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Title: Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention Abstract: In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify pot... |
Title: POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples Abstract: In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from outside the target classes, to improve few-shot learning. Specifically, we exploit the easily available out-of-distribution sam... |
Title: ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret Abstract: Recent techniques for approximating Nash equilibria in very large games leverage neural networks to learn approximately optimal policies (strategies). One promising line of research uses neural netwo... |
Title: Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem Abstract: Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem r... |
Title: Simplifying Polylogarithms with Machine Learning Abstract: Polylogrithmic functions, such as the logarithm or dilogarithm, satisfy a number of algebraic identities. For the logarithm, all the identities follow from the product rule. For the dilogarithm and higher-weight classical polylogarithms, the identities c... |
Title: Deep Hierarchical Planning from Pixels Abstract: Intelligent agents need to select long sequences of actions to solve complex tasks. While humans easily break down tasks into subgoals and reach them through millions of muscle commands, current artificial intelligence is limited to tasks with horizons of a few hu... |
Title: Push--Pull with Device Sampling Abstract: We consider decentralized optimization problems in which a number of agents collaborate to minimize the average of their local functions by exchanging over an underlying communication graph. Specifically, we place ourselves in an asynchronous model where only a random po... |
Title: Likelihood-free Model Choice for Simulator-based Models with the Jensen--Shannon Divergence Abstract: Choice of appropriate structure and parametric dimension of a model in the light of data has a rich history in statistical research, where the first seminal approaches were developed in 1970s, such as the Akaike... |
Title: Words are all you need? Capturing human sensory similarity with textual descriptors Abstract: Recent advances in multimodal training use textual descriptions to significantly enhance machine understanding of images and videos. Yet, it remains unclear to what extent language can fully capture sensory experiences ... |
Title: What-Is and How-To for Fairness in Machine Learning: A Survey, Reflection, and Perspective Abstract: Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addres... |
Title: Uplifting Bandits Abstract: We introduce a multi-armed bandit model where the reward is a sum of multiple random variables, and each action only alters the distributions of some of them. After each action, the agent observes the realizations of all the variables. This model is motivated by marketing campaigns an... |
Title: Sparse Fusion Mixture-of-Experts are Domain Generalizable Learners Abstract: Domain generalization (DG) aims at learning generalizable models under distribution shifts to avoid redundantly overfitting massive training data. Previous works with complex loss design and gradient constraint have not yet led to empir... |
Title: STable: Table Generation Framework for Encoder-Decoder Models Abstract: The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks. Following this constatation, we propose a framework for text-to-... |
Title: Model-Based Reinforcement Learning Is Minimax-Optimal for Offline Zero-Sum Markov Games Abstract: This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data. Specifically, consider a $\gamma$-discounted infinite-horizon Markov game with $S$ states, where the ... |
Title: Neural Collapse: A Review on Modelling Principles and Generalization Abstract: With a recent observation of the "Neural Collapse (NC)" phenomena by Papyan et al., various efforts have been made to model it and analyse the implications. Neural collapse describes that in deep classifier networks, the class feature... |
Title: Resolving the Human Subjects Status of Machine Learning's Crowdworkers Abstract: In recent years, machine learning (ML) has come to rely more heavily on crowdworkers, both for building bigger datasets and for addressing research questions requiring human interaction or judgment. Owing to the diverse tasks perfor... |
Title: Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting Abstract: The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that can be applied to state-of-the-art trans... |
Title: High-dimensional limit theorems for SGD: Effective dynamics and critical scaling Abstract: We study the scaling limits of stochastic gradient descent (SGD) with constant step-size in the high-dimensional regime. We prove limit theorems for the trajectories of summary statistics (i.e., finite-dimensional function... |
Title: Accelerating Score-based Generative Models for High-Resolution Image Synthesis Abstract: Score-based generative models (SGMs) have recently emerged as a promising class of generative models. The key idea is to produce high-quality images by recurrently adding Gaussian noises and gradients to a Gaussian sample un... |
Title: ReCo: A Dataset for Residential Community Layout Planning Abstract: Layout planning is centrally important in the field of architecture and urban design. Among the various basic units carrying urban functions, residential community plays a vital part for supporting human life. Therefore, the layout planning of r... |
