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Title: Recursive Monte Carlo and Variational Inference with Auxiliary Variables Abstract: A key challenge in applying Monte Carlo and variational inference (VI) is the design of proposals and variational families that are flexible enough to closely approximate the posterior, but simple enough to admit tractable densiti... |
Title: Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization Abstract: We study the asymmetric matrix factorization problem under a natural nonconvex formulation with arbitrary overparamatrization. We consider the model-free setting with no further assumption on the rank or singular ... |
Title: Recursive Reasoning Graph for Multi-Agent Reinforcement Learning Abstract: Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requiring complex interactions, existing algorithms can suffer... |
Title: Graph Neural Network Potential for Magnetic Materials Abstract: Machine Learning (ML) interatomic potential has shown its great power in condensed matter physics. However, ML interatomic potential for a magnetic system including both structural degrees of freedom and magnetic moments has not been well developed ... |
Title: Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis Abstract: Cyberattacks from within an organization's trusted entities are known as insider threats. Anomaly detection using deep learning requires comprehensive data, but insider threat data is not readily available due to confiden... |
Title: Leveraging Reward Gradients For Reinforcement Learning in Differentiable Physics Simulations Abstract: In recent years, fully differentiable rigid body physics simulators have been developed, which can be used to simulate a wide range of robotic systems. In the context of reinforcement learning for control, thes... |
Title: Compartmental Models for COVID-19 and Control via Policy Interventions Abstract: We demonstrate an approach to replicate and forecast the spread of the SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming languages (PPLs). Our goal is to study the impact of various modeling assumptions a... |
Title: Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning Abstract: In this paper, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. To decentralize the implementation of GP training optimization algorithms, we emp... |
Title: Diffusion Maps : Using the Semigroup Property for Parameter Tuning Abstract: Diffusion maps (DM) constitute a classic dimension reduction technique, for data lying on or close to a (relatively) low-dimensional manifold embedded in a much larger dimensional space. The DM procedure consists in constructing a spect... |
Title: MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication Abstract: Communication is a hallmark of intelligence. In this work, we present MIRROR, an approach to (i) quickly learn human models from human demonstrations, and (ii) use the models for subsequent communication planning in a... |
Title: Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance Abstract: This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i.e., sensing, data forwarding, computing, and patrolling, for predictive police surveillance. Ou... |
Title: Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation Abstract: Multi-agent settings remain a fundamental challenge in the reinforcement learning (RL) domain due to the partial observability and the lack of accurate real-time interactions across agents. In this paper, we prop... |
Title: Domain Adaptation with Factorizable Joint Shift Abstract: Existing domain adaptation (DA) usually assumes the domain shift comes from either the covariates or the labels. However, in real-world applications, samples selected from different domains could have biases in both the covariates and the labels. In this ... |
Title: GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation Abstract: Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep gen... |
Title: Enabling Automated Machine Learning for Model-Driven AI Engineering Abstract: Developing smart software services requires both Software Engineering and Artificial Intelligence (AI) skills. AI practitioners, such as data scientists often focus on the AI side, for example, creating and training Machine Learning (M... |
Title: Evaluation of Interpretability Methods and Perturbation Artifacts in Deep Neural Networks Abstract: The challenge of interpreting predictions from deep neural networks has prompted the development of numerous interpretability methods. Many of interpretability methods attempt to quantify the importance of input f... |
Title: Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset Abstract: Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts whilst also providing an objective assessment. The time saved by such a process would both help ... |
Title: On Steering Multi-Annotations per Sample for Multi-Task Learning Abstract: The study of multi-task learning has drawn great attention from the community. Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously remains to be explored. Previous works attempt to modify th... |
Title: On the importance of stationarity, strong baselines and benchmarks in transport prediction problems Abstract: Over the last years, the transportation community has witnessed a tremendous amount of research contributions on new deep learning approaches for spatio-temporal forecasting. These contributions tend to ... |
Title: Towards a Responsible AI Development Lifecycle: Lessons From Information Security Abstract: Legislation and public sentiment throughout the world have promoted fairness metrics, explainability, and interpretability as prescriptions for the responsible development of ethical artificial intelligence systems. Despi... |
