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Title: Convergence for score-based generative modeling with polynomial complexity Abstract: Score-based generative modeling (SGM) is a highly successful approach for learning a probability distribution from data and generating further samples. We prove the first polynomial convergence guarantees for the core mechanic b... |
Title: Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings Abstract: The ability to understand and generate languages sets human cognition apart from other known life forms'. We study a way of combing two of the most successful routes to meaning of language--statistical language models a... |
Title: Towards Understanding Sharpness-Aware Minimization Abstract: Sharpness-Aware Minimization (SAM) is a recent training method that relies on worst-case weight perturbations which significantly improves generalization in various settings. We argue that the existing justifications for the success of SAM which are ba... |
Title: Evaluating Graph Generative Models with Contrastively Learned Features Abstract: A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality. So far, most techniques use either traditional metrics based on subgraph counting, or the representati... |
Title: Contrastive Learning for Unsupervised Domain Adaptation of Time Series Abstract: Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medic... |
Title: RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans Abstract: In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost. Nevertheless, the coarse resolution may lead to d... |
Title: On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques Abstract: The purpose of this project was to collect and analyse data about the comparability and real-life applicability of published results focusing on Microsoft Windows malware, more sp... |
Title: Distributed Adversarial Training to Robustify Deep Neural Networks at Scale Abstract: Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular approach... |
Title: Near-Optimal Sample Complexity Bounds for Constrained MDPs Abstract: In contrast to the advances in characterizing the sample complexity for solving Markov decision processes (MDPs), the optimal statistical complexity for solving constrained MDPs (CMDPs) remains unknown. We resolve this question by providing min... |
Title: On the reusability of samples in active learning Abstract: An interesting but not extensively studied question in active learning is that of sample reusability: to what extent can samples selected for one learner be reused by another? This paper explains why sample reusability is of practical interest, why reusa... |
Title: Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation Abstract: This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Intera... |
Title: Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Abstract: Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior. Recently, multiple methods h... |
Title: Multi-user Co-inference with Batch Processing Capable Edge Server Abstract: Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scenarios that the energy-constrained mobile devices offload i... |
Title: Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring Abstract: The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use. We distinguish two types of learnable uncertainty: model uncertainty due t... |
Title: Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models Abstract: The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as cryptocurrencies and meme st... |
Title: Tackling Multiple Tasks with One Single Learning Framework Abstract: Deep Multi-Task Learning (DMTL) has been widely studied in the machine learning community and applied to a broad range of real-world applications. Searching for the optimal knowledge sharing in DMTL is more challenging for sequential learning p... |
Title: Learning Generalized Wireless MAC Communication Protocols via Abstraction Abstract: To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G wireless networks, conventional medium access control (MAC) procedures need to evolve to enable base stations (BSs) and user equipments (UEs) to automatica... |
Title: Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network Abstract: Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration method... |
Title: Differentiable and Transportable Structure Learning Abstract: We are interested in unsupervised structure learning with a particular focus on directed acyclic graphical (DAG) models. Compute required to infer these structures is typically super-exponential in the amount of variables, as inference requires a swee... |
Title: Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets Abstract: Smart manufacturing systems are being deployed at a growing rate because of their ability to interpret a wide variety of sensed information and act on the knowledge gleaned from system observations. In many cases, the ... |
Title: Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process Abstract: In typical scenarios where the Federated Learning (FL) framework applies, it is common for clients to have insufficient training data to produce an accurate model. Thus, models that provide not only point estimations, bu... |
