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Title: Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees Abstract: Deep neural network-based classifiers have been shown to be vulnerable to imperceptible perturbations to their input, such as $\ell_p$-bounded norm adversarial attacks. This has motivated the development... |
Title: Efficient Image Representation Learning with Federated Sampled Softmax Abstract: Learning image representations on decentralized data can bring many benefits in cases where data cannot be aggregated across data silos. Softmax cross entropy loss is highly effective and commonly used for learning image representat... |
Title: A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices Abstract: By mimicking brain-like cognition and exploiting parallelism, hyperdimensional computing (HDC) classifiers have been emerging as a lightweight framework to achieve efficient on-device inference. Nonetheless, they have t... |
Title: What Matters For Meta-Learning Vision Regression Tasks? Abstract: Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on regression tasks with high dimensional inputs such as images. This p... |
Title: Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers Abstract: Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Cont... |
Title: Monitoring Time Series With Missing Values: a Deep Probabilistic Approach Abstract: Systems are commonly monitored for health and security through collection and streaming of multivariate time series. Advances in time series forecasting due to adoption of multilayer recurrent neural network architectures make it... |
Title: Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It Abstract: Many fundamental problems affecting the care of critically ill patients lead to similar analytical challenges: physicians cannot easily estimate the effects of at-risk medical co... |
Title: The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach Abstract: In today's world, a massive amount of data is available in almost every sector. This data has become an asset as we can use this enormous amount of data to find information. Mainly h... |
Title: Correlated quantization for distributed mean estimation and optimization Abstract: We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose error guarantee depends on the deviation of data points instead of their abs... |
Title: Investigation of Factorized Optical Flows as Mid-Level Representations Abstract: In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks. To investigate the advan... |
Title: DISCO: Comprehensive and Explainable Disinformation Detection Abstract: Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed for numerous issues, such as political agendas and manipulating financial ma... |
Title: Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems Abstract: It is well accepted that acquiring downlink channel state information in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems is challenging because of the large overhead in training a... |
Title: Addressing Bias in Visualization Recommenders by Identifying Trends in Training Data: Improving VizML Through a Statistical Analysis of the Plotly Community Feed Abstract: Machine learning is a promising approach to visualization recommendation due to its high scalability and representational power. Researchers ... |
Title: Data-Efficient Structured Pruning via Submodular Optimization Abstract: Structured pruning is an effective approach for compressing large pre-trained neural networks without significantly affecting their performance, which involves removing redundant regular regions of weights. However, current structured prunin... |
Title: Renyi Fair Information Bottleneck for Image Classification Abstract: We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB). We consider two different fairness constraints - demographic parity and equalized odds - for learning fair repres... |
Title: Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method Abstract: For robots to be effectively deployed in novel environments and tasks, they must be able to understand the feedback expressed by humans during intervention. This can either correct undesirable behavior or indicate addit... |
Title: Temporal Difference Learning for Model Predictive Control Abstract: Data-driven model predictive control has two key advantages over model-free methods: a potential for improved sample efficiency through model learning, and better performance as computational budget for planning increases. However, it is both co... |
Title: Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification Abstract: Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortality rates of breas... |
Title: Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization Abstract: Achieving invariance to nuisance transformations is a fundamental challenge in the construction of robust and reliable vision systems. Existing approaches to invariance scale exponentially with the dimension of the family... |
Title: Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition Abstract: Language model fusion helps smart assistants recognize words which are rare in acoustic data but abundant in text-only corpora (typed search logs). However, such corpora have properties that hinder downstream pe... |
Title: Learning to control from expert demonstrations Abstract: In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By first focusing on feedback linearizable systems, we show how to combine expert demonstrations into a stabilizing controller, ... |