Title: A Perspective on Robotic Telepresence and Teleoperation using Cognition: Are we there yet? Abstract: Telepresence and teleoperation robotics have attracted a great amount of attention in the last 10 years. With the Artificial Intelligence (AI) revolution already being started, we can see a wide range of robotic ... |
Title: Smoothing with the Best Rectangle Window is Optimal for All Tapered Rectangle Windows Abstract: We investigate the optimal selection of weight windows for the problem of weighted least squares. We show that weight windows should be symmetric around its center, which is also its peak. We consider the class of tap... |
Title: Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit Abstract: We introduce a method for pricing consumer credit using recent advances in offline deep reinforcement learning. This approach relies on a static dataset and requires no assumptions on the functional form of demand. Using both re... |
Title: Coresets for Data Discretization and Sine Wave Fitting Abstract: In the \emph{monitoring} problem, the input is an unbounded stream $P={p_1,p_2\cdots}$ of integers in $[N]:=\{1,\cdots,N\}$, that are obtained from a sensor (such as GPS or heart beats of a human). The goal (e.g., for anomaly detection) is to appro... |
Title: Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment Abstract: In a warehouse environment, tasks appear dynamically. Consequently, a task management system that matches them with the workforce too early (e.g., weeks in advance) is necessarily sub-optimal. Also, the rapidly inc... |
Title: HEAR: Holistic Evaluation of Audio Representations Abstract: What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis... |
Title: A Unified View of SDP-based Neural Network Verification through Completely Positive Programming Abstract: Verifying that input-output relationships of a neural network conform to prescribed operational specifications is a key enabler towards deploying these networks in safety-critical applications. Semidefinite ... |
Title: Frames for Graph Signals on the Symmetric Group: A Representation Theoretic Approach Abstract: An important problem in the field of graph signal processing is developing appropriate overcomplete dictionaries for signals defined on different families of graphs. The Cayley graph of the symmetric group has natural ... |
Title: Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors Abstract: We present a simple and effective approach for posterior uncertainty quantification in deep operator networks (DeepONets); an emerging paradigm for supervised learning in function spaces. We adopt a frequentist appro... |
Title: Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation Abstract: Best-of-N (BoN) Average Displacement Error (ADE)/ Final Displacement Error (FDE) is the most used metric for evaluating trajectory prediction models. Yet, the BoN does not quanti... |
Title: Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably? Abstract: Meta learning aims at learning a model that can quickly adapt to unseen tasks. Widely used meta learning methods include model agnostic meta learning (MAML), implicit MAML, Bayesian MAML. Thanks to its ability ... |
Title: Leashing the Inner Demons: Self-Detoxification for Language Models Abstract: Language models (LMs) can reproduce (or amplify) toxic language seen during training, which poses a risk to their practical application. In this paper, we conduct extensive experiments to study this phenomenon. We analyze the impact of ... |
Title: ILDAE: Instance-Level Difficulty Analysis of Evaluation Data Abstract: Knowledge of questions' difficulty level helps a teacher in several ways, such as estimating students' potential quickly by asking carefully selected questions and improving quality of examination by modifying trivial and hard questions. Can ... |
Title: Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography Abstract: Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with numerou... |
Title: Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation Abstract: We present Nonparametric Approximation of Inter-Trace returns (NAIT), a Reinforcement Learning algorithm for discrete action, pixel-based environments that is both highly sample and computation efficient. ... |
Title: GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction Abstract: Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formu... |
Title: HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data Abstract: The problem of human activity recognition from mobile sensor data applies to multiple domains, such as health monitoring, personal fitness, daily life logging, and senior care. A critical challenge for tra... |
Title: SurvSet: An open-source time-to-event dataset repository Abstract: Time-to-event (T2E) analysis is a branch of statistics that models the duration of time it takes for an event to occur. Such events can include outcomes like death, unemployment, or product failure. Most modern machine learning (ML) algorithms, l... |
Title: Singular Value Perturbation and Deep Network Optimization Abstract: We develop new theoretical results on matrix perturbation to shed light on the impact of architecture on the performance of a deep network. In particular, we explain analytically what deep learning practitioners have long observed empirically: t... |
Title: HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data Abstract: Traffic accident forecasting is a significant problem for transportation management and public safety. However, this problem is challenging due to the spatial heterogeneity of the en... |
Title: Prediction of transport property via machine learning molecular movements Abstract: Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are represented by multiple features, such as thermodyna... |
