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Title: Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback Abstract: The problem of online learning with graph feedback has been extensively studied in the literature due to its generality and potential to model various learning tasks. Existing works mainly study the adversarial and s... |
Title: Explainable Models via Compression of Tree Ensembles Abstract: Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of... |
Title: On Privacy and Personalization in Cross-Silo Federated Learning Abstract: While the application of differential privacy (DP) has been well-studied in cross-device federated learning (FL), there is a lack of work considering DP for cross-silo FL, a setting characterized by a limited number of clients each contain... |
Title: Multimodal Dialogue State Tracking Abstract: Designed for tracking user goals in dialogues, a dialogue state tracker is an essential component in a dialogue system. However, the research of dialogue state tracking has largely been limited to unimodality, in which slots and slot values are limited by knowledge do... |
Title: Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence Abstract: A major challenge in modern machine learning is theoretically understanding the generalization properties of overparameterized models. Many existing tools rely on \em uniform convergence \em (UC), a property that, whe... |
Title: Generalization Bounds for Data-Driven Numerical Linear Algebra Abstract: Data-driven algorithms can adapt their internal structure or parameters to inputs from unknown application-specific distributions, by learning from a training sample of inputs. Several recent works have applied this approach to problems in ... |
Title: Pure Exploration of Causal Bandits Abstract: Causal bandit problem integrates causal inference with multi-armed bandits. The pure exploration of causal bandits is the following online learning task: given a causal graph with unknown causal inference distributions, in each round we can choose to either intervene ... |
Title: Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization Abstract: We report on aggressive quantization strategies that greatly accelerate inference of Recurrent Neural Network Transducers (RNN-T). We use a 4 bit integer representation for both we... |
Title: Domain Generalization via Selective Consistency Regularization for Time Series Classification Abstract: Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during training. Popular domain alignment ... |
Title: Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training Abstract: Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization. However, previous ODL approaches reg... |
Title: Let Invariant Rationale Discovery Inspire Graph Contrastive Learning Abstract: Leading graph contrastive learning (GCL) methods perform graph augmentations in two fashions: (1) randomly corrupting the anchor graph, which could cause the loss of semantic information, or (2) using domain knowledge to maintain sali... |
Title: EPG2S: Speech Generation and Speech Enhancement based on Electropalatography and Audio Signals using Multimodal Learning Abstract: Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the abili... |
Title: The Scattering Transform Network with Generalized Morse Wavelets and Its Application to Music Genre Classification Abstract: We propose to use the Generalized Morse Wavelets (GMWs) instead of commonly-used Morlet (or Gabor) wavelets in the Scattering Transform Network (STN), which we call the GMW-STN, for signal... |
Title: Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm Abstract: Quantum computing is anticipated to offer immense computational capabilities which could provide efficient solutions to many data science problems. However, the current generation of quantum devices are... |
Title: Conformal prediction set for time-series Abstract: When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods. In this paper, we devel... |
Title: Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning Abstract: Class-incremental learning (CIL) suffers from the notorious dilemma between learning newly added classes and preserving previously learned class knowledge. That catastrophic forgetting issue could be mitigated by storing histori... |
Title: Architectural Backdoors in Neural Networks Abstract: Machine learning is vulnerable to adversarial manipulation. Previous literature has demonstrated that at the training stage attackers can manipulate data and data sampling procedures to control model behaviour. A common attack goal is to plant backdoors i.e. f... |
Title: Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness Abstract: Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish. The main hurdle lies in the massive amount of non-lin... |
Title: Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization Abstract: Real-world data collected from multiple domains can have multiple, distinct distribution shifts over multiple attributes. However, state-of-the art advances in domain generalization (DG) algorithms focus only on sp... |
Title: Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature Abstract: Many methods for performing multi-objective optimisation of computationally expensive problems have been proposed recently. Typically, a probabilistic surrogate for each objective is constructed from an initial d... |
