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Title: Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model Abstract: In high-stake scenarios like medical treatment and auto-piloting, it's risky or even infeasible to collect online experimental data to train the agent. Simulation-based training can alleviate this issue, but may suffe... |
Title: AugShuffleNet: Improve ShuffleNetV2 via More Information Communication Abstract: Based on ShuffleNetV2, we build a more powerful and efficient model family, termed as AugShuffleNets, by introducing higher frequency of cross-layer information communication for better model performance. Evaluated on the CIFAR-10 a... |
Title: ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction Abstract: Query similarity prediction task is generally solved by regression based models with square loss. Such a model is agnostic of absolute similarity values and it penalizes the regression error at all ranges of similarity values at the ... |
Title: Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns Abstract: Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such a... |
Title: Context-LSTM: a robust classifier for video detection on UCF101 Abstract: Video detection and human action recognition may be computationally expensive, and need a long time to train models. In this paper, we were intended to reduce the training time and the GPU memory usage of video detection, and achieved a co... |
Title: ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles Abstract: Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction. The ASHRAE Great Energy Predictor III... |
Title: Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD Abstract: Powered by the simplicity of lock-free asynchrony, Hogwilld! is a go-to approach to parallelize SGD over a shared-memory setting. Despite its popularity and concomitant extensions, such as PASSM+ wherein concurrent processes update a sha... |
Title: Measuring anomalies in cigarette sales by using official data from Spanish provinces: Are there only the anomalies detected by the Empty Pack Surveys (EPS) used by Transnational Tobacco Companies (TTCs)? Abstract: There is literature that questions the veracity of the studies commissioned by the transnational to... |
Title: Exploring Customer Price Preference and Product Profit Role in Recommender Systems Abstract: Most of the research in the recommender systems domain is focused on the optimization of the metrics based on historical data such as Mean Average Precision (MAP) or Recall. However, there is a gap between the research a... |
Title: The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest Abstract: Most representative decision tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for... |
Title: Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4 Abstract: Modern high-scoring models of vision in the brain score competition do not stem from Vision Transformers. However, in this paper, we provide evidence against the unexpected ... |
Title: DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning Abstract: Offline reinforcement learning algorithms promise to be applicable in settings where a fixed dataset is available and no new experience can be acquired. However, such formulation is inevitably offline-data-hungry and, in practic... |
Title: FlexBlock: A Flexible DNN Training Accelerator with Multi-Mode Block Floating Point Support Abstract: Training deep neural networks (DNNs) is a computationally expensive job, which can take weeks or months even with high performance GPUs. As a remedy for this challenge, community has started exploring the use of... |
Title: Set-valued prediction in hierarchical classification with constrained representation complexity Abstract: Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of classes instead of a single clas... |
Title: Algebraic Learning: Towards Interpretable Information Modeling Abstract: Along with the proliferation of digital data collected using sensor technologies and a boost of computing power, Deep Learning (DL) based approaches have drawn enormous attention in the past decade due to their impressive performance in ext... |
Title: A Survey on Deep Graph Generation: Methods and Applications Abstract: Graphs are ubiquitous in encoding relational information of real-world objects in many domains. Graph generation, whose purpose is to generate new graphs from a distribution similar to the observed graphs, has received increasing attention tha... |
Title: Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs Abstract: We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few large convolutional kernels instead of a stac... |
Title: Private Non-Convex Federated Learning Without a Trusted Server Abstract: We study differentially private (DP) federated learning (FL) with non-convex loss functions and heterogeneous (non-i.i.d.) client data in the absence of a trusted server, both with and without a secure "shuffler" to anonymize client reports... |
Title: TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence Abstract: We present a self-supervised and self-calibrating multi-shot approach to imaging through atmospheric turbulence, called TurbuGAN. Our approach requires no paire... |
Title: Algorithmic Recourse in the Face of Noisy Human Responses Abstract: As machine learning (ML) models are increasingly being deployed in high-stakes applications, there has been growing interest in providing recourse to individuals adversely impacted by model predictions (e.g., an applicant whose loan has been den... |
Title: Adaptive Model Predictive Control by Learning Classifiers Abstract: Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the presence of environment disturbances. Despite the successes, it is st... |
Title: The Role of Local Steps in Local SGD Abstract: We consider the distributed stochastic optimization problem where $n$ agents want to minimize a global function given by the sum of agents' local functions, and focus on the heterogeneous setting when agents' local functions are defined over non-i.i.d. data sets. We... |