Title: EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning Abstract: Recent state-of-the-art methods in semi-supervised learning (SSL) combine consistency regularization with confidence-based pseudo-labeling. To obtain high-quality pseudo-labels, a high confidence threshold is typically adopted. Howe... |
Title: Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation Abstract: The task of unsupervised semantic segmentation aims to cluster pixels into semantically meaningful groups. Specifically, pixels assigned to the same cluster should share high-level semantic properties like their object or... |
Title: Does a Technique for Building Multimodal Representation Matter? -- Comparative Analysis Abstract: Creating a meaningful representation by fusing single modalities (e.g., text, images, or audio) is the core concept of multimodal learning. Although several techniques for building multimodal representations have be... |
Title: Dynamic stability of power grids -- new datasets for Graph Neural Networks Abstract: One of the key challenges for the success of the energy transition towards renewable energies is the analysis of the dynamic stability of power grids. However, dynamic solutions are intractable and exceedingly expensive for larg... |
Title: Don't "research fast and break things": On the ethics of Computational Social Science Abstract: This article is concerned with setting up practical guardrails within the research activities and environments of CSS. It aims to provide CSS scholars, as well as policymakers and other stakeholders who apply CSS meth... |
Title: Darknet Traffic Classification and Adversarial Attacks Abstract: The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activitie... |
Title: Acceleration of cerebral blood flow and arterial transit time maps estimation from multiple post-labeling delay arterial spin-labeled MRI via deep learning Abstract: Purpose: Arterial spin labeling (ASL) perfusion imaging indicates direct and absolute measurement of cerebral blood flow (CBF). Arterial transit ti... |
Title: Compositional Mixture Representations for Vision and Text Abstract: Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the co... |
Title: Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa Abstract: Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well ... |
Title: Image-based Treatment Effect Heterogeneity Abstract: Randomized controlled trials (RCTs) are considered the gold standard for estimating the effects of interventions. Recent work has studied effect heterogeneity in RCTs by conditioning estimates on tabular variables such as age and ethnicity. However, such varia... |
Title: GraphMLP: A Graph MLP-Like Architecture for 3D Human Pose Estimation Abstract: Modern multi-layer perceptron (MLP) models have shown competitive results in learning visual representations without self-attention. However, existing MLP models are not good at capturing local details and lack prior knowledge of huma... |
Title: Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data Abstract: Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomi... |
Title: Look, Radiate, and Learn: Self-supervised Localisation via Radio-Visual Correspondence Abstract: Next generation cellular networks will implement radio sensing functions alongside customary communications, thereby enabling unprecedented worldwide sensing coverage outdoors. Deep learning has revolutionised comput... |
Title: Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward Abstract: The remarkable success of reinforcement learning (RL) heavily relies on observing the reward of every visited state-action pair. In many real world applications, however, an agent can observe only a score that represents the ... |
Title: SmartGD: A Self-Challenging Generative Adversarial Network for Graph Drawing Abstract: A multitude of studies have been conducted on graph drawing, but many existing methods only focus on optimizing particular aesthetic aspects of graph layout. Given a graph, generating a good layout that satisfies certain human... |
Title: Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GAN Abstract: Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult in large part due to privacy concerns. For this reason, generative image models are highly sought after to ... |
Title: Density Estimation with Autoregressive Bayesian Predictives Abstract: Bayesian methods are a popular choice for statistical inference in small-data regimes due to the regularization effect induced by the prior, which serves to counteract overfitting. In the context of density estimation, the standard Bayesian ap... |
Title: Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification Abstract: It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to align deep models' decision-making pr... |
Title: Invariant Structure Learning for Better Generalization and Causal Explainability Abstract: Learning the causal structure behind data is invaluable for improving generalization and obtaining high-quality explanations. We propose a novel framework, Invariant Structure Learning (ISL), that is designed to improve ca... |
Title: Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery Abstract: Developing Machine Learning (ML) algorithms for heterogeneous/mixed data is a longstanding problem. Many ML algorithms are not applicable to mixed data, which include numeric and non-numeric data, text, gra... |