Title: Adaptive Expert Models for Personalization in Federated Learning Abstract: Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-Independent and Ide... |
Title: Metric-Fair Classifier Derandomization Abstract: We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier $f: X \to [0,1]$, sample a deterministic classifier $\hat{f}: X \to \{0,1\}$ that approximates the output of $f$ in aggregate over any data distribution. R... |
Title: Large-Scale Differentiable Causal Discovery of Factor Graphs Abstract: A common theme in causal inference is learning causal relationships between observed variables, also known as causal discovery. This is usually a daunting task, given the large number of candidate causal graphs and the combinatorial nature of... |
Title: Discovery of the Content and Engagement with the Content Abstract: In the second half of the 20th century, Parliament allowed broadcasters to transmit radio and eventually television coverage of debates and meetings of select committees. More recently, in an effort to further improve transparency and citizen eng... |
Title: Search-Based Testing Approach for Deep Reinforcement Learning Agents Abstract: Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challeng... |
Title: Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems Abstract: We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller ... |
Title: Beyond Adult and COMPAS: Fairness in Multi-Class Prediction Abstract: We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy ... |
Title: Gaussian Blue Noise Abstract: Among the various approaches for producing point distributions with blue noise spectrum, we argue for an optimization framework using Gaussian kernels. We show that with a wise selection of optimization parameters, this approach attains unprecedented quality, provably surpassing the... |
Title: FixEval: Execution-based Evaluation of Program Fixes for Competitive Programming Problems Abstract: Source code repositories consist of large codebases, often containing error-prone programs. The increasing complexity of software has led to a drastic rise in time and costs for identifying and fixing these defect... |
Title: On Calibrated Model Uncertainty in Deep Learning Abstract: Estimated uncertainty by approximate posteriors in Bayesian neural networks are prone to miscalibration, which leads to overconfident predictions in critical tasks that have a clear asymmetric cost or significant losses. Here, we extend the approximate i... |
Title: Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma Grading Abstract: Pancreatic cancers have one of the worst prognoses compared to other cancers, as they are diagnosed when cancer has progressed towards its latter stages. The current manual histological grading for diagnosing pancreatic adeno... |
Title: Federated Data Analytics: A Study on Linear Models Abstract: As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge compute resources are exploited to process more of the data locally. This regime of analytics is coined as federat... |
Title: Participation and Data Valuation in IoT Data Markets through Distributed Coalitions Abstract: This paper considers a market for Internet of Things (IoT) data that is used to train machine learning models. The data is supplied to the market platform through a network and the price of the data is controlled based ... |
Title: Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments Abstract: Modern Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices, with the data acquisition and processing infrastructure setting restrictions in terms of computational power and en... |
Title: A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading Abstract: Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude is lower than the peak pr... |
Title: Robust Attack Graph Generation Abstract: We present a method to learn automaton models that are more robust to input modifications. It iteratively aligns sequences to a learned model, modifies the sequences to their aligned versions, and re-learns the model. Automaton learning algorithms are typically very good ... |
Title: HyperImpute: Generalized Iterative Imputation with Automatic Model Selection Abstract: Consider the problem of imputing missing values in a dataset. One the one hand, conventional approaches using iterative imputation benefit from the simplicity and customizability of learning conditional distributions directly,... |
Title: Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport? Abstract: Wasserstein Generative Adversarial Networks (WGANs) are the popular generative models built on the theory of Optimal Transport (OT) and the Kantorovich duality. Despite the success of WGANs, it is still unclear how well the underlyin... |
Title: Pareto Invariant Risk Minimization Abstract: Despite the success of invariant risk minimization (IRM) in tackling the Out-of-Distribution generalization problem, IRM can compromise the optimality when applied in practice. The practical variants of IRM, e.g., IRMv1, have been shown to have significant gaps with I... |
Title: SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos Abstract: The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision ... |