Title: MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks Abstract: In many personalized recommendation scenarios, the generalization ability of a target task can be improved via learning with additional auxiliary tasks alongside this target task on a multi-task networ... |
Title: Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits Abstract: An ideal strategy in zero-sum games should not only grant the player an average reward no less than the value of Nash equilibrium, but also exploit the (adaptive) opponents when they are suboptimal. While most... |
Title: Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions Abstract: Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy o... |
Title: CheckSel: Efficient and Accurate Data-valuation Through Online Checkpoint Selection Abstract: Data valuation and subset selection have emerged as valuable tools for application-specific selection of important training data. However, the efficiency-accuracy tradeoffs of state-of-the-art methods hinder their wides... |
Title: Semi-Discrete Normalizing Flows through Differentiable Tessellation Abstract: Mapping between discrete and continuous distributions is a difficult task and many have had to resort to approximate or heuristical approaches. We propose a tessellation-based approach that directly learns quantization boundaries on a ... |
Title: A Comparative Study on Forecasting of Retail Sales Abstract: Predicting product sales of large retail companies is a challenging task considering volatile nature of trends, seasonalities, events as well as unknown factors such as market competitions, change in customer's preferences, or unforeseen events, e.g., ... |
Title: Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions Abstract: We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over diff... |
Title: DIAS: A Domain-Independent Alife-Based Problem-Solving System Abstract: A domain-independent problem-solving system based on principles of Artificial Life is introduced. In this system, DIAS, the input and output dimensions of the domain are laid out in a spatial medium. A population of actors, each seeing only ... |
Title: Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective Abstract: One of the most fundamental questions in quantitative finance is the existence of continuous-time diffusion models that fit market prices of a given set of options. Traditionally, one employs a mix of intuition, ... |
Title: The Role of Interactivity in Structured Estimation Abstract: We study high-dimensional sparse estimation under three natural constraints: communication constraints, local privacy constraints, and linear measurements (compressive sensing). Without sparsity assumptions, it has been established that interactivity c... |
Title: Rethinking Stability for Attribution-based Explanations Abstract: As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are stable, e.g., robust to infinitesimal perturbations to an input. Howe... |
Title: Asymptotic Behavior of Bayesian Generalization Error in Multinomial Mixtures Abstract: Multinomial mixtures are widely used in the information engineering field, however, their mathematical properties are not yet clarified because they are singular learning models. In fact, the models are non-identifiable and th... |
Title: Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram Abstract: In recent years, self-supervised learning methods have shown significant improvement for pre-training with unlabeled data and have proven helpful for electrocardiogram signals. However, most previous pre-tr... |
Title: Attention based Memory video portrait matting Abstract: We proposed a novel trimap free video matting method based on the attention mechanism. By the nature of the problem, most existing approaches use either multiple computational expansive modules or complex algorithms to exploit temporal information fully. We... |
Title: Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition Abstract: Emotional recognition through exploring the electroencephalography (EEG) characteristics has been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding the complex dynamical phenomena a... |
Title: Communication-Efficient Federated Distillation with Active Data Sampling Abstract: Federated learning (FL) is a promising paradigm to enable privacy-preserving deep learning from distributed data. Most previous works are based on federated average (FedAvg), which, however, faces several critical issues, includin... |
Title: Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models Abstract: Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining separate instances for diffe... |
Title: Less is More: Proxy Datasets in NAS approaches Abstract: Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections betw... |
Title: Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records Abstract: Question Answering on Electronic Health Records (EHR-QA) has a significant impact on the healthcare domain, and it is being actively studied. Previous research on structured EHR-QA focuses on converting nat... |
Title: Towards Neural Sparse Linear Solvers Abstract: Large sparse symmetric linear systems appear in several branches of science and engineering thanks to the widespread use of the finite element method (FEM). The fastest sparse linear solvers available implement hybrid iterative methods. These methods are based on he... |
Title: Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey Abstract: Marine scientists use remote underwater video recording to survey fish species in their natural habitats. This helps them understand and predict how fish respond to climate change, habitat degradation, and fishin... |
Title: Forward Compatible Few-Shot Class-Incremental Learning Abstract: Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without forgetting old ones. This scenario becomes more challenging when new c... |