Title: SYNERgy between SYNaptic consolidation and Experience Replay for general continual learning Abstract: Continual learning (CL) in the brain is facilitated by a complex set of mechanisms. This includes the interplay of multiple memory systems for consolidating information as posited by the complementary learning s... |
Title: Robust Semantic Communications with Masked VQ-VAE Enabled Codebook Abstract: Although semantic communications have exhibited satisfactory performance for a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleadin... |
Title: Few-Shot Audio-Visual Learning of Environment Acoustics Abstract: Room impulse response (RIR) functions capture how the surrounding physical environment transforms the sounds heard by a listener, with implications for various applications in AR, VR, and robotics. Whereas traditional methods to estimate RIRs assu... |
Title: Patch-based Object-centric Transformers for Efficient Video Generation Abstract: In this work, we present Patch-based Object-centric Video Transformer (POVT), a novel region-based video generation architecture that leverages object-centric information to efficiently model temporal dynamics in videos. We build up... |
Title: Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning Abstract: Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure. Specifically, the los... |
Title: Neural Diffusion Processes Abstract: Gaussian processes provide an elegant framework for specifying prior and posterior distributions over functions. They are, however, also computationally expensive, and limited by the expressivity of their covariance function. We propose Neural Diffusion Processes (NDPs), a no... |
Title: How unfair is private learning ? Abstract: As machine learning algorithms are deployed on sensitive data in critical decision making processes, it is becoming increasingly important that they are also private and fair. In this paper, we show that, when the data has a long-tailed structure, it is not possible to ... |
Title: Diffusion Curvature for Estimating Local Curvature in High Dimensional Data Abstract: We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature. Our measure uses the framework of diffusion maps, including the data diffusion operator, to structure point cloud data and ... |
Title: Hidden Markov Models with Momentum Abstract: Momentum is a popular technique for improving convergence rates during gradient descent. In this research, we experiment with adding momentum to the Baum-Welch expectation-maximization algorithm for training Hidden Markov Models. We compare discrete Hidden Markov Mode... |
Title: Classification of Stochastic Processes with Topological Data Analysis Abstract: In this study, we examine if engineered topological features can distinguish time series sampled from different stochastic processes with different noise characteristics, in both balanced and unbalanced sampling schemes. We compare o... |
Title: Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecasting Abstract: Behavior prediction models have proliferated in recent years, especially in the popular real-world robotics application of autonomous driving, where representing th... |
Title: FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization Abstract: Hyperparameter optimization (HPO) is crucial for machine learning algorithms to achieve satisfactory performance, whose progress has been boosted by related benchmarks. Nonetheless, existing efforts in benchmarking all focus on HPO f... |
Title: Predict better with less training data using a QNN Abstract: Over the past decade, machine learning revolutionized vision-based quality assessment for which convolutional neural networks (CNNs) have now become the standard. In this paper, we consider a potential next step in this development and describe a quanv... |
Title: Out-of-Distribution Detection with Class Ratio Estimation Abstract: Density-based Out-of-distribution (OOD) detection has recently been shown unreliable for the task of detecting OOD images. Various density ratio based approaches achieve good empirical performance, however methods typically lack a principled pro... |
Title: Mathematical model bridges disparate timescales of lifelong learning Abstract: Lifelong learning occurs on timescales ranging from minutes to decades. People can lose themselves in a new skill, practicing for hours until exhausted. And they can pursue mastery over days or decades, perhaps abandoning old skills e... |
Title: Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines Abstract: Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing a... |
Title: A Study of Continual Learning Methods for Q-Learning Abstract: We present an empirical study on the use of continual learning (CL) methods in a reinforcement learning (RL) scenario, which, to the best of our knowledge, has not been described before. CL is a very active recent research topic concerned with machin... |
Title: Few-shot Prompting Towards Controllable Response Generation Abstract: Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model. Recent works also exhibit the possibility of steering a chatbot's output by plugging in an appropriate prompt. Gra... |
Title: Boundary between noise and information applied to filtering neural network weight matrices Abstract: Deep neural networks have been successfully applied to a broad range of problems where overparametrization yields weight matrices which are partially random. A comparison of weight matrix singular vectors to the ... |
Title: Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata Abstract: Weighted finite automata (WFAs) have been widely applied in many fields. One of the classic problems for WFAs is probability distribution estimation over sequences of discrete symbol... |
