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Title: A Novel Multimodal Approach for Studying the Dynamics of Curiosity in Small Group Learning Abstract: Curiosity is a vital metacognitive skill in educational contexts, leading to creativity, and a love of learning. And while many school systems increasingly undercut curiosity by teaching to the test, teachers are... |
Title: Unified and Effective Ensemble Knowledge Distillation Abstract: Ensemble knowledge distillation can extract knowledge from multiple teacher models and encode it into a single student model. Many existing methods learn and distill the student model on labeled data only. However, the teacher models are usually lea... |
Title: 1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality Abstract: This paper presents an alternate representation framework to commonly used time-frequency representation for acoustic scene classification (ASC). A raw audio signal is represented using a pre-trained convolutional neural... |
Title: What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy Abstract: Auxiliary tasks have been argued to be useful for representation learning in reinforcement learning. Although many auxiliary tasks have been empirically shown to be effective for accelerating lear... |
Title: Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation Abstract: We consider unsupervised domain adaptation (UDA), where labeled data from a source domain (e.g., photographs) and unlabeled data from a target domain (e.g., sketches) are used to learn a classifier for the target ... |
Title: Robust and Efficient Aggregation for Distributed Learning Abstract: Distributed learning paradigms, such as federated and decentralized learning, allow for the coordination of models across a collection of agents, and without the need to exchange raw data. Instead, agents compute model updates locally based on t... |
Title: Monarch: Expressive Structured Matrices for Efficient and Accurate Training Abstract: Large neural networks excel in many domains, but they are expensive to train and fine-tune. A popular approach to reduce their compute or memory requirements is to replace dense weight matrices with structured ones (e.g., spars... |
Title: Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language Abstract: Large pretrained (e.g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLM... |
Title: From Statistical to Causal Learning Abstract: We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality. Some of the hard open problems of machine learning and A... |
Title: Learning the conditional law: signatures and conditional GANs in filtering and prediction of diffusion processes Abstract: We consider the filtering and prediction problem for a diffusion process. The signal and observation are modeled by stochastic differential equations (SDEs) driven by Wiener processes. In cl... |
Title: On the Importance of Asymmetry for Siamese Representation Learning Abstract: Many recent self-supervised frameworks for visual representation learning are based on certain forms of Siamese networks. Such networks are conceptually symmetric with two parallel encoders, but often practically asymmetric as numerous ... |
Title: Simplicial Embeddings in Self-Supervised Learning and Downstream Classification Abstract: We introduce Simplicial Embeddings (SEMs) as a way to constrain the encoded representations of a self-supervised model to $L$ simplices of $V$ dimensions each using a Softmax operation. This procedure imposes a structure on... |
Title: Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations Abstract: Deep learning has in recent years achieved immense success in all areas of computer vision and has the potential of assisting medical doctors in analyzing visual content for disease and other abn... |
Title: CogNGen: Constructing the Kernel of a Hyperdimensional Predictive Processing Cognitive Architecture Abstract: We present a new cognitive architecture that combines two neurobiologically plausible, computational models: (1) a variant of predictive processing known as neural generative coding (NGC) and (2) hyperdi... |
Title: Universal Lymph Node Detection in T2 MRI using Neural Networks Abstract: Purpose: Identification of abdominal Lymph Nodes (LN) that are suspicious for metastasis in T2 Magnetic Resonance Imaging (MRI) scans is critical for staging of lymphoproliferative diseases. Prior work on LN detection has been limited to sp... |
Title: Bayesian Image Super-Resolution with Deep Modeling of Image Statistics Abstract: Modeling statistics of image priors is useful for image super-resolution, but little attention has been paid from the massive works of deep learning-based methods. In this work, we propose a Bayesian image restoration framework, whe... |
Title: Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning Abstract: In this paper, we propose an explainable and interpretable diabetic retinopathy (ExplainDR) classification model based on neural-symbolic learning. To gain explainability, a highlevel symbolic representa... |
Title: Learning Neural Acoustic Fields Abstract: Our environment is filled with rich and dynamic acoustic information. When we walk into a cathedral, the reverberations as much as appearance inform us of the sanctuary's wide open space. Similarly, as an object moves around us, we expect the sound emitted to also exhibi... |
Title: TopTemp: Parsing Precipitate Structure from Temper Topology Abstract: Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and evaluation of new manufacturing methodologies is labor-, time-, a... |