Title: Reconstructing Training Data from Trained Neural Networks Abstract: Understanding to what extent neural networks memorize training data is an intriguing question with practical and theoretical implications. In this paper we show that in some cases a significant fraction of the training data can in fact be recons... |
Title: Hybrid full-field thermal characterization of additive manufacturing processes using physics-informed neural networks with data Abstract: Understanding the thermal behavior of additive manufacturing (AM) processes is crucial for enhancing the quality control and enabling customized process design. Most purely ph... |
Title: On the Identifiability of Nonlinear ICA: Sparsity and Beyond Abstract: Nonlinear independent component analysis (ICA) aims to recover the underlying independent latent sources from their observable nonlinear mixtures. How to make the nonlinear ICA model identifiable up to certain trivial indeterminacies is a lon... |
Title: Condensing Graphs via One-Step Gradient Matching Abstract: As training deep learning models on large dataset takes a lot of time and resources, it is desired to construct a small synthetic dataset with which we can train deep learning models sufficiently. There are recent works that have explored solutions on co... |
Title: When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints Abstract: Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process's performance. A centerpiece of a pre... |
Title: Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective Abstract: Recent years have witnessed remarkable success achieved by graph neural networks (GNNs) in many real-world applications such as recommendation and drug discovery. Despite the success, oversmoothing has been identified as one of th... |
Title: Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks Abstract: The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-precision offers substantial memory and ... |
Title: Disparate Impact in Differential Privacy from Gradient Misalignment Abstract: As machine learning becomes more widespread throughout society, aspects including data privacy and fairness must be carefully considered, and are crucial for deployment in highly regulated industries. Unfortunately, the application of ... |
Title: Improving Diversity with Adversarially Learned Transformations for Domain Generalization Abstract: To be successful in single source domain generalization, maximizing diversity of synthesized domains has emerged as one of the most effective strategies. Many of the recent successes have come from methods that pre... |
Title: Taxonomy of Benchmarks in Graph Representation Learning Abstract: Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collec... |
Title: Variable Bitrate Neural Fields Abstract: Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations. State-of-the-art results are obtained by conditioning a neural approximation with a lookup from trainable feat... |
Title: Masked Frequency Modeling for Self-Supervised Visual Pre-Training Abstract: We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models. Instead of randomly inserting mask tokens to the input embeddings in the spatial domain, in this pap... |
Title: Masked Siamese ConvNets Abstract: Self-supervised learning has shown superior performances over supervised methods on various vision benchmarks. The siamese network, which encourages embeddings to be invariant to distortions, is one of the most successful self-supervised visual representation learning approaches... |
Title: Prefix Language Models are Unified Modal Learners Abstract: With the success of vision-language pre-training, we have witnessed the state-of-the-art has been pushed on multi-modal understanding and generation. However, the current pre-training paradigm is either incapable of targeting all modalities at once (e.g... |
Title: MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields Abstract: Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, several equivariant message passing neural networks (MPNNs) have been shown ... |
Title: Diffusion Models for Video Prediction and Infilling Abstract: To predict and anticipate future outcomes or reason about missing information in a sequence is a key ability for agents to be able to make intelligent decisions. This requires strong temporally coherent generative capabilities. Diffusion models have s... |
Title: ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features Abstract: Deep learning models have achieved remarkable success in different areas of machine learning over the past decade; however, the size and complexity of these models make them difficult to understand. I... |
Title: Learning to Accelerate Partial Differential Equations via Latent Global Evolution Abstract: Simulating the time evolution of Partial Differential Equations (PDEs) of large-scale systems is crucial in many scientific and engineering domains such as fluid dynamics, weather forecasting and their inverse optimizatio... |
Title: Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator Abstract: Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications such as petroleum production, where fast... |
Title: Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Abstract: We introduce repriorisation, a data-dependent reparameterisation which transforms a Bayesian neural network (BNN) posterior to a distribution whose KL divergence to the BNN prior vanishes as layer widths grow.... |