Title: Learning in games from a stochastic approximation viewpoint Abstract: We develop a unified stochastic approximation framework for analyzing the long-run behavior of multi-agent online learning in games. Our framework is based on a "primal-dual", mirrored Robbins-Monro (MRM) template which encompasses a wide arra... |
Title: A Unified Convergence Theorem for Stochastic Optimization Methods Abstract: In this work, we provide a fundamental unified convergence theorem used for deriving expected and almost sure convergence results for a series of stochastic optimization methods. Our unified theorem only requires to verify several repres... |
Title: To remove or not remove Mobile Apps? A data-driven predictive model approach Abstract: Mobile app stores are the key distributors of mobile applications. They regularly apply vetting processes to the deployed apps. Yet, some of these vetting processes might be inadequate or applied late. The late removal of appl... |
Title: PrivHAR: Recognizing Human Actions From Privacy-preserving Lens Abstract: The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose an optimizing framework to provide robust visual privacy pr... |
Title: ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation Abstract: Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain. Contrastive learning (CL) in the context of UDA can help to better separate clas... |
Title: Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits Abstract: Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, per... |
Title: "GAN I hire you?" -- A System for Personalized Virtual Job Interview Training Abstract: Job interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. Professional job interview trainers give educative feedback about the shown behavior a... |
Title: Gradient Obfuscation Gives a False Sense of Security in Federated Learning Abstract: Federated learning has been proposed as a privacy-preserving machine learning framework that enables multiple clients to collaborate without sharing raw data. However, client privacy protection is not guaranteed by design in thi... |
Title: Sim2real for Reinforcement Learning Driven Next Generation Networks Abstract: The next generation of networks will actively embrace artificial intelligence (AI) and machine learning (ML) technologies for automation networks and optimal network operation strategies. The emerging network structure represented by O... |
Title: Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling Abstract: In this paper, we apply an multi-agent reinforcement learning (MARL) framework allowing the base station (BS) and the user equipments (UEs) to jointly learn a channel access policy and its signaling in a wireless multiple ... |
Title: Boosting the Confidence of Generalization for $L_2$-Stable Randomized Learning Algorithms Abstract: Exponential generalization bounds with near-tight rates have recently been established for uniformly stable learning algorithms. The notion of uniform stability, however, is stringent in the sense that it is invar... |
Title: $p$-Sparsified Sketches for Fast Multiple Output Kernel Methods Abstract: Kernel methods are learning algorithms that enjoy solid theoretical foundations while suffering from important computational limitations. Sketching, that consists in looking for solutions among a subspace of reduced dimension, is a widely ... |
Title: Towards Understanding Why Mask-Reconstruction Pretraining Helps in Downstream Tasks Abstract: For unsupervised pretraining, mask-reconstruction pretraining (MRP) approaches randomly mask input patches and then reconstruct pixels or semantic features of these masked patches via an auto-encoder. Then for a downstr... |
Title: Multi-channel neural networks for predicting influenza A virus hosts and antigenic types Abstract: Influenza occurs every season and occasionally causes pandemics. Despite its low mortality rate, influenza is a major public health concern, as it can be complicated by severe diseases like pneumonia. A fast, accur... |
Title: Syntactic Inductive Biases for Deep Learning Methods Abstract: In this thesis, we try to build a connection between the two schools by introducing syntactic inductive biases for deep learning models. We propose two families of inductive biases, one for constituency structure and another one for dependency struct... |
Title: Unsupervised Knowledge Adaptation for Passenger Demand Forecasting Abstract: Considering the multimodal nature of transport systems and potential cross-modal correlations, there is a growing trend of enhancing demand forecasting accuracy by learning from multimodal data. These multimodal forecasting models can i... |
Title: Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window Abstract: Since the pandemic of COVID-19, several deep learning methods were proposed to analyze the chest Computed Tomography (CT) for diagnosis. In the current situation, the disease course classification is significant ... |
Title: Entropic Convergence of Random Batch Methods for Interacting Particle Diffusion Abstract: We propose a co-variance corrected random batch method for interacting particle systems. By establishing a certain entropic central limit theorem, we provide entropic convergence guarantees for the law of the entire traject... |
Title: Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance Abstract: Many deep reinforcement learning algorithms rely on simple forms of exploration, such as the additive action-noise often used in continuous control domains. Typically, the scaling factor of this action noise i... |
Title: Binary Single-dimensional Convolutional Neural Network for Seizure Prediction Abstract: Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction. However, these methods still cannot be implemented as part of implantable or efficient wearable devices due to thei... |