Title: Robust Distillation for Worst-class Performance Abstract: Knowledge distillation has proven to be an effective technique in improving the performance a student model using predictions from a teacher model. However, recent work has shown that gains in average efficiency are not uniform across subgroups in the dat... |
Title: On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice Abstract: We study two of the most popular performance metrics in medical image segmentation, Accuracy and Dice, when the target labels are noisy. For both metrics, several statements rel... |
Title: What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience Abstract: In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about environment... |
Title: Mapping fNIRS to fMRI with Neural Data Augmentation and Machine Learning Models Abstract: Advances in neuroimaging techniques have provided us novel insights into understanding how the human mind works. Functional magnetic resonance imaging (fMRI) is the most popular and widely used neuroimaging technique, and t... |
Title: The Modality Focusing Hypothesis: On the Blink of Multimodal Knowledge Distillation Abstract: Multimodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning. One common practice is to adopt a well-performed multimodal network as the teacher in the hope that i... |
Title: Multimodal Learning with Transformers: A Survey Abstract: Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and big data, Transformer-based multimodal learning has become a hot topic in A... |
Title: Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations Abstract: Adversarial training (AT) and its variants have spearheaded progress in improving neural network robustness to adversarial perturbations and common corruptions in the las... |
Title: Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training Abstract: Data clipping is crucial in reducing noise in quantization operations and improving the achievable accuracy of quantization-aware training (QAT). Current practices rely on heuristics to set clipping threshold ... |
Title: Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning Abstract: Face anti-spoofing has drawn a lot of attention due to the high security requirements in biometric authentication systems. Bringing face biometric to commercial hardware became mostly dependent on developing reliable methods for ... |
Title: Fiberwise dimensionality reduction of topologically complex data with vector bundles Abstract: Datasets with non-trivial large scale topology can be hard to embed in low-dimensional Euclidean space with existing dimensionality reduction algorithms. We propose to model topologically complex datasets using vector ... |
Title: Overparametrized linear dimensionality reductions: From projection pursuit to two-layer neural networks Abstract: Given a cloud of $n$ data points in $\mathbb{R}^d$, consider all projections onto $m$-dimensional subspaces of $\mathbb{R}^d$ and, for each such projection, the empirical distribution of the projecte... |
Title: A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization Abstract: We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is only assumed to be lower semi-continuous and prox-bounded. We combi... |
Title: Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory Abstract: We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (... |
Title: FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks Abstract: Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN. However, it is ... |
Title: Zeroth-Order Topological Insights into Iterative Magnitude Pruning Abstract: Modern-day neural networks are famously large, yet also highly redundant and compressible; there exist numerous pruning strategies in the deep learning literature that yield over 90% sparser sub-networks of fully-trained, dense architec... |
Title: LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks Abstract: Fine-tuning pretrained language models (LMs) without making any architectural changes has become a norm for learning various language downstream tasks. However, for non-language downstream tasks, a common practice is to emplo... |
Title: Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data Abstract: High-fidelity reconstruction of fluids from sparse multiview RGB videos remains a formidable challenge due to the complexity of the underlying physics as well as complex occlusion and lighting in captures. Existing solutions eithe... |
Title: Probabilistic Conformal Prediction Using Conditional Random Samples Abstract: This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP construct the predictive set based on random samples f... |
Title: Learning Enhanced Representations for Tabular Data via Neighborhood Propagation Abstract: Prediction over tabular data is an essential and fundamental problem in many important downstream tasks. However, existing methods either take a data instance of the table independently as input or do not fully utilize the ... |
Title: Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search Abstract: Improving the quality of search results can significantly enhance users experience and engagement with search engines. In spite of several recent advancements in the fields of machine learning and data mining, correctly... |