Title: Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks Abstract: Deploying Deep Neural Networks in low-power embedded devices for real time-constrained applications requires optimization of memory and computational complexity of the networks, usually by quantizing the weights. Most of ... |
Title: Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems Abstract: Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensiv... |
Title: Evaluating Proposed Fairness Models for Face Recognition Algorithms Abstract: The development of face recognition algorithms by academic and commercial organizations is growing rapidly due to the onset of deep learning and the widespread availability of training data. Though tests of face recognition algorithm p... |
Title: Universal Regression with Adversarial Responses Abstract: We provide algorithms for regression with adversarial responses under large classes of non-i.i.d. instance sequences, on general separable metric spaces, with provably minimal assumptions. We also give characterizations of learnability in this regression ... |
Title: On the influence of over-parameterization in manifold based surrogates and deep neural operators Abstract: Constructing accurate and generalizable approximators for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. In this work, we propose new developments and perform compa... |
Title: The Transitive Information Theory and its Application to Deep Generative Models Abstract: Paradoxically, a Variational Autoencoder (VAE) could be pushed in two opposite directions, utilizing powerful decoder model for generating realistic images but collapsing the learned representation, or increasing regulariza... |
Title: Connecting sufficient conditions for domain adaptation: source-guided uncertainty, relaxed divergences and discrepancy localization Abstract: Recent advances in domain adaptation establish that requiring a low risk on the source domain and equal feature marginals degrade the adaptation's performance. At the same... |
Title: SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning Abstract: Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many interesting domains are too complex to specify... |
Title: Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning Abstract: As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to... |
Title: A Tree-Structured Multi-Task Model Recommender Abstract: Tree-structured multi-task architectures have been employed to jointly tackle multiple vision tasks in the context of multi-task learning (MTL). The major challenge is to determine where to branch out for each task given a backbone model to optimize for bo... |
Title: Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA Abstract: Temporal Graph Neural Networks (TGNNs) are powerful models to capture temporal, structural, and contextual information on temporal graphs. The generated temporal node embeddings outperform other methods in many downstream ... |
Title: Improving Neural ODEs via Knowledge Distillation Abstract: Neural Ordinary Differential Equations (Neural ODEs) construct the continuous dynamics of hidden units using ordinary differential equations specified by a neural network, demonstrating promising results on many tasks. However, Neural ODEs still do not p... |
Title: Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models Abstract: Wide neural networks with linear output layer have been shown to be near-linear, and to have near-constant neural tangent kernel (NTK), in a region containing the optimization path of gradient descent. The... |
Title: Librarian-in-the-Loop: A Natural Language Processing Paradigm for Detecting Informal Mentions of Research Data in Academic Literature Abstract: Data citations provide a foundation for studying research data impact. Collecting and managing data citations is a new frontier in archival science and scholarly communi... |
Title: Internet-augmented language models through few-shot prompting for open-domain question answering Abstract: In this work, we aim to capitalize on the unique few-shot capabilities of large-scale language models (LSLMs) to overcome some of their challenges with respect to grounding to factual and up-to-date informa... |
Title: Optimal Methods for Risk Averse Distributed Optimization Abstract: This paper studies the communication complexity of risk averse optimization over a network. The problem generalizes the well-studied risk-neutral finite-sum distributed optimization problem and its importance stems from the need to handle risk in... |
Title: Collusion Detection in Team-Based Multiplayer Games Abstract: In the context of competitive multiplayer games, collusion happens when two or more teams decide to collaborate towards a common goal, with the intention of gaining an unfair advantage from this cooperation. The task of identifying colluders from the ... |
Title: Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials Abstract: Estimating treatment effects from observational data provides insights about causality guiding many real-world applications such as different clinical study designs, which are the formulations of trials, experiments, and o... |
Title: PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks Abstract: With the increasing abundance of pretrained models in recent years, the problem of selecting the best pretrained checkpoint for a particular downstream classification task has been gaining incr... |