Title: Machine learning-based patient selection in an emergency department Abstract: The performance of Emergency Departments (EDs) is of great importance for any health care system, as they serve as the entry point for many patients. However, among other factors, the variability of patient acuity levels and correspond... |
Title: Explanation as Question Answering based on a Task Model of the Agent's Design Abstract: We describe a stance towards the generation of explanations in AI agents that is both human-centered and design-based. We collect questions about the working of an AI agent through participatory design by focus groups. We cap... |
Title: Using Mixed-Effect Models to Learn Bayesian Networks from Related Data Sets Abstract: We commonly assume that data are a homogeneous set of observations when learning the structure of Bayesian networks. However, they often comprise different data sets that are related but not homogeneous because they have been c... |
Title: Disentangled Ontology Embedding for Zero-shot Learning Abstract: Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). However, existing ZSL methods that utilize KGs all neglect the intrins... |
Title: Motiflets -- Fast and Accurate Detection of Motifs in Time Series Abstract: A motif intuitively is a short time series that repeats itself approximately the same within a larger time series. Such motifs often represent concealed structures, such as heart beats in an ECG recording, or sleep spindles in EEG sleep ... |
Title: On gradient descent training under data augmentation with on-line noisy copies Abstract: In machine learning, data augmentation (DA) is a technique for improving the generalization performance. In this paper, we mainly considered gradient descent of linear regression under DA using noisy copies of datasets, in w... |
Title: Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models Abstract: Transfer learning aims to leverage knowledge from pre-trained models to benefit the target task. Prior transfer learning work mainly transfers from a single model. However, with the emergence of deep models pre-trained from different resou... |
Title: Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer Abstract: The increased integration of renewable energy poses a slew of technical challenges for the operation of power distribution networks. Among them, voltage fluctuations caused by the instability of r... |
Title: Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction Abstract: The task of learning to map an input set onto a permuted sequence of its elements is challenging for neural networks. Set-to-sequence problems occur in natural language processing, compute... |
Title: Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach Abstract: Rule sets are highly interpretable logical models in which the predicates for decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model comprises an unordered collection of if-th... |
Title: Latent Boundary-guided Adversarial Training Abstract: Deep Neural Networks (DNNs) have recently achieved great success in many classification tasks. Unfortunately, they are vulnerable to adversarial attacks that generate adversarial examples with a small perturbation to fool DNN models, especially in model shari... |
Title: Performance, Transparency and Time. Feature selection to speed up the diagnosis of Parkinson's disease Abstract: Accurate and early prediction of a disease allows to plan and improve a patient's quality of future life. During pandemic situations, the medical decision becomes a speed challenge in which physicians... |
Title: Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning Abstract: Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to a specific benchmark with... |
Title: Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022 Abstract: We build a classification model for the BirdCLEF 2022 challenge using unsupervised methods. We implement an unsupervised representation of the training dataset using a triplet loss on spectrogram representation of audio motifs. Our... |
Title: Quantitative performance evaluation of Bayesian neural networks Abstract: Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance. Various approaches have been investigated including Bayesian neura... |
Title: Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression Abstract: The noisy intermediate-scale quantum (NISQ) devices enable the implementation of the variational quantum circuit (VQC) for quantum neural networks (QNN). Although the VQC-based QNN has succeeded in many m... |
Title: What do we learn? Debunking the Myth of Unsupervised Outlier Detection Abstract: Even though auto-encoders (AEs) have the desirable property of learning compact representations without labels and have been widely applied to out-of-distribution (OoD) detection, they are generally still poorly understood and are u... |
Title: Metric Based Few-Shot Graph Classification Abstract: Many modern deep-learning techniques do not work without enormous datasets. At the same time, several fields demand methods working in scarcity of data. This problem is even more complex when the samples have varying structures, as in the case of graphs. Graph... |
Title: Autoregressive Perturbations for Data Poisoning Abstract: The prevalence of data scraping from social media as a means to obtain datasets has led to growing concerns regarding unauthorized use of data. Data poisoning attacks have been proposed as a bulwark against scraping, as they make data "unlearnable" by add... |
Title: Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials Abstract: A recent goal in the theory of deep learning is to identify how neural networks can escape the "lazy training," or Neural Tangent Kernel (NTK) regime, where the network is coupled with its firs... |