Title: Differentially Private Federated Learning with Local Regularization and Sparsification Abstract: User-level differential privacy (DP) provides certifiable privacy guarantees to the information that is specific to any user's data in federated learning. Existing methods that ensure user-level DP come at the cost o... |
Title: Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics Abstract: The IoT vision of a trillion connected devices over the next decade requires reliable end-to-end connectivity and automated device management platforms. While we have seen successful efforts for maintaining small IoT testb... |
Title: Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning Abstract: In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement learning based on the entropic risk measure. We propose a novel definition of sub-optimality gaps, which we call cascaded gaps, ... |
Title: Matrix Decomposition Perspective for Accuracy Assessment of Item Response Theory Abstract: The item response theory obtains the estimates and their confidence intervals for parameters of abilities of examinees and difficulties of problems by using the observed item response matrix consisting of 0/1 value element... |
Title: Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Mat\'ern Correlations Abstract: We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Mat\'ern correlations whose smoothness parameter $\nu$ is a half-integer. The proposed algorithm only... |
Title: Searching for Robust Neural Architectures via Comprehensive and Reliable Evaluation Abstract: Neural architecture search (NAS) could help search for robust network architectures, where defining robustness evaluation metrics is the important procedure. However, current robustness evaluations in NAS are not suffic... |
Title: On the Construction of Distribution-Free Prediction Intervals for an Image Regression Problem in Semiconductor Manufacturing Abstract: The high-volume manufacturing of the next generation of semiconductor devices requires advances in measurement signal analysis. Many in the semiconductor manufacturing community ... |
Title: Fast Community Detection based on Graph Autoencoder Reconstruction Abstract: With the rapid development of big data, how to efficiently and accurately discover tight community structures in large-scale networks for knowledge discovery has attracted more and more attention. In this paper, a community detection fr... |
Title: Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime Abstract: Stochastic gradient descent (SGD) has achieved great success due to its superior performance in both optimization and generalization. Most of existing generalization analyses are made for single-pass SGD, which is a less practi... |
Title: Detecting data-driven robust statistical arbitrage strategies with deep neural networks Abstract: We present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets. Robust statistical arbitrage strategies refer to self-financing trading st... |
Title: Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation Abstract: Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable for many biomedical researcher... |
Title: Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm Abstract: In this paper, we study the stochastic bandits problem with $k$ unknown heavy-tailed and corrupted reward distributions or arms with time-invariant corruption distributions. At each iteration, the player chooses an arm.... |
Title: A comparative study of several ADPCM schemes with linear and nonlinear prediction Abstract: In this paper we compare several ADPCM schemes with nonlinear prediction based on neural nets with the classical ADPCM schemes based on several linear prediction schemes. Main studied variations of the ADPCM scheme with a... |
Title: Speaker recognition by means of a combination of linear and nonlinear predictive models Abstract: This paper deals the combination of nonlinear predictive models with classical LPCC parameterization for speaker recognition. It is shown that the combination of both a measure defined over LPCC coefficients and a m... |
Title: Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features Abstract: While neural text-to-speech systems perform remarkably well in high-resource scenarios, they cannot be applied to the majority of the over 6,000 spoken languages in the world due to a lack of appropriate training... |
Title: Maximizing Conditional Independence for Unsupervised Domain Adaptation Abstract: Unsupervised domain adaptation studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different distributions. Existing methods mainly focus on matching the marginal distributions of the so... |
Title: Generalized Spectral Clustering for Directed and Undirected Graphs Abstract: Spectral clustering is a popular approach for clustering undirected graphs, but its extension to directed graphs (digraphs) is much more challenging. A typical workaround is to naively symmetrize the adjacency matrix of the directed gra... |
Title: Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization Abstract: The legacy mobility robustness optimization (MRO) in self-organizing networks aims at improving handover performance by optimizing cell-specific handover parameters. However, such solutions cannot satisfy... |
Title: A deep branching solver for fully nonlinear partial differential equations Abstract: We present a multidimensional deep learning implementation of a stochastic branching algorithm for the numerical solution of fully nonlinear PDEs. This approach is designed to tackle functional nonlinearities involving gradient ... |
