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Title: Optimizing Airborne Wind Energy with Reinforcement Learning Abstract: Airborne Wind Energy is a lightweight technology that allows power extraction from the wind using airborne devices such as kites and gliders, where the airfoil orientation can be dynamically controlled in order to maximize performance. The dyn... |
Title: Discovering Human-Object Interaction Concepts via Self-Compositional Learning Abstract: A comprehensive understanding of human-object interaction (HOI) requires detecting not only a small portion of predefined HOI concepts (or categories) but also other reasonable HOI concepts, while current approaches usually f... |
Title: Example-based Hypernetworks for Out-of-Distribution Generalization Abstract: While Natural Language Processing (NLP) algorithms keep reaching unprecedented milestones, out-of-distribution generalization is still challenging. In this paper we address the problem of multi-source adaptation to unknown domains: Give... |
Title: Benchmarking Algorithms for Automatic License Plate Recognition Abstract: We evaluated a lightweight Convolutional Neural Network (CNN) called LPRNet [1] for automatic License Plate Recognition (LPR). We evaluated the algorithm on two datasets, one composed of real license plate images and the other of synthetic... |
Title: Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework Abstract: The performance of deep learning models highly depends on the amount of training data. It is common practice for today's data holders to merge their datasets and train models collaboratively, which yet poses ... |
Title: DeepDPM: Deep Clustering With an Unknown Number of Clusters Abstract: Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering methods are parametric... |
Title: MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization Abstract: The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully... |
Title: Blind Source Separation for Mixture of Sinusoids with Near-Linear Computational Complexity Abstract: We propose a multi-tone decomposition algorithm that can find the frequencies, amplitudes and phases of the fundamental sinusoids in a noisy observation sequence. Under independent identically distributed Gaussia... |
Title: On the Neural Tangent Kernel Analysis of Randomly Pruned Wide Neural Networks Abstract: We study the behavior of ultra-wide neural networks when their weights are randomly pruned at the initialization, through the lens of neural tangent kernels (NTKs). We show that for fully-connected neural networks when the ne... |
Title: LibMTL: A Python Library for Multi-Task Learning Abstract: This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi-Task Learning (MTL). LibMTL considers different settings and approaches i... |
Title: Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks Abstract: Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and... |
Title: MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation Abstract: Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis. Skin cancer is one of the most common types of cancer... |
Title: Diagonal State Spaces are as Effective as Structured State Spaces Abstract: Modeling long range dependencies in sequential data is a fundamental step towards attaining human-level performance in many modalities such as text, vision, audio and video. While attention-based models are a popular and effective choice... |
Title: Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection Abstract: As large Pre-trained Language Models (PLMs) trained on large amounts of data in an unsupervised manner become more ubiquitous, identifying various types of bias in the text has come into sharp focus. Existing "Ster... |
Title: Physics Guided Generative Adversarial Networks for Generations of Crystal Materials with Symmetry Constraints Abstract: Discovering new materials is a long-standing challenging task that is critical to the progress of human society. Conventional approaches such as trial-and-error experiments and computational si... |
Title: piRank: A Probabilistic Intent Based Ranking Framework for Facebook Search Abstract: While numerous studies have been conducted in the literature exploring different types of machine learning approaches for search ranking, most of them are focused on specific pre-defined problems but only a few of them have stud... |
Title: MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering Abstract: This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS \& NEET ... |
Title: Algorithmic support of a personal virtual assistant for automating the processing of client requests Abstract: This article describes creating algorithmic support for the functioning of a personal virtual assistant, which allows automating the processing of customer requests. The study aims to reduce errors and ... |
Title: Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions Abstract: Continual learning is an emerging paradigm in machine learning, wherein a model is exposed in an online fashion to data from multiple different distributions (i.e. environments), and is expected... |
Title: Velocity continuation with Fourier neural operators for accelerated uncertainty quantification Abstract: Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies -- due to errors in the background squared-slowness model. Uncertainty quantification is essential fo... |
Title: Towards Domain Generalization in Object Detection Abstract: Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied. Recently sever... |
Title: Towards physiology-informed data augmentation for EEG-based BCIs Abstract: Most EEG-based Brain-Computer Interfaces (BCIs) require a considerable amount of training data to calibrate the classification model, owing to the high variability in the EEG data, which manifests itself between participants, but also wit... |
Title: Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network Abstract: Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general. In this paper we propose a deep le... |
Title: Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators Abstract: Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solvers for these phy... |
Title: Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge Abstract: Text-to-Speech (TTS) services that run on edge devices have many advantages compared to cloud TTS, e.g., latency and privacy issues. However, neural vocoders with a low complexity and small model footprint inevitably generat... |
Title: Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders Abstract: This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing with the reconstruction of broken artifacts or... |
Title: Risk regularization through bidirectional dispersion Abstract: Many alternative notions of "risk" (e.g., CVaR, entropic risk, DRO risk) have been proposed and studied, but these risks are all at least as sensitive as the mean to loss tails on the upside, and tend to ignore deviations on the downside. In this wor... |
Title: Optimisation-free Classification and Density Estimation with Quantum Circuits Abstract: We demonstrate the implementation of a novel machine learning framework for probability density estimation and classification using quantum circuits. The framework maps a training data set or a single data sample to the quant... |
Title: PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation Abstract: Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages... |
Title: STaR: Bootstrapping Reasoning With Reasoning Abstract: Generating step-by-step "chain-of-thought" rationales improves language model performance on complex reasoning tasks like mathematics or commonsense question-answering. However, inducing language model rationale generation currently requires either construct... |
Title: Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis Abstract: Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early detection of sepsis is critical for patient survival. In this paper, we propose a multimodal Transformer model for early s... |
Title: Enhancing Transformer Efficiency for Multivariate Time Series Classification Abstract: Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy. However, for large-scale (either high-dimensional or long-sequential) time series (TS) datasets, there is an add... |
Title: Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic Library Abstract: Mathematical reasoning recently has been shown as a hard challenge for neural systems. Abilities including expression translation, logical reasoning, and mathematics knowledge acquiring appear to be essential to over... |
Title: Conjugate Gradient Method for Generative Adversarial Networks Abstract: While the generative model has many advantages, it is not feasible to calculate the Jensen-Shannon divergence of the density function of the data and the density function of the model of deep neural networks; for this reason, various alterna... |
Title: EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English Abstract: While cultural backgrounds have been shown to affect linguistic expressions, existing natural language processing (NLP) research on culture modeling is overly coarse-grained and does not examine cultural differen... |
Title: MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design Abstract: Molecule design is a fundamental problem in molecular science and has critical applications in a variety of areas, such as drug discovery, material science, etc. However, due to the large searching space, it is impossible ... |
Title: Automated Progressive Learning for Efficient Training of Vision Transformers Abstract: Recent advances in vision Transformers (ViTs) have come with a voracious appetite for computing power, high-lighting the urgent need to develop efficient training methods for ViTs. Progressive learning, a training scheme where... |
Title: Multi-View Substructure Learning for Drug-Drug Interaction Prediction Abstract: Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment. Previous studies usually model drug information constrained on a single view such as the drug itself, leading to incomp... |
Title: Optimistic Online Convex Optimization in Dynamic Environments Abstract: In this paper, we study the optimistic online convex optimization problem in dynamic environments. Existing works have shown that Ader enjoys an $O\left(\sqrt{\left(1+P_T\right)T}\right)$ dynamic regret upper bound, where $T$ is the number o... |
Title: Robust Unlearnable Examples: Protecting Data Against Adversarial Learning Abstract: The tremendous amount of accessible data in cyberspace face the risk of being unauthorized used for training deep learning models. To address this concern, methods are proposed to make data unlearnable for deep learning models by... |
Title: Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL) Abstract: The unlabeled data are generally assumed to be normal data in detecting abnormal data via semisupervised learning. This assumption, however, causes inevitable detection error when distribution of unlabeled data is different fro... |
Title: UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning Abstract: Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization performance of deep neural networks. To c... |
Title: Gradient-Matching Coresets for Rehearsal-Based Continual Learning Abstract: The goal of continual learning (CL) is to efficiently update a machine learning model with new data without forgetting previously-learned knowledge. Most widely-used CL methods rely on a rehearsal memory of data points to be reused while... |
Title: Distributed Task Management in the Heterogeneous Fog: A Socially Concave Bandit Game Abstract: Fog computing has emerged as a potential solution to the explosive computational demand of mobile users. This potential mainly stems from the capacity of task offloading and allocation at the network edge, which reduce... |
Title: Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training? Abstract: In Neural Architecture Search (NAS), reducing the cost of architecture evaluation remains one of the most crucial challenges. Among a plethora of efforts to bypass tra... |
Title: Computer Science Named Entity Recognition in the Open Research Knowledge Graph Abstract: Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can beset the task and has b... |
Title: On-the-fly Feature Based Speaker Adaptation for Dysarthric and Elderly Speech Recognition Abstract: Automatic recognition of dysarthric and elderly speech highly challenging tasks to date. Speaker-level heterogeneity attributed to accent or gender commonly found in normal speech, when aggregated with age and spe... |
Title: New insights into four-boson renormalization group limit cycles Abstract: Using machine learning techniques, we verify that the emergence of renormalization group limit cycles beyond the unitary limit is transferred from the three-boson subsystems to the whole four-boson system. Focussing on four identical boson... |
Title: Boosting Black-Box Adversarial Attacks with Meta Learning Abstract: Deep neural networks (DNNs) have achieved remarkable success in diverse fields. However, it has been demonstrated that DNNs are very vulnerable to adversarial examples even in black-box settings. A large number of black-box attack methods have b... |
Title: To Fold or Not to Fold: a Necessary and Sufficient Condition on Batch-Normalization Layers Folding Abstract: Batch-Normalization (BN) layers have become fundamental components in the evermore complex deep neural network architectures. Such models require acceleration processes for deployment on edge devices. How... |
Title: Knowledge Distillation: Bad Models Can Be Good Role Models Abstract: Large neural networks trained in the overparameterized regime are able to fit noise to zero train error. Recent work \citep{nakkiran2020distributional} has empirically observed that such networks behave as "conditional samplers" from the noisy ... |
Title: Few-Shot Learning with Siamese Networks and Label Tuning Abstract: We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on neural textual entailment models has been found to give strong resul... |
Title: Revisiting Model-based Value Expansion Abstract: Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the effectiveness of value function learning. However, to date, these methods are being outperformed by Dyna-style algorithms with conceptually simpler 1-ste... |
Title: Random matrix analysis of deep neural network weight matrices Abstract: Neural networks have been used successfully in a variety of fields, which has led to a great deal of interest in developing a theoretical understanding of how they store the information needed to perform a particular task. We study the weigh... |
Title: Federated Learning with Position-Aware Neurons Abstract: Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i.d. data across clients make the locally updated parameters imprecisely aligned, dis... |
Title: Training speaker recognition systems with limited data Abstract: This work considers training neural networks for speaker recognition with a much smaller dataset size compared to contemporary work. We artificially restrict the amount of data by proposing three subsets of the popular VoxCeleb2 dataset. These subs... |
Title: Bi-level Doubly Variational Learning for Energy-based Latent Variable Models Abstract: Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate that requi... |
Title: Object Memory Transformer for Object Goal Navigation Abstract: This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes. To this end, we propose Object ... |
Title: Hierarchical Transformer Model for Scientific Named Entity Recognition Abstract: The task of Named Entity Recognition (NER) is an important component of many natural language processing systems, such as relation extraction and knowledge graph construction. In this work, we present a simple and effective approach... |
Title: Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach Abstract: How do property prices respond to changes in local taxes and local public services? Attempts to measure this, starting with Oates (1969), have suffered from a lack of local public service controls. ... |
Title: STUDIES: Corpus of Japanese Empathetic Dialogue Speech Towards Friendly Voice Agent Abstract: We present STUDIES, a new speech corpus for developing a voice agent that can speak in a friendly manner. Humans naturally control their speech prosody to empathize with each other. By incorporating this "empathetic dia... |
Title: Lifetime Prediction of 1550 nm DFB Laser using Machine learning Techniques Abstract: A novel approach based on an artificial neural network (ANN) for lifetime prediction of 1.55 um InGaAsP MQW-DFB laser diodes is presented. It outperforms the conventional lifetime projection using accelerated aging tests. |
Title: Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks Abstract: Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks. One key feature of TCNs is time-dilated convolution, whose optimization requires extensive ... |
Title: On the Suitability of Neural Networks as Building Blocks for The Design of Efficient Learned Indexes Abstract: With the aim of obtaining time/space improvements in classic Data Structures, an emerging trend is to combine Machine Learning techniques with the ones proper of Data Structures. This new area goes unde... |
Title: On the Handwriting Tasks' Analysis to Detect Fatigue Abstract: Practical determination of physical recovery after intense exercise is a challenging topic that must include mechanical aspects as well as cognitive ones because most of physical sport activities, as well as professional activities (including brain c... |
Title: 5G Routing Interfered Environment Abstract: 5G is the next-generation cellular network technology, with the goal of meeting the critical demand for bandwidth required to accommodate a high density of users. It employs flexible architectures to accommodate the high density. 5G is enabled by mmWave communication, ... |
Title: Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning Abstract: The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However, the use of deep learn... |
Title: MixNN: A design for protecting deep learning models Abstract: In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters. The layers in a deep learning model of MixNN are fully decentralized. It hides communication address, layer parameters and operations,... |
Title: Stochastic Parameterizations: Better Modelling of Temporal Correlations using Probabilistic Machine Learning Abstract: The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Usi... |
Title: Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals Abstract: Fast and accurate fault detection and localization in fiber optic cables is extremely important to ensure the optical network survivability and reliability. Hence there exis... |
Title: Numerical and geometrical aspects of flow-based variational quantum Monte Carlo Abstract: This article aims to summarize recent and ongoing efforts to simulate continuous-variable quantum systems using flow-based variational quantum Monte Carlo techniques, focusing for pedagogical purposes on the example of boso... |
Title: Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy Abstract: Predictions of hydrologic variables across the entire water cycle have significant value for water resource management as well as downstream applications such ... |
Title: Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition Abstract: Noise-robust automatic speech recognition degrades significantly in face of over-suppression problem, which usually exists in the front-end speech enhancement module. To alleviate such issue, we propose novel dual-path style learni... |
Title: A Framework of Meta Functional Learning for Regularising Knowledge Transfer Abstract: Machine learning classifiers' capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel frame... |
Title: Safe Active Learning for Multi-Output Gaussian Processes Abstract: Multi-output regression problems are commonly encountered in science and engineering. In particular, multi-output Gaussian processes have been emerged as a promising tool for modeling these complex systems since they can exploit the inherent corr... |
Title: Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division Abstract: Deep imitation learning requires many expert demonstrations, which can be hard to obtain, especially when many tasks are involved. However, different tasks often share similarities, so learning them jointly can gr... |
Title: WSEBP: A Novel Width-depth Synchronous Extension-based Basis Pursuit Algorithm for Multi-Layer Convolutional Sparse Coding Abstract: The pursuit algorithms integrated in multi-layer convolutional sparse coding (ML-CSC) can interpret the convolutional neural networks (CNNs). However, many current state-of-art (SO... |
Title: Time-inhomogeneous diffusion geometry and topology Abstract: Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifold learning, denoising, clustering, and visualization of high-dimensiona... |
Title: TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs Abstract: Many real world graphs contain time domain information. Temporal Graph Neural Networks capture temporal information as well as structural and contextual information in the generated dynamic node embeddings. Researchers have show... |
Title: Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings Abstract: In speaker recognition, where speech segments are mapped to embeddings on the unit hypersphere, two scoring backends are commonly used, namely cosine scoring or PLDA. Both have advantages and disadvan... |
Title: Multi-Task Learning for Visual Scene Understanding Abstract: Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: training a separate neural network for each individual task. Many real-world problems, however, call for a mul... |
Title: Learning Where to Learn in Cross-View Self-Supervised Learning Abstract: Self-supervised learning (SSL) has made enormous progress and largely narrowed the gap with the supervised ones, where the representation learning is mainly guided by a projection into an embedding space. During the projection, current meth... |
Title: Properties and Performance of the ABCDe Random Graph Model with Community Structure Abstract: In this paper, we investigate properties and performance of synthetic random graph models with a built-in community structure. Such models are important for evaluating and tuning community detection algorithms that are ... |
Title: Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices Abstract: Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements of the subject's arm affect the performance of P... |
Title: Wind speed forecast using random forest learning method Abstract: Wind speed forecasting models and their application to wind farm operations are attaining remarkable attention in the literature because of its benefits as a clean energy source. In this paper, we suggested the time series machine learning approac... |
Title: Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning Abstract: In recent years, reinforcement learning (RL) has shown outstanding performance for locomotion control of highly articulated robotic systems. Such approaches typically involve tedious reward function tuning to achieve t... |
Title: RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging Abstract: The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvasc... |
Title: Attributable Visual Similarity Learning Abstract: This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Most existing similarity learning methods exacerbate the unexplainability by mapping each sample to a single poi... |
Title: FedVLN: Privacy-preserving Federated Vision-and-Language Navigation Abstract: Data privacy is a central problem for embodied agents that can perceive the environment, communicate with humans, and act in the real world. While helping humans complete tasks, the agent may observe and process sensitive information o... |
Title: Neural Vocoder is All You Need for Speech Super-resolution Abstract: Speech super-resolution (SR) is a task to increase speech sampling rate by generating high-frequency components. Existing speech SR methods are trained in constrained experimental settings, such as a fixed upsampling ratio. These strong constra... |
Title: Controllable Dynamic Multi-Task Architectures Abstract: Multi-task learning commonly encounters competition for resources among tasks, specifically when model capacity is limited. This challenge motivates models which allow control over the relative importance of tasks and total compute cost during inference tim... |
Title: Energy-based Latent Aligner for Incremental Learning Abstract: Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks may not align well with the updates suitable for older tasks. The resu... |
Title: GIRAFFE HD: A High-Resolution 3D-aware Generative Model Abstract: 3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. In particular, the current state-of-the-art model GIRAFFE can control each object's rotation, translation, scale, and sce... |
Title: Domino: Discovering Systematic Errors with Cross-Modal Embeddings Abstract: Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e... |
Title: A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps Abstract: Climate control of buildings makes up a significant portion of global energy consumption, with groundwater heat pumps providing a suitable alternative. To prevent possibly negative interactions between heat pumps throughout... |
Title: Learning to segment fetal brain tissue from noisy annotations Abstract: Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressi... |
Title: Deep Learning and Artificial General Intelligence: Still a Long Way to Go Abstract: In recent years, deep learning using neural network architecture, i.e. deep neural networks, has been on the frontier of computer science research. It has even lead to superhuman performance in some problems, e.g., in computer vi... |
Title: Error Correction Code Transformer Abstract: Error correction code is a major part of the communication physical layer, ensuring the reliable transfer of data over noisy channels. Recently, neural decoders were shown to outperform classical decoding techniques. However, the existing neural approaches present stro... |
Title: Comparing in context: Improving cosine similarity measures with a metric tensor Abstract: Cosine similarity is a widely used measure of the relatedness of pre-trained word embeddings, trained on a language modeling goal. Datasets such as WordSim-353 and SimLex-999 rate how similar words are according to human an... |
Title: DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series Abstract: In this work, we introduce DAMNETS, a deep generative model for Markovian network time series. Time series of networks are found in many fields such as trade or payment networks in economics, contact networks in epidemiol... |
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