Title: Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction Abstract: This paper demonstrates how an efficient representation of the planned path using B-splines, and a construction procedure that takes advantage of the neural network's inductive bias, speed up ... |
Title: Solving parametric partial differential equations with deep rectified quadratic unit neural networks Abstract: Implementing deep neural networks for learning the solution maps of parametric partial differential equations (PDEs) turns out to be more efficient than using many conventional numerical methods. Howeve... |
Title: Neural Theorem Provers Delineating Search Area Using RNN Abstract: Although traditional symbolic reasoning methods are highly interpretable, their application in knowledge graphs link prediction has been limited due to their computational inefficiency. A new RNNNTP method is proposed in this paper, using a gener... |
Title: Identifying the root cause of cable network problems with machine learning Abstract: Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in the past was cumbersome and time-consuming. Even with machine learning due to the heterogene... |
Title: Supervised segmentation of NO2 plumes from individual ships using TROPOMI satellite data Abstract: Starting from 2021, the International Maritime Organization significantly tightened the $\text{NO}_\text{x}$ emission requirements for ships entering the Baltic and North Sea waters. Since all methods currently use... |
Title: Modelling Non-Smooth Signals with Complex Spectral Structure Abstract: The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a model for signals with complex spectral structure. A significant limitation of the GPCM is that it assumes a rapidly decaying spectrum: it can only model smooth signals. ... |
Title: A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources Abstract: Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impe... |
Title: Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks Abstract: We present a novel bi-modal system based on deep networks to address the problem of learning associations and simple meanings of objects depicted in "authored" images, such as fine art paintings and draw... |
Title: Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification Abstract: Adversarial attacks are often considered as threats to the robustness of Deep Neural Networks (DNNs). Various defending techniques have been developed to mitigate the potential negative impact of adversarial att... |
Title: SuperCone: Unified User Segmentation over Heterogeneous Experts via Concept Meta-learning Abstract: We study the problem of user segmentation: given a set of users and one or more predefined groups or segments, assign users to their corresponding segments. As an example, for a segment indicating particular inter... |
Title: Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science Abstract: Data science and machine learning provide indispensable techniques for understanding phenomena at scale, but the discretionary choices made when doing this work are often not recognized. Drawing from qualitative re... |
Title: Compressing CNN Kernels for Videos Using Tucker Decompositions: Towards Lightweight CNN Applications Abstract: Convolutional Neural Networks (CNN) are the state-of-the-art in the field of visual computing. However, a major problem with CNNs is the large number of floating point operations (FLOPs) required to per... |
Title: Teleconnection patterns of different El Ni\~no types revealed by climate network curvature Abstract: The diversity of El Ni\~no events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) types. While the remote impacts, i.e. teleconnections, of EP and CP events have b... |
Title: Ensemble plasticity and network adaptability in SNNs Abstract: Artificial Spiking Neural Networks (ASNNs) promise greater information processing efficiency because of discrete event-based (i.e., spike) computation. Several Machine Learning (ML) applications use biologically inspired plasticity mechanisms as unsu... |
Title: A New Learning Paradigm for Stochastic Configuration Network: SCN+ Abstract: Learning using privileged information (LUPI) paradigm, which pioneered teacher-student interaction mechanism, makes the learning models use additional information in training stage. This paper is the first to propose an incremental lear... |
Title: Neural Forecasting of the Italian Sovereign Bond Market with Economic News Abstract: In this paper we employ economic news within a neural network framework to forecast the Italian 10-year interest rate spread. We use a big, open-source, database known as Global Database of Events, Language and Tone to extract t... |
Title: A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning Abstract: The knowledge of the users' electricity consumption pattern is an important coordinating mechanism between the utility company and the electricity consumers in terms of key decision makings. The load de... |
Title: A Mixed Integer Programming Approach for Verifying Properties of Binarized Neural Networks Abstract: Many approaches for verifying input-output properties of neural networks have been proposed recently. However, existing algorithms do not scale well to large networks. Recent work in the field of model compressio... |
Title: Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case Abstract: The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Today, when it comes to renewable energy gene... |
Title: Web Mining to Inform Locations of Charging Stations for Electric Vehicles Abstract: The availability of charging stations is an important factor for promoting electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for city planners, the crucial question is where to place charging stations so ... |
Title: Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis Abstract: The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence. The development of the ABSA task is very much hinde... |