Title: Semantic Segmentation in Art Paintings Abstract: Semantic segmentation is a difficult task even when trained in a supervised manner on photographs. In this paper, we tackle the problem of semantic segmentation of artistic paintings, an even more challenging task because of a much larger diversity in colors, text... |
Title: Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry Abstract: In the pharmaceutical industry, the maintenance of production machines must be audited by the regulator. In this context, the problem of predictive maintenance is not when to maintain a machine, but what p... |
Title: Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion Abstract: Techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two pr... |
Title: On Credit Assignment in Hierarchical Reinforcement Learning Abstract: Hierarchical Reinforcement Learning (HRL) has held longstanding promise to advance reinforcement learning. Yet, it has remained a considerable challenge to develop practical algorithms that exhibit some of these promises. To improve our fundam... |
Title: Neural network approach to reconstructing spectral functions and complex poles of confined particles Abstract: Reconstructing spectral functions from propagator data is difficult as solving the analytic continuation problem or applying an inverse integral transformation are ill-conditioned problems. Recent work ... |
Title: Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review Abstract: In recent years, the popularity and use of Artificial Intelligence (AI) and large investments on theInternet of Medical Things (IoMT) will be common to use products such as smart socks, smartpants, and smart shirts. These pro... |
Title: PAC-Bayesian Lifelong Learning For Multi-Armed Bandits Abstract: We present a PAC-Bayesian analysis of lifelong learning. In the lifelong learning problem, a sequence of learning tasks is observed one-at-a-time, and the goal is to transfer information acquired from previous tasks to new learning tasks. We consid... |
Title: Regularising for invariance to data augmentation improves supervised learning Abstract: Data augmentation is used in machine learning to make the classifier invariant to label-preserving transformations. Usually this invariance is only encouraged implicitly by including a single augmented input during training. ... |
Title: SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding Abstract: Prevailing deep models are single-purpose and overspecialize at individual tasks. However, when being extended to new tasks, they typically forget previously learned skills and learn from scratch. We address thi... |
Title: Dynamic ConvNets on Tiny Devices via Nested Sparsity Abstract: This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets) suited for inference tasks deployed on resource-constrained devices at the edge of the Internet-... |
Title: Automated Few-Shot Time Series Forecasting based on Bi-level Programming Abstract: New micro-grid design with renewable energy sources and battery storage systems can help improve greenhouse gas emissions and reduce the operational cost. To provide an effective short-/long-term forecasting of both energy generat... |
Title: Neural Enhancement of Factor Graph-based Symbol Detection Abstract: We study the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Cyclic factor graphs have the potential to yield low-complexity symbol detectors, but are suboptimal if the ubiquitous sum-... |
Title: L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments Abstract: Human gaze is a crucial cue used in various applications such as human-robot interaction and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze direction. However, esti... |
Title: High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks Abstract: This paper presents a method for estimating high-resolution electricity peak demand given lower resolution data. The technique won a data competition organized by the British distribution network operator ... |
Title: Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference Abstract: Full Bayesian posteriors are rarely analytically tractable, which is why real-world Bayesian inference heavily relies on approximate techniques. Approximations generally differ from the true posterior and... |
Title: Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment Abstract: We consider multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents can be brittle because they can overfit their training partners' policies. This ov... |
Title: Machine learning using longitudinal prescription and medical claims for the detection of nonalcoholic steatohepatitis (NASH) Abstract: Objectives To develop and evaluate machine learning models to detect suspected undiagnosed nonalcoholic steatohepatitis (NASH) patients for diagnostic screening and clinical mana... |
Title: Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning Abstract: Tensor networks have demonstrated significant value for machine learning in a myriad of different applications. However, optimizing tensor networks using standard gradient descent has proven to be difficult in practice... |
Title: A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control Abstract: Due to their ability to adapt to different terrains, quadruped robots have drawn much attention in the research field of robot learning. Legged mobile manipulation, where a quadruped robot is equipped with a rob... |
Title: Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors Abstract: Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applie... |
Title: Estimation and Model Misspecification: Fake and Missing Features Abstract: We consider estimation under model misspecification where there is a model mismatch between the underlying system, which generates the data, and the model used during estimation. We propose a model misspecification framework which enables... |