Title: Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization Abstract: This paper presents a reinforcement learning (RL) framework that leverages Frank-Wolfe policy optimization to address frame-level bit allocation for HEVC/H.265. Most previous R... |
Title: Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches Abstract: This study proposes a framework for manifold learning of image patches using the concept of equivalence classes: manifold modeling in quotient space (MMQS). In MMQS, we do not consider a set of local p... |
Title: IAE-Net: Integral Autoencoders for Discretization-Invariant Learning Abstract: Discretization invariant learning aims at learning in the infinite-dimensional function spaces with the capacity to process heterogeneous discrete representations of functions as inputs and/or outputs of a learning model. This paper p... |
Title: TiSAT: Time Series Anomaly Transformer Abstract: While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score. We show that a rudimentary Random Guess method can outperform state-of-t... |
Title: TextConvoNet:A Convolutional Neural Network based Architecture for Text Classification Abstract: In recent years, deep learning-based models have significantly improved the Natural Language Processing (NLP) tasks. Specifically, the Convolutional Neural Network (CNN), initially used for computer vision, has shown... |
Title: Assessing Phenotype Definitions for Algorithmic Fairness Abstract: Disease identification is a core, routine activity in observational health research. Cohorts impact downstream analyses, such as how a condition is characterized, how patient risk is defined, and what treatments are studied. It is thus critical t... |
Title: A Review of Open Source Software Tools for Time Series Analysis Abstract: Time series data is used in a wide range of real world applications. In a variety of domains , detailed analysis of time series data (via Forecasting and Anomaly Detection) leads to a better understanding of how events associated with a sp... |
Title: Clustering Label Inference Attack against Practical Split Learning Abstract: Split learning is deemed as a promising paradigm for privacy-preserving distributed learning, where the learning model can be cut into multiple portions to be trained at the participants collaboratively. The participants only exchange t... |
Title: Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement Abstract: In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers). In order... |
Title: Conditional Synthetic Data Generation for Personal Thermal Comfort Models Abstract: Personal thermal comfort models aim to predict an individual's thermal comfort response, instead of the average response of a large group. Recently, machine learning algorithms have proven to be having enormous potential as a can... |
Title: API: Boosting Multi-Agent Reinforcement Learning via Agent-Permutation-Invariant Networks Abstract: Multi-agent reinforcement learning suffers from poor sample efficiency due to the exponential growth of the state-action space. Considering a homogeneous multiagent system, a global state consisting of $m$ homogen... |
Title: BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis Abstract: Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem, due to the lack of available datasets, models and standard ev... |
Title: Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness Abstract: Robustness of deep neural networks (DNNs) to malicious perturbations is a hot topic in trustworthy AI. Existing techniques obtain robust models given fixed datasets, either by modifying model structures, or by optimizing... |
Title: AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify Entities Abstract: In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical s... |
Title: Parsimonious Physics-Informed Random Projection Neural Networks for Initial-Value Problems of ODEs and index-1 DAEs Abstract: We address a physics-informed neural network based on the concept of random projections for the numerical solution of IVPs of nonlinear ODEs in linear-implicit form and index-1 DAEs, whic... |
Title: Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Abstract: Differential privacy analysis of randomized learning algorithms typically relies on composition theorems, where the implicit assumption is that the internal state of the iterative algorithm is revealed to the adversary. How... |
Title: A Contribution-based Device Selection Scheme in Federated Learning Abstract: In a Federated Learning (FL) setup, a number of devices contribute to the training of a common model. We present a method for selecting the devices that provide updates in order to achieve improved generalization, fast convergence, and ... |
Title: Forecasting the abnormal events at well drilling with machine learning Abstract: We present a data-driven and physics-informed algorithm for drilling accident forecasting. The core machine-learning algorithm uses the data from the drilling telemetry representing the time-series. We have developed a Bag-of-featur... |
Title: Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation Abstract: It is common to model a deterministic response function, such as the output of a computer experiment, as a Gaussian process with a Mat\'ern covariance kernel. The smoothness parameter of a Mat\'ern kernel determines ... |