Title: Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training Abstract: The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial attacks. Although there has ... |
Title: On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis Abstract: Nonlinear independent component analysis (nICA) aims at recovering statistically independent latent components that are mixed by unknown nonlinear functions. Central to nICA is the identifiability of... |
Title: Permutation Search of Tensor Network Structures via Local Sampling Abstract: Recent works put much effort into tensor network structure search (TN-SS), aiming to select suitable tensor network (TN) structures, involving the TN-ranks, formats, and so on, for the decomposition or learning tasks. In this paper, we ... |
Title: CorticalFlow$^{++}$: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability Abstract: The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurfer, CAT, or CIVET.... |
Title: Deep Isolation Forest for Anomaly Detection Abstract: Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years. It iteratively performs axis-parallel data space partition in a tree structure to isolate deviated data objects from the other data, with the isolation... |
Title: Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model Abstract: Natural Language Processing(NLP) demonstrates a great potential to support financial decision-making by analyzing the text from social media or news outlets. In this work, we build a platform to study the NLP... |
Title: Transformers are Meta-Reinforcement Learners Abstract: The transformer architecture and variants presented remarkable success across many machine learning tasks in recent years. This success is intrinsically related to the capability of handling long sequences and the presence of context-dependent weights from t... |
Title: Explainable AI for High Energy Physics Abstract: Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to convey information and build the desired input-output relationship are often intrac... |
Title: Confidence Score for Source-Free Unsupervised Domain Adaptation Abstract: Source-free unsupervised domain adaptation (SFUDA) aims to obtain high performance in the unlabeled target domain using the pre-trained source model, not the source data. Existing SFUDA methods assign the same importance to all target samp... |
Title: SpecNet2: Orthogonalization-free spectral embedding by neural networks Abstract: Spectral methods which represent data points by eigenvectors of kernel matrices or graph Laplacian matrices have been a primary tool in unsupervised data analysis. In many application scenarios, parametrizing the spectral embedding ... |
Title: The Kidneys Are Not All Normal: Investigating the Speckle Distributions of Transplanted Kidneys Abstract: Modelling ultrasound speckle has generated considerable interest for its ability to characterize tissue properties. As speckle is dependent on the underlying tissue architecture, modelling it may aid in task... |
Title: The Open Kidney Ultrasound Data Set Abstract: Ultrasound use is because of its low cost, non-ionizing, and non-invasive characteristics, and has established itself as a cornerstone radiological examination. Research on ultrasound applications has also expanded, especially with image analysis with machine learnin... |
Title: SoTeacher: A Student-oriented Teacher Network Training Framework for Knowledge Distillation Abstract: How to train an ideal teacher for knowledge distillation is still an open problem. It has been widely observed that a teacher minimizing the empirical risk not necessarily yields the best performing student, sug... |
Title: Learning Best Combination for Efficient N:M Sparsity Abstract: By forcing at most N out of M consecutive weights to be non-zero, the recent N:M network sparsity has received increasing attention for its two attractive advantages: 1) Promising performance at a high sparsity. 2) Significant speedups on NVIDIA A100... |
Title: Energy Flows: Towards Determinant-Free Training of Normalizing Flows Abstract: Normalizing flows are a popular approach for constructing probabilistic and generative models. However, maximum likelihood training of flows is challenging due to the need to calculate computationally expensive determinants of Jacobia... |
Title: Severe Damage Recovery in Evolving Soft Robots through Differentiable Programming Abstract: Biological systems are very robust to morphological damage, but artificial systems (robots) are currently not. In this paper we present a system based on neural cellular automata, in which locomoting robots are evolved an... |
Title: Matching Pursuit Based Scheduling for Over-the-Air Federated Learning Abstract: This paper develops a class of low-complexity device scheduling algorithms for over-the-air federated learning via the method of matching pursuit. The proposed scheme tracks closely the close-to-optimal performance achieved by differ... |
Title: Exploring speaker enrolment for few-shot personalisation in emotional vocalisation prediction Abstract: In this work, we explore a novel few-shot personalisation architecture for emotional vocalisation prediction. The core contribution is an `enrolment' encoder which utilises two unlabelled samples of the target... |
Title: Causal Discovery for Fairness Abstract: It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination against individuals or minor... |