Title: Water and Sediment Analyse Using Predictive Models Abstract: The increasing prevalence of marine pollution during the past few decades motivated recent research to help ease the situation. Typical water quality assessment requires continuous monitoring of water and sediments at remote locations with labour inten... |
Title: Multivariate Time Series Forecasting with Latent Graph Inference Abstract: This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series. Its modularity allows it to be integrated with current univariate methods. Our approach allows to... |
Title: Attention-based Region of Interest (ROI) Detection for Speech Emotion Recognition Abstract: Automatic emotion recognition for real-life appli-cations is a challenging task. Human emotion expressions aresubtle, and can be conveyed by a combination of several emo-tions. In most existing emotion recognition studies... |
Title: Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey Abstract: Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However, the severe imbalanced data, especially the limited number of defect samples, has ... |
Title: Learning Solution Manifolds for Control Problems via Energy Minimization Abstract: A variety of control tasks such as inverse kinematics (IK), trajectory optimization (TO), and model predictive control (MPC) are commonly formulated as energy minimization problems. Numerical solutions to such problems are well-es... |
Title: Deep Neural Decision Forest for Acoustic Scene Classification Abstract: Acoustic scene classification (ASC) aims to classify an audio clip based on the characteristic of the recording environment. In this regard, deep learning based approaches have emerged as a useful tool for ASC problems. Conventional approach... |
Title: Multi-Modal Attribute Extraction for E-Commerce Abstract: To improve users' experience as they navigate the myriad of options offered by online marketplaces, it is essential to have well-organized product catalogs. One key ingredient to that is the availability of product attributes such as color or material. Ho... |
Title: Generalization Through The Lens Of Leave-One-Out Error Abstract: Despite the tremendous empirical success of deep learning models to solve various learning tasks, our theoretical understanding of their generalization ability is very limited. Classical generalization bounds based on tools such as the VC dimension... |
Title: S-Rocket: Selective Random Convolution Kernels for Time Series Classification Abstract: Random convolution kernel transform (Rocket) is a fast, efficient, and novel approach for time series feature extraction, using a large number of randomly initialized convolution kernels, and classification of the represented... |
Title: Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations Abstract: In this paper, we will evaluate the performance of graph neural networks in two distinct domains: computer vision and reinforcement learning. In the computer vision section, we seek to learn whether a ... |
Title: Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged Learning Abstract: Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs), with the goal of conducting long time prediction of the evolution of the unknown system. Training a DNN with l... |
Title: Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer Abstract: Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters. We show that, in the recently discovered Maximal Update Parametrization (muP... |
Title: State space partitioning based on constrained spectral clustering for block particle filtering Abstract: The particle filter (PF) is a powerful inference tool widely used to estimate the filtering distribution in non-linear and/or non-Gaussian problems. To overcome the curse of dimensionality of PF, the block PF... |
Title: Reinforcement Learning for Location-Aware Scheduling Abstract: Recent techniques in dynamical scheduling and resource management have found applications in warehouse environments due to their ability to organize and prioritize tasks in a higher temporal resolution. The rise of deep reinforcement learning, as a l... |
Title: EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships Abstract: Topographical structures represent connections between entities and provide a comprehensive design of complex systems. Currently these structures are used to discover correlates of neuronal and haemodynamical activity. In... |
Title: DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data Abstract: Synthetic data can be used in various applications, such as correcting bias datasets or replacing scarce original data for simulation purposes. Generative Adversarial Networks (GANs) are considered state-of-the-art for d... |
Title: On observability and optimal gain design for distributed linear filtering and prediction Abstract: This paper presents a new approach to distributed linear filtering and prediction. The problem under consideration consists of a random dynamical system observed by a multi-agent network of sensors where the networ... |
Title: Influencing Long-Term Behavior in Multiagent Reinforcement Learning Abstract: The main challenge of multiagent reinforcement learning is the difficulty of learning useful policies in the presence of other simultaneously learning agents whose changing behaviors jointly affect the environment's transition and rewa... |
Title: One Objective for All Models -- Self-supervised Learning for Topic Models Abstract: Self-supervised learning has significantly improved the performance of many NLP tasks. In this paper, we highlight a key advantage of self-supervised learning -- when applied to data generated by topic models, self-supervised lea... |
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