Title: A Unified Sequence Interface for Vision Tasks Abstract: While language tasks are naturally expressed in a single, unified, modeling framework, i.e., generating sequences of tokens, this has not been the case in computer vision. As a result, there is a proliferation of distinct architectures and loss functions fo... |
Title: Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity Abstract: We propose a general framework to design posterior sampling methods for model-based RL. We show that the proposed algorithms can be analyzed by reducing regret to Hellinger distance based conditional probabil... |
Title: Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution Abstract: Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains. However, given a new detection task, it is unclear how to choose an algorithm to use, nor how to set its... |
Title: Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone Abstract: Vision-language (VL) pre-training has recently received considerable attention. However, most existing end-to-end pre-training approaches either only aim to tackle VL tasks such as image-text retrieval, visual question answering (V... |
Title: Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games Abstract: We study the performance of policy gradient methods for the subclass of Markov games known as Markov potential games (MPGs), which extends the notion of normal-form potential games to the stateful setti... |
Title: Statistical and Computational Phase Transitions in Group Testing Abstract: We study the group testing problem where the goal is to identify a set of k infected individuals carrying a rare disease within a population of size n, based on the outcomes of pooled tests which return positive whenever there is at least... |
Title: Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays Abstract: The existing analysis of asynchronous stochastic gradient descent (SGD) degrades dramatically when any delay is large, giving the impression that performance depends primarily on the delay. On the contrary, we prove much better guarantees for ... |
Title: Sublinear Algorithms for Hierarchical Clustering Abstract: Hierarchical clustering over graphs is a fundamental task in data mining and machine learning with applications in domains such as phylogenetics, social network analysis, and information retrieval. Specifically, we consider the recently popularized objec... |
Title: Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning Abstract: This paper proposes using communication pipelining to enhance the wireless spectrum utilization efficiency and convergence speed of federated learning in mobile edge computing applications... |
Title: Rethinking Initialization of the Sinkhorn Algorithm Abstract: Computing an optimal transport (OT) coupling between distributions plays an increasingly important role in machine learning. While OT problems can be solved as linear programs, adding an entropic smoothing term is known to result in solvers that are f... |
Title: Epistemic Deep Learning Abstract: The belief function approach to uncertainty quantification as proposed in the Demspter-Shafer theory of evidence is established upon the general mathematical models for set-valued observations, called random sets. Set-valued predictions are the most natural representations of un... |
Title: ARES: Locally Adaptive Reconstruction-based Anomaly Scoring Abstract: How can we detect anomalies: that is, samples that significantly differ from a given set of high-dimensional data, such as images or sensor data? This is a practical problem with numerous applications and is also relevant to the goal of making... |
Title: Sparse Subspace Clustering in Diverse Multiplex Network Model Abstract: The paper considers the DIverse MultiPLEx (DIMPLE) network model, introduced in Pensky and Wang (2021), where all layers of the network have the same collection of nodes and are equipped with the Stochastic Block Models. In addition, all lay... |
Title: BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data Abstract: Fast and accurate detection of the disease can significantly help in reducing the strain on the healthcare facility of any country to reduce ... |
Title: Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates Abstract: Robust and sparse estimation of linear regression coefficients is investigated. The situation addressed by the present paper is that covariates and noises are sampled from heavy-tailed distributions, ... |
Title: Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures Abstract: We present new classes of positive definite kernels on non-standard spaces that are integrally strictly positive definite or characteristic. In particular, we discuss radial kernels on separable Hilbert spaces, and introdu... |
Title: Machine Learning is Abduction Inference Abstract: Concept of Abduction with Gradated Contradictions is introduced here as a form of Peirce's abduction inference. The general form of abduction criterion is formalized in the proposed Logic of Gradated Contradictions and Logic of Recursive Aggregation. Common steps... |
Title: NatGen: Generative pre-training by "Naturalizing" source code Abstract: Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and translation. These models have adopted varying pre-training o... |
Title: A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions Abstract: Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as features in a vectorized form through v... |
Title: E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations Abstract: Event cameras respond to brightness changes in the scene asynchronously and independently for every pixel. Due to the properties, these cameras have distinct features: high dynamic... |
Title: Calibrating Agent-based Models to Microdata with Graph Neural Networks Abstract: Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose. In recent years, simulation-based inference methods have emerged as powerful tools for perform... |
Title: Contrastive Learning as Goal-Conditioned Reinforcement Learning Abstract: In reinforcement learning (RL), it is easier to solve a task if given a good representation. While deep RL should automatically acquire such good representations, prior work often finds that learning representations in an end-to-end fashio... |
Title: A Meta-Analysis of Distributionally-Robust Models Abstract: State-of-the-art image classifiers trained on massive datasets (such as ImageNet) have been shown to be vulnerable to a range of both intentional and incidental distribution shifts. On the other hand, several recent classifiers with favorable out-of-dis... |
Title: Bayesian Federated Learning via Predictive Distribution Distillation Abstract: For most existing federated learning algorithms, each round consists of minimizing a loss function at each client to learn an optimal model at the client, followed by aggregating these client models at the server. Point estimation of ... |
Title: On the fast convergence of minibatch heavy ball momentum Abstract: Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of acceleration in the literature. In this work, we aim to close the ga... |
Title: Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation Abstract: Open-Set Domain Adaptation (OSDA) assumes that a target domain contains unknown classes, which are not discovered in a source domain. Existing domain adversarial learning methods are not suitable for OSDA because distribution matc... |
Title: A Deep Generative Model of Neonatal Cortical Surface Development Abstract: The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to... |
Title: Body Gesture Recognition to Control a Social Robot Abstract: In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body ges... |
Title: Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming Abstract: Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challenging to learn a stab... |
Title: BaIT: Barometer for Information Trustworthiness Abstract: This paper presents a new approach to the FNC-1 fake news classification task which involves employing pre-trained encoder models from similar NLP tasks, namely sentence similarity and natural language inference, and two neural network architectures using... |
Title: Corruption-Robust Contextual Search through Density Updates Abstract: We study the problem of contextual search in the adversarial noise model. Let $d$ be the dimension of the problem, $T$ be the time horizon and $C$ be the total amount of noise in the system. For the $\eps$-ball loss, we give a tight regret bou... |
Title: QONNX: Representing Arbitrary-Precision Quantized Neural Networks Abstract: We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-ba... |
Title: Investigating Multi-Feature Selection and Ensembling for Audio Classification Abstract: Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns--particularly in classifica... |
Title: Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation Abstract: Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrians, as the annotations of the occluded parts are not available in relevant automotive datasets. For example, CityPersons,... |
Title: Large-scale, multi-centre, multi-disease validation of an AI clinical tool for cine CMR analysis Abstract: INTRODUCTION: Artificial intelligence (AI) has the potential to facilitate the automation of CMR analysis for biomarker extraction. However, most AI algorithms are trained on a specific input domain (e.g., ... |
Title: VisageSynTalk: Unseen Speaker Video-to-Speech Synthesis via Speech-Visage Feature Selection Abstract: The goal of this work is to reconstruct speech from a silent talking face video. Recent studies have shown impressive performance on synthesizing speech from silent talking face videos. However, they have not ex... |
Title: Understanding and Optimizing Deep Learning Cold-Start Latency on Edge Devices Abstract: DNNs are ubiquitous on edge devices nowadays. With its increasing importance and use cases, it's not likely to pack all DNNs into device memory and expect that each inference has been warmed up. Therefore, cold inference, the... |
Title: Predicting Gender via Eye Movements Abstract: In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard ... |
Title: Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification Abstract: Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large... |
Title: Multi-Objective Hyperparameter Optimization -- An Overview Abstract: Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal performance when hyperpara... |
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