Title: Bandwidth Enables Generalization in Quantum Kernel Models Abstract: Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings. For example, quantum kernel methods have been shown to provide an exponential speedup on a learning version of... |
Title: COVIDHunter: COVID-19 pandemic wave prediction and mitigation via seasonality-aware modeling Abstract: Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests p... |
Title: Generalizing experimental findings: identification beyond adjustments Abstract: We aim to generalize the results of a randomized controlled trial (RCT) to a target population with the help of some observational data. This is a problem of causal effect identification with multiple data sources. Challenges arise w... |
Title: CNN-based Classification Framework for Tissues of Lung with Additional Information Abstract: Interstitial lung diseases are a large group of heterogeneous diseases characterized by different degrees of alveolitis and pulmonary fibrosis. Accurately diagnosing these diseases has significant guiding value for formu... |
Title: Task Transfer and Domain Adaptation for Zero-Shot Question Answering Abstract: Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning methods to new domains, labeled data may not always be avail... |
Title: Conformal Off-Policy Prediction Abstract: Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected return, define the target parameter through averaging and provide a point estimator only. I... |
Title: Visual Radial Basis Q-Network Abstract: While reinforcement learning (RL) from raw images has been largely investigated in the last decade, existing approaches still suffer from a number of constraints. The high input dimension is often handled using either expert knowledge to extract handcrafted features or env... |
Title: Stein Variational Goal Generation For Reinforcement Learning in Hard Exploration Problems Abstract: Multi-goal Reinforcement Learning has recently attracted a large amount of research interest. By allowing experience to be shared between related training tasks, this setting favors generalization for new tasks at... |
Title: Deep Variational Implicit Processes Abstract: Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesian neural networks or neural samplers. IPs can be used as priors over functions, result... |
Title: Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images Abstract: Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. If the motion artefacts alter a cor... |
Title: Adversarial Vulnerability of Randomized Ensembles Abstract: Despite the tremendous success of deep neural networks across various tasks, their vulnerability to imperceptible adversarial perturbations has hindered their deployment in the real world. Recently, works on randomized ensembles have empirically demonst... |
Title: Counting Markov Equivalent Directed Acyclic Graphs Consistent with Background Knowledge Abstract: A polynomial-time exact algorithm for counting the number of directed acyclic graphs in a Markov equivalence class was recently given by Wien\"obst, Bannach, and Li\'skiewicz (AAAI 2021). In this paper, we consider ... |
Title: RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search Abstract: Social bots are referred to as the automated accounts on social networks that make attempts to behave like human. While Graph Neural Networks (GNNs) has been massively applied to the field of social bot detect... |
Title: Universally Expressive Communication in Multi-Agent Reinforcement Learning Abstract: Allowing agents to share information through communication is crucial for solving complex tasks in multi-agent reinforcement learning. In this work, we consider the question of whether a given communication protocol can express ... |
Title: Supervised Dictionary Learning with Auxiliary Covariates Abstract: Supervised dictionary learning (SDL) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. The goal of SDL is to learn a class-discrimi... |
Title: DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning Abstract: Transfer learning has been widely used in natural language processing through deep pretrained language models, such as Bidirectional Encoder Representations from Transformers and Universal Sentence Encoder. Despite the great ... |
Title: Quantitative performance evaluation of Bayesian neural networks Abstract: Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance. Various approaches have been investigated including Bayesian neura... |
Title: Physics-driven Deep Learning for PET/MRI Abstract: In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, ... |
Title: PhML-DyR: A Physics-Informed ML framework for Dynamic Reconfiguration in Power Systems Abstract: A transformation of the US electricity sector is underway with aggressive targets to achieve 100% carbon pollution-free electricity by 2035. To achieve this objective while maintaining a safe and reliable power grid,... |
Title: The Dynamics of Riemannian Robbins-Monro Algorithms Abstract: Many important learning algorithms, such as stochastic gradient methods, are often deployed to solve nonlinear problems on Riemannian manifolds. Motivated by these applications, we propose a family of Riemannian algorithms generalizing and extending t... |
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