Title: Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input Abstract: For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matr... |
Title: Blind Extraction of Equitable Partitions from Graph Signals Abstract: Finding equitable partitions is closely related to the extraction of graph symmetries and of interest in a variety of applications context such as node role detection, cluster synchronization, consensus dynamics, and network control problems. ... |
Title: Deep Regression Ensembles Abstract: We introduce a methodology for designing and training deep neural networks (DNN) that we call "Deep Regression Ensembles" (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE has two components, randomly ... |
Title: Semantic Norm Recognition and its application to Portuguese Law Abstract: Being able to clearly interpret legal texts and fully understanding our rights, obligations and other legal norms has become progressively more important in the digital society. However, simply giving citizens access to the laws is not eno... |
Title: Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control Abstract: Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector. Recently, controllers based on Deep Reinforcement Learning (DRL) have been shown to... |
Title: Bias-variance decomposition of overparameterized regression with random linear features Abstract: In classical statistics, the bias-variance trade-off describes how varying a model's complexity (e.g., number of fit parameters) affects its ability to make accurate predictions. According to this trade-off, optimal... |
Title: LoopITR: Combining Dual and Cross Encoder Architectures for Image-Text Retrieval Abstract: Dual encoders and cross encoders have been widely used for image-text retrieval. Between the two, the dual encoder encodes the image and text independently followed by a dot product, while the cross encoder jointly feeds i... |
Title: CoCo-FL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization Abstract: Devices participating in federated learning (FL) typically have heterogeneous communication and computation resources. However, all devices need to finish training by the same deadline dictated by ... |
Title: Prediction-Guided Distillation for Dense Object Detection Abstract: Real-world object detection models should be cheap and accurate. Knowledge distillation (KD) can boost the accuracy of a small, cheap detection model by leveraging useful information from a larger teacher model. However, a key challenge is ident... |
Title: Fully Adaptive Composition in Differential Privacy Abstract: Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However, these results require that the privacy p... |
Title: Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time Abstract: The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validat... |
Title: projUNN: efficient method for training deep networks with unitary matrices Abstract: In learning with recurrent or very deep feed-forward networks, employing unitary matrices in each layer can be very effective at maintaining long-range stability. However, restricting network parameters to be unitary typically c... |
Title: Geometric and Topological Inference for Deep Representations of Complex Networks Abstract: Understanding the deep representations of complex networks is an important step of building interpretable and trustworthy machine learning applications in the age of internet. Global surrogate models that approximate the p... |
Title: An Empirical Study of Low Precision Quantization for TinyML Abstract: Tiny machine learning (tinyML) has emerged during the past few years aiming to deploy machine learning models to embedded AI processors with highly constrained memory and computation capacity. Low precision quantization is an important model c... |
Title: Towards Less Constrained Macro-Neural Architecture Search Abstract: Networks found with Neural Architecture Search (NAS) achieve state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, most NAS methods heavily rely on human-defined assumptions that constrain the searc... |
Title: SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network Accelerators under Soft Errors Abstract: Specialized hardware accelerators have been designed and employed to maximize the performance efficiency of Spiking Neural Networks (SNNs). However, such accelerators are vulnerable to transient faults (i.e., so... |
Title: Context is Everything: Implicit Identification for Dynamics Adaptation Abstract: Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables such as environment parameters cannot necessarily be p... |
Title: On Embeddings for Numerical Features in Tabular Deep Learning Abstract: Recently, Transformer-like deep architectures have shown strong performance on tabular data problems. Unlike traditional models, e.g., MLP, these architectures map scalar values of numerical features to high-dimensional embeddings before mix... |
Title: Conditional Prompt Learning for Vision-Language Models Abstract: With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets. A recently proposed method named Context Optimization (CoOp) introduces the concept of pr... |
Title: Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients Abstract: Glioblastomas are the most common malignant brain tumors in adults. Approximately 200000 people die each year from Glioblastoma in the world. Glioblastoma patients have a median survival of 12 months with optima... |
Title: SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video Games Using Risk Based Testing and Machine Learning Abstract: Testing video games is an increasingly difficult task as traditional methods fail to scale with growing software systems. Manual testing is a very labor-intensive process, and the... |
Title: neos: End-to-End-Optimised Summary Statistics for High Energy Physics Abstract: The advent of deep learning has yielded powerful tools to automatically compute gradients of computations. This is because training a neural network equates to iteratively updating its parameters using gradient descent to find the mi... |
Title: Self Pre-training with Masked Autoencoders for Medical Image Analysis Abstract: Masked Autoencoder (MAE) has recently been shown to be effective in pre-training Vision Transformers (ViT) for natural image analysis. By performing the pretext task of reconstructing the original image from only partial observations... |
Title: Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision Abstract: Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary. An outstanding challenge is identifying electrode activation patterns that lead to intelligible... |
Title: A Linearithmic Time Locally Optimal Algorithm for the Multiway Number Partition Optimization Abstract: We study the problem of multiway number partition optimization, which has a myriad of applications in the decision, learning and optimization literature. Even though the original multiway partitioning problem i... |
Title: Koopman Methods for Estimation of Animal Motions over Unknown Submanifolds Abstract: This paper introduces a data-dependent approximation of the forward kinematics map for certain types of animal motion models. It is assumed that motions are supported on a low-dimensional, unknown configuration manifold $Q$ that... |
Title: BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets Abstract: Current semi-supervised learning (SSL) methods assume a balance between the number of data points available for each class in both the labeled and the unlabeled data sets. However, there naturally exists a class imbalance in ... |
Title: NELA-GT-2021: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles Abstract: In this paper, we present the fourth installment of the NELA-GT datasets, NELA-GT-2021. The dataset contains 1.8M articles from 367 outlets between January 1st, 2021 and December 31st, 2021. Just as in pa... |
Title: Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization Abstract: The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literature seldom explores the application of investor s... |
Title: Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation Abstract: Semi-supervised segmentation tackles the scarcity of annotations by leveraging unlabeled data with a small amount of labeled data. A prominent way to utilize the unlabeled data is by consistency training which commonl... |
Title: Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks Abstract: Continual learning, also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (H... |
Title: Learning-based Localizability Estimation for Robust LiDAR Localization Abstract: LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in rea... |
Title: Deep Binary Reinforcement Learning for Scalable Verification Abstract: The use of neural networks as function approximators has enabled many advances in reinforcement learning (RL). The generalization power of neural networks combined with advances in RL algorithms has reignited the field of artificial intellige... |
Title: DNN Training Acceleration via Exploring GPGPU Friendly Sparsity Abstract: The training phases of Deep neural network~(DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the inference phase of DNNs. However, it is hardly used in the ... |
Title: Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease Abstract: Background: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better u... |
Title: Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding Abstract: Despite recent advances of AI, story understanding remains an open and under-investigated problem. We collect, preprocess, and publicly release a video-language story dataset, Synopses of Movie Narratives(SyMoN), containing ... |
Title: Bayesian inference via sparse Hamiltonian flows Abstract: A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during Bayesian inference, with the goal of reducing computational cost. Although past work has shown empirically that there often exists a coreset with low inferential ... |
Title: Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI Abstract: Purpose: To introduce a dual-domain reconstruction network with V-Net and K-Net for accurate MR image reconstruction from undersampled k-space data. Methods: Most state-of-the-art reconstruction methods apply U-Net or cascaded U-Nets... |
Title: An Efficient Video Streaming Architecture with QoS Control for Virtual Desktop Infrastructure in Cloud Computing Abstract: In virtual desktop infrastructure (VDI) environments, the remote display protocol has a big responsibility to transmit video data from a data center-hosted desktop to the endpoint. The proto... |
Title: Learning Distinctive Margin toward Active Domain Adaptation Abstract: Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in tr... |
Title: A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction Abstract: Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-dee... |
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