Title: Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations Abstract: In this paper, we present a work in progress about a deep learning based approach for automatic code optimization in polyhedral compilers. The proposed technique explores combinations of affine and non-affine ... |
Title: Integrating Symmetry into Differentiable Planning Abstract: We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms, specifically on 2D robotic path planning problems: navigation and manipulation. We first formalize the idea from Value Iterat... |
Title: Toward Certified Robustness Against Real-World Distribution Shifts Abstract: We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications and realistic deployment settings by proposing a novel ... |
Title: Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making Abstract: Objective: Shapley additive explanations (SHAP) is a popular post-hoc technique for explaining black box models. While the impact of data imbalance on predict... |
Title: Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression Abstract: Recent advances in distributed optimization and learning have shown that communication compression is one of the most effective means of reducing communication. While there have been many results on conver... |
Title: Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning Abstract: For those seeking healthcare advice online, AI based dialogue agents capable of interacting with patients to perform automatic disease diagnosis are a viable option. This application necessitates efficient inqui... |
Title: Joint Adversarial Learning for Cross-domain Fair Classification Abstract: Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discrimination and unfairness, which hinder their adoption on high-stak... |
Title: pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning Abstract: Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients. However, standard... |
Title: Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization Abstract: Brain inspired spiking neural networks (SNNs) have been successfully applied to many pattern recognition domains. The SNNs based deep structure have achieved considerable results in perc... |
Title: Neural Bandit with Arm Group Graph Abstract: Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information. Motivated by the fact that the arms usually exhibit group behaviors and the mutual impacts exist among groups, we introduce a new mode... |
Title: Alternately Optimized Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have demonstrated powerful representation capability in numerous graph-based tasks. Specifically, the decoupled structures of GNNs such as APPNP become popular due to their simplicity and performance advantages. However, the end-t... |
Title: Network Report: A Structured Description for Network Datasets Abstract: The rapid development of network science and technologies depends on shareable datasets. Currently, there is no standard practice for reporting and sharing network datasets. Some network dataset providers only share links, while others provi... |
Title: Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping Abstract: In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions. A common approach to uncertainty estimation maintains an ensemble of models. In recent years,... |
Title: Can Backdoor Attacks Survive Time-Varying Models? Abstract: Backdoors are powerful attacks against deep neural networks (DNNs). By poisoning training data, attackers can inject hidden rules (backdoors) into DNNs, which only activate on inputs containing attack-specific triggers. While existing work has studied b... |
Title: Subject Granular Differential Privacy in Federated Learning Abstract: This paper introduces subject granular privacy in the Federated Learning (FL) setting, where a subject is an individual whose private information is embodied by several data items either confined within a single federation user or distributed ... |
Title: Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet and IGTD Algorithm Abstract: Convolutional Neural Networks (CNN) have been a good solution for understanding a vast image dataset. As the increased number of battery-equipped electric vehicles is flourishing globally, there... |
Title: FedPop: A Bayesian Approach for Personalised Federated Learning Abstract: Personalised federated learning (FL) aims at collaboratively learning a machine learning model taylored for each client. Albeit promising advances have been made in this direction, most of existing approaches works do not allow for uncerta... |
Title: Towards Scalable Hyperbolic Neural Networks using Taylor Series Approximations Abstract: Hyperbolic networks have shown prominent improvements over their Euclidean counterparts in several areas involving hierarchical datasets in various domains such as computer vision, graph analysis, and natural language proces... |
Title: Decoupled Self-supervised Learning for Non-Homophilous Graphs Abstract: In this paper, we study the problem of conducting self-supervised learning for node representation learning on non-homophilous graphs. Existing self-supervised learning methods typically assume the graph is homophilous where linked nodes oft... |
Title: One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift Abstract: In this paper, we investigate $\textit{open-set recognition}$ with domain shift, where the final goal is to achieve $\textit{Source-free Universal Domain Adaptation}$ (SF-UNDA), which addresses the situation where there exist b... |
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