Title: The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications Abstract: We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL). Specifical... |
Title: Privatized Graph Federated Learning Abstract: Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi... |
Title: Modelling variability in vibration-based PBSHM via a generalised population form Abstract: Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen in the literature. However, SHM is still fa... |
Title: Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design Abstract: Real-world scientific or engineering applications often involve mathematical modeling of complex uncertain systems with a large number of unknown parameters. The complexity of such systems, and the eno... |
Title: Similarity-based prediction of Ejection Fraction in Heart Failure Patients Abstract: Biomedical research is increasingly employing real world evidence (RWE) to foster discoveries of novel clinical phenotypes and to better characterize long term effect of medical treatments. However, due to limitations inherent i... |
Title: On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes Abstract: Generative Adversarial Networks (GANs) learn an implicit generative model from data samples through a two-player game. In this paper, we study the existence of Nash equilibrium of the game which is consistent as the numbe... |
Title: Ethical and Fairness Implications of Model Multiplicity Abstract: While predictive models are a purely technological feat, they may operate in a social context in which benign engineering choices entail unexpected real-life consequences. Fairness -- pertaining both to individuals and groups -- is one of such con... |
Title: CAROL: Confidence-Aware Resilience Model for Edge Federations Abstract: In recent years, the deployment of large-scale Internet of Things (IoT) applications has given rise to edge federations that seamlessly interconnect and leverage resources from multiple edge service providers. The requirement of supporting b... |
Title: On the benefits of knowledge distillation for adversarial robustness Abstract: Knowledge distillation is normally used to compress a big network, or teacher, onto a smaller one, the student, by training it to match its outputs. Recently, some works have shown that robustness against adversarial attacks can also ... |
Title: Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media Abstract: The massive spread of visual content through the web and social media poses both challenges and opportunities. Tracking visually-similar content is an important task for studying and analyzing social phenomena ... |
Title: Orchestrated Value Mapping for Reinforcement Learning Abstract: We present a general convergent class of reinforcement learning algorithms that is founded on two distinct principles: (1) mapping value estimates to a different space using arbitrary functions from a broad class, and (2) linearly decomposing the re... |
Title: Optimal Correlated Equilibria in General-Sum Extensive-Form Games: Fixed-Parameter Algorithms, Hardness, and Two-Sided Column-Generation Abstract: We study the problem of finding optimal correlated equilibria of various sorts: normal-form coarse correlated equilibrium (NFCCE), extensive-form coarse correlated eq... |
Title: Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data Abstract: When dealing with binary classification of data with only one labeled class data scientists employ two main approaches, namely One-Class (OC) classification and Positive Unlabeled (PU) learning. The former only learns f... |
Title: Geometry of Data Abstract: Topological data analysis asks when balls in a metric space $(X,d)$ intersect. Geometric data analysis asks how much balls have to be enlarged to intersect. We connect this principle to the traditional core geometric concept of curvature. This enables us, on one hand, to reconceptualiz... |
Title: Physico-chemical properties extraction from the fluorescence spectrum with 1D-convolutional neural networks: application to olive oil Abstract: The olive oil sector produces a substantial impact in the Mediterranean's economy and lifestyle. Many studies exist which try to optimize the different steps in the oliv... |
Title: The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models Abstract: Transformer-based language models have become a key building block for natural language processing. While these models are extremely accurate, they can be too large and computationally intensive to run on sta... |
Title: Graph-Survival: A Survival Analysis Framework for Machine Learning on Temporal Networks Abstract: Continuous time temporal networks are attracting increasing attention due their omnipresence in real-world datasets and they manifold applications. While static network models have been successful in capturing stati... |
Title: FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems Abstract: Swarm intelligence is being increasingly deployed in autonomous systems, such as drones and unmanned vehicles. Federated reinforcement learning (FRL), a key swarm intelligence paradigm where agents interact w... |
Title: GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models Abstract: Providing natural language instructions in prompts is a useful new paradigm for improving task performance of large language models in a zero-shot setting. Recent work has aimed to improve such prompts via manual re... |
Title: InsetGAN for Full-Body Image Generation Abstract: While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities, hairstyles, clothing, and the variance in pose. Instead of modeling this complex ... |
Title: The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining Abstract: In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm named \textit{machine unlearning}, which enables data holders to proactively erase their data from a trained ... |
Title: Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation Abstract: We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallu... |
Title: Phenomenology of