Title: UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation Abstract: Vision Transformers (ViT)s have recently become popular due to their outstanding modeling capabilities, in particular for capturing long-range information, and scalability to dataset and model si... |
Title: Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents Abstract: Chemical Mass Casualty Incidents (MCI) place a heavy burden on hospital staff and resources. Machine Learning (ML) tools can provide efficient decision suppo... |
Title: SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks Abstract: Real-world autonomous driving datasets comprise of images aggregated from different drives on the road. The ability to relight captured scenes to unseen lighting conditions, in a controllable ... |
Title: Cluster-based ensemble learning for wind power modeling with meteorological wind data Abstract: Optimal implementation and monitoring of wind energy generation hinge on reliable power modeling that is vital for understanding turbine control, farm operational optimization, and grid load balance. Based on the idea... |
Title: Knowledge distillation with error-correcting transfer learning for wind power prediction Abstract: Wind power prediction, especially for turbines, is vital for the operation, controllability, and economy of electricity companies. Hybrid methodologies combining advanced data science with weather forecasting have ... |
Title: Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid Control Abstract: Reinforcement learning (RL) is a promising approach for deriving control policies for complex systems. As we show in two control problems, the derived policies from using the Proximal Policy Optimization ... |
Title: Learnable latent embeddings for joint behavioral and neural analysis Abstract: Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural dynamics during adaptive behaviors ... |
Title: Assimilation of Satellite Active Fires Data Abstract: Wildland fires pose an increasingly serious problem in our society. The number and severity of these fires has been rising for many years. Wildfires pose direct threats to life and property as well as threats through ancillary effects like reduced air quality... |
Title: Testing Feedforward Neural Networks Training Programs Abstract: Nowadays, we are witnessing an increasing effort to improve the performance and trustworthiness of Deep Neural Networks (DNNs), with the aim to enable their adoption in safety critical systems such as self-driving cars. Multiple testing techniques a... |
Title: A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems Abstract: In this paper, we consider a wireless network of smart sensors (agents) that monitor a dynamical process and send measurements to a base station that performs global monitoring and decision-ma... |
Title: Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk Abstract: We investigate a natural but surprisingly unstudied approach to the multi-armed bandit problem under safety risk constraints. Each arm is associated with an unknown law on safety risks and rewards, and the learner's goal is to max... |
Title: Identifying Exoplanets with Machine Learning Methods: A Preliminary Study Abstract: The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require... |
Title: Analysis of Sparse Subspace Clustering: Experiments and Random Projection Abstract: Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner. As a technique that is used in many domains, such as face clustering, plant categoriz... |
Title: SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning Abstract: Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics. However, the growing threat of adversarial attacks raises serious concer... |
Title: Path Development Network with Finite-dimensional Lie Group Representation Abstract: The path signature, a mathematically principled and universal feature of sequential data, leads to a performance boost of deep learning-based models in various sequential data tasks as a complimentary feature. However, it suffers... |
Title: Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation Abstract: Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ... |
Title: Variational message passing for online polynomial NARMAX identification Abstract: We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a posterior predictive... |
Title: Speaker adaptation for Wav2vec2 based dysarthric ASR Abstract: Dysarthric speech recognition has posed major challenges due to lack of training data and heavy mismatch in speaker characteristics. Recent ASR systems have benefited from readily available pretrained models such as wav2vec2 to improve the recognitio... |
Title: Revealing the real-world CO2 emission reduction of ridesplitting and its determinants based on machine learning Abstract: Ridesplitting, which is a form of pooled ridesourcing service, has great potential to alleviate the negative impacts of ridesourcing on the environment. However, most existing studies only ex... |
Title: Distributional Gradient Boosting Machines Abstract: We present a unified probabilistic gradient boosting framework for regression tasks that models and predicts the entire conditional distribution of a univariate response variable as a function of covariates. Our likelihood-based approach allows us to either mod... |
Title: Paoding: Supervised Robustness-preserving Data-free Neural Network Pruning Abstract: When deploying pre-trained neural network models in real-world applications, model consumers often encounter resource-constraint platforms such as mobile and smart devices. They typically use the pruning technique to reduce the ... |
Title: HLDC: Hindi Legal Documents Corpus Abstract: Many populous countries including India are burdened with a considerable backlog of legal cases. Development of automated systems that could process legal documents and augment legal practitioners can mitigate this. However, there is a dearth of high-quality corpora t... |
Title: Efficient comparison of sentence embeddings Abstract: The domain of natural language processing (NLP), which has greatly evolved over the last years, has highly benefited from the recent developments in word and sentence embeddings. Such embeddings enable the transformation of complex NLP tasks, like semantic si... |
Title: AdaSmooth: An Adaptive Learning Rate Method based on Effective Ratio Abstract: It is well known that we need to choose the hyper-parameters in Momentum, AdaGrad, AdaDelta, and other alternative stochastic optimizers. While in many cases, the hyper-parameters are tuned tediously based on experience becoming more ... |
Title: Intelligence at the Extreme Edge: A Survey on Reformable TinyML Abstract: The rapid miniaturization of Machine Learning (ML) for low powered processing has opened gateways to provide cognition at the extreme edge (E.g., sensors and actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research field ... |
Title: Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks Abstract: Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data. As calculating Lipschitz constants is NP-hard, techni... |
Title: Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning Abstract: Private and public organizations regularly collect and analyze digitalized data about their associates, volunteers, clients, etc. However, because most personal data are sensitive, there is a ... |
Title: Adversarial Neon Beam: Robust Physical-World Adversarial Attack to DNNs Abstract: In the physical world, light affects the performance of deep neural networks. Nowadays, many products based on deep neural network have been put into daily life. There are few researches on the effect of light on the performance of... |
Title: A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data Abstract: High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position-transitional Latent Factor Analys... |
Title: Accurate Online Posterior Alignments for Principled Lexically-Constrained Decoding Abstract: Online alignment in machine translation refers to the task of aligning a target word to a source word when the target sequence has only been partially decoded. Good online alignments facilitate important applications suc... |
Title: Dimensionless machine learning: Imposing exact units equivariance Abstract: Units equivariance is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings. Here, we employ dimensional analysis and ideas f... |
Title: Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks Abstract: With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and news feed sit... |
Title: Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation Abstract: We present a novel class incremental learning approach based on deep neural networks, which continually learns new tasks with limited memory for storing examples in the previous tasks. Our algorithm is based on kno... |
Title: AutoProtoNet: Interpretability for Prototypical Networks Abstract: In meta-learning approaches, it is difficult for a practitioner to make sense of what kind of representations the model employs. Without this ability, it can be difficult to both understand what the model knows as well as to make meaningful corre... |
Title: Exploiting Local and Global Features in Transformer-based Extreme Multi-label Text Classification Abstract: Extreme multi-label text classification (XMTC) is the task of tagging each document with the relevant labels from a very large space of predefined categories. Recently, large pre-trained Transformer models... |
Title: Risk-Aware Control and Optimization for High-Renewable Power Grids Abstract: The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations. Indeed, the increased stochasticity in load and the ... |
Title: Model-Free and Model-Based Policy Evaluation when Causality is Uncertain Abstract: When decision-makers can directly intervene, policy evaluation algorithms give valid causal estimates. In off-policy evaluation (OPE), there may exist unobserved variables that both impact the dynamics and are used by the unknown ... |
Title: Long-tailed Extreme Multi-label Text Classification with Generated Pseudo Label Descriptions Abstract: Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe data scarce problem associated w... |
Title: Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach Abstract: Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the difference... |
Title: A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations Abstract: We present a novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently. Making effective recommendations to these time-sensitive cold-start users is critica... |
Title: Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification Abstract: To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extract the sp... |
Title: FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging Abstract: Federated learning, conducive to solving data privacy and security problems, has attracted increasing attention recently. However, the existing federated boosting model sequentially builds a decision tree model... |
Title: Towards Web Phishing Detection Limitations and Mitigation Abstract: Web phishing remains a serious cyber threat responsible for most data breaches. Machine Learning (ML)-based anti-phishing detectors are seen as an effective countermeasure, and are increasingly adopted by web-browsers and software products. Howe... |
Title: Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets Abstract: Recent advances in modeling large-scale complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex systems can require significant computational... |
Title: A Computational Analysis of Pitch Drift in Unaccompanied Solo Singing using DBSCAN Clustering Abstract: Unaccompanied vocalists usually change the tuning unintentionally and end up with a higher or lower pitch than the starting point during a long performance. This phenomenon is called pitch drift, which is depe... |
Title: On Efficiently Acquiring Annotations for Multilingual Models Abstract: When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among them, and training on a high-resource language followed by zer... |
Title: A Differentially Private Framework for Deep Learning with Convexified Loss Functions Abstract: Differential privacy (DP) has been applied in deep learning for preserving privacy of the underlying training sets. Existing DP practice falls into three categories - objective perturbation, gradient perturbation and o... |
Title: Understanding the unstable convergence of gradient descent Abstract: Most existing analyses of (stochastic) gradient descent rely on the condition that for $L$-smooth costs, the step size is less than $2/L$. However, many works have observed that in machine learning applications step sizes often do not fulfill t... |
Title: Learning-Based Approaches for Graph Problems: A Survey Abstract: Over the years, many graph problems specifically those in NP-complete are studied by a wide range of researchers. Some famous examples include graph colouring, travelling salesman problem and subgraph isomorphism. Most of these problems are typical... |
Title: Correlation Functions in Random Fully Connected Neural Networks at Finite Width Abstract: This article considers fully connected neural networks with Gaussian random weights and biases and $L$ hidden layers, each of width proportional to a large parameter $n$. For polynomially bounded non-linearities we give sha... |
Title: Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI Abstract: As research and industry moves towards large-scale models capable of numerous downstream tasks, the complexity of understanding multi-modal datasets that give nuance to models rapidly increases. A clear and thorough underst... |
Title: Faces: AI Blitz XIII Solutions Abstract: AI Blitz XIII Faces challenge hosted on www.aicrowd.com platform consisted of five problems: Sentiment Classification, Age Prediction, Mask Prediction, Face Recognition, and Face De-Blurring. Our team GLaDOS took second place. Here we present our solutions and results. Co... |
Title: Breaking the De-Pois Poisoning Defense Abstract: Attacks on machine learning models have been, since their conception, a very persistent and evasive issue resembling an endless cat-and-mouse game. One major variant of such attacks is poisoning attacks which can indirectly manipulate an ML model. It has been obse... |
Title: pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results Abstract: We present pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data. We have trained this model on thousands of actual... |
Title: Adversarially robust segmentation models learn perceptually-aligned gradients Abstract: The effects of adversarial training on semantic segmentation networks has not been thoroughly explored. While previous work has shown that adversarially-trained image classifiers can be used to perform image synthesis, we hav... |
Title: Fitting an immersed submanifold to data via Sussmann's orbit theorem Abstract: This paper describes an approach for fitting an immersed submanifold of a finite-dimensional Euclidean space to random samples. The reconstruction mapping from the ambient space to the desired submanifold is implemented as a compositi... |
Title: A System for Interactive Examination of Learned Security Policies Abstract: We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a... |
Title: Proceedings of TDA: Applications of Topological Data Analysis to Data Science, Artificial Intelligence, and Machine Learning Workshop at SDM 2022 Abstract: Topological Data Analysis (TDA) is a rigorous framework that borrows techniques from geometric and algebraic topology, category theory, and combinatorics in ... |
Title: Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion Abstract: Despite the rapid advancement of navigation algorithms, mobile robots often produce anomalous behaviors that can lead to navigation failures. The ability to detect such anomalous behaviors is a key component in modern robots to a... |
Title: Byzantine-Robust Federated Linear Bandits Abstract: In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model. Standard federated learning algorithms applied to this setting are vulnerable ... |
Title: Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs Abstract: Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human. To do so, the AI must be able to recognize the goals and constraints of the human and have the means to help them. We pr... |
Title: Seemo: A new tool for early design window view satisfaction evaluation in residential buildings Abstract: People spend approximately 90% of their lives indoors, and thus arguably, the indoor space design can significantly influence occupant well-being. Adequate views to the outside are one of the most cited indo... |
Title: Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation Abstract: Current language generation models suffer from issues such as repetition, incoherence, and hallucinations. An often-repeated hypothesis is that this brittleness of generation models is caused by th... |
Title: Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution Abstract: The conventional sliced Wasserstein is defined between two probability measures that have realizations as vectors. When comparing two probability measures over images, practitioners first need to vectorize images and then projec... |
Title: Continuous Variable Quantum MNIST Classifiers Abstract: In this paper, classical and continuous variable (CV) quantum neural network hybrid multiclassifiers are presented using the MNIST dataset. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to pr... |
Title: Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs Abstract: Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly be... |
Title: Learning Linear Symmetries in Data Using Moment Matching Abstract: It is common in machine learning and statistics to use symmetries derived from expert knowledge to simplify problems or improve performance, using methods like data augmentation or penalties. In this paper we consider the unsupervised and semi-su... |
Title: Capturing positive utilities during the estimation of recursive logit models: A prism-based approach Abstract: Although the recursive logit (RL) model has been recently popular and has led to many applications and extensions, an important numerical issue with respect to the evaluation of value functions remains ... |
Title: MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems Abstract: The task of developing a machine learning (ML) model for a particular problem is inherently open-ended, and there is an unbounded set of possible solutions. Steps of the ML development pipeline, such a... |
Title: Analysis of Joint Speech-Text Embeddings for Semantic Matching Abstract: Embeddings play an important role in many recent end-to-end solutions for language processing problems involving more than one data modality. Although there has been some effort to understand the properties of single-modality embedding spac... |
Title: Differentiable Rendering for Synthetic Aperture Radar Imagery Abstract: There is rising interest in integrating signal and image processing pipelines into deep learning training to incorporate more domain knowledge. This can lead to deep neural networks that are trained more robustly and with limited data, as we... |
Title: Into-TTS : Intonation Template based Prosody Control System Abstract: Intonations take an important role in delivering the intention of the speaker. However, current end-to-end TTS systems often fail to model proper intonations. To alleviate this problem, we propose a novel, intuitive method to synthesize speech... |
Title: FedSynth: Gradient Compression via Synthetic Data in Federated Learning Abstract: Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that could desparately affect the global model accuracy. I... |
Title: Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming Abstract: Recent advancement in the field of pervasive healthcare monitoring systems causes the generation of a huge amount of lifelog data in real-time. Chronic diseases... |
Title: Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation Abstract: Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver in... |
Title: GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment Abstract: Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled ... |
Title: Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part II-Dependent systems Abstract: In using the Bayesian network (BN) to construct the complex multistate system's reliability model as described in Part I, the memory storage requirements of the node probability t... |
Title: Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers Abstract: To maximize the accuracy and increase the overall acceptance of text classifiers, we propose a framework for the efficient, in-operation moderation of classifiers' output. Our framework focuses on use cases in which F1-scores o... |
Title: MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment Abstract: The acoustic environment can degrade speech quality during communication (e.g., video call, remote presentation, outside voice recording), and its impact is often unknown. Objective metrics for speech quality have proven chall... |
Title: Discretely Indexed Flows Abstract: In this paper we propose Discretely Indexed flows (DIF) as a new tool for solving variational estimation problems. Roughly speaking, DIF are built as an extension of Normalizing Flows (NF), in which the deterministic transport becomes stochastic, and more precisely discretely i... |
Title: Deep learning, stochastic gradient descent and diffusion maps Abstract: Stochastic gradient descent (SGD) is widely used in deep learning due to its computational efficiency but a complete understanding of why SGD performs so well remains a major challenge. It has been observed empirically that most eigenvalues ... |
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