly convex margin losses, including the logistic loss (minimized by the classic LogitBoost algorithm), as well as the squared margin loss and canonical boosting loss. Furthermore, we show... |
Title: Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia Abstract: Lower-and-middle income countries are faced with challenges arising from a lack of data on cause of death (COD), which can limit decisions on population health and disease manageme... |
Title: Cross Pairwise Ranking for Unbiased Item Recommendation Abstract: Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The loss functions, such as the mostly used pointwise Binary Cross-Entrop... |
Title: Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams Abstract: Devising intelligent agents able to live in an environment and learn by observing the surroundings is a longstanding goal of Artificial Intelligence. From a bare Machine Learning perspective, challenges arise when t... |
Title: IRC-safe Graph Autoencoder for an unsupervised anomaly detection Abstract: Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consiste... |
Title: Hybridised Loss Functions for Improved Neural Network Generalisation Abstract: Loss functions play an important role in the training of artificial neural networks (ANNs), and can affect the generalisation ability of the ANN model, among other properties. Specifically, it has been shown that the cross entropy and... |
Title: Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking Abstract: With the explosive growth of scientific publications, making the synthesis of scientific knowledge and fact checking becomes an increasingly complex task. In this paper, we propose a multi-task approach for verifying th... |
Title: Sentiment Analysis of Cybersecurity Content on Twitter and Reddit Abstract: Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in... |
Title: Graph Neural Networks for Microbial Genome Recovery Abstract: Microbes have a profound impact on our health and environment, but our understanding of the diversity and function of microbial communities is severely limited. Through DNA sequencing of microbial communities (metagenomics), DNA fragments (reads) of t... |
Title: Low-dimensional representation of infant and adult vocalization acoustics Abstract: During the first years of life, infant vocalizations change considerably, as infants develop the vocalization skills that enable them to produce speech sounds. Characterizations based on specific acoustic features, protophone cat... |
Title: Data-Efficient Backdoor Attacks Abstract: Recent studies have proven that deep neural networks are vulnerable to backdoor attacks. Specifically, by mixing a small number of poisoned samples into the training set, the behavior of the trained model can be maliciously controlled. Existing attack methods construct s... |
Title: Federated Stochastic Primal-dual Learning with Differential Privacy Abstract: Federated learning (FL) is a new paradigm that enables many clients to jointly train a machine learning (ML) model under the orchestration of a parameter server while keeping the local data not being exposed to any third party. However... |
Title: Empowering Next POI Recommendation with Multi-Relational Modeling Abstract: With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of t... |
Title: On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations Abstract: Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model's design space and informed decision-making in many physical domains. The usage of surroga... |
Title: Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims Abstract: False information has a significant negative influence on individuals as well as on the whole society. Especially in the current COVID-19 era, we witness an unprecedented growth of medical misinformation. To help tackle this... |
Title: Designing Perceptual Puzzles by Differentiating Probabilistic Programs Abstract: We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we d... |
Title: From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report) Abstract: Industry 4.0 offers opportunities to combine multiple sensor data sources using IoT technologies for better utilization of raw material in production lines. A common belief that dat... |
Title: Supervised Attention in Sequence-to-Sequence Models for Speech Recognition Abstract: Attention mechanism in sequence-to-sequence models is designed to model the alignments between acoustic features and output tokens in speech recognition. However, attention weights produced by models trained end to end do not al... |
Title: Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms Abstract: Text summarization is a technique for condensing a big piece of text into a few key elements that give a general impression of the content. When someone requires a quick and precise s... |
Title: Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition Abstract: When a financial institution declines an application for credit, an adverse action (AA) is said to occur. The applicant is then entitled to an explanation for the negative decision. This paper focuses on credit decisions based o... |
Title: Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning Abstract: How have individuals of social animals in nature evolved to learn from each other, and what would be the optimal strategy for such learning in a specific environment? Here, we address both problems by... |
Title: Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks Abstract: Several areas have been improved with Deep Learning during the past years. Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achievements over the past years; however, for us... |
Title: Multi-task Learning for Concurrent Prediction of Thermal Comfort, Sensation, and Preference Abstract: Indoor thermal comfort immensely impacts the health and performance of occupants. Therefore, researchers and engineers have proposed numerous computational models to estimate thermal comfort (TC). Given the impe... |
Title: Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings Abstract: Given multiple source word embeddings learnt using diverse algorithms and lexical resources, meta word embedding learning methods attempt to learn more accurate and wide-coverage word embeddings. Prior work on met... |
Title: Quantum-classical convolutional neural networks in radiological image classification Abstract: Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear. However, there are indication... |
Title: On Fragile Features and Batch Normalization in Adversarial Training Abstract: Modern deep learning architecture utilize batch normalization (BN) to stabilize training and improve accuracy. It has been shown that the BN layers alone are surprisingly expressive. In the context of robustness against adversarial exa... |
Title: Streaming Algorithms for High-Dimensional Robust Statistics Abstract: We study high-dimensional robust statistics tasks in the streaming model. A recent line of work obtained computationally efficient algorithms for a range of high-dimensional robust estimation tasks. Unfortunately, all previous algorithms requi... |
Title: Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning Abstract: We propose a population-level analysis to address issues of data sparsity when building predictive models of engineering infrastructure. By sharing information between similar assets, hierarchical Bayesian... |
Title: A survey on attention mechanisms for medical applications: are we moving towards better algorithms? Abstract: The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, th... |
Title: Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network and Machine Learning Abstract: Accelerated ripening through the exposure of fruits to controlled environmental conditions and gases is nowadays one of the most assessed food technologies, especially for climacteric and exotic products. Howev... |
Title: Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators Abstract: Reconfigurable accelerators for deep neural networks (DNNs) promise to improve performance such as inference latency. STONNE is the first cycle-accurate simulator for reconfigurable DNN inference accelerators which allow... |
Title: Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level Abstract: Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. Various data-driven methods have been proposed for point prediction of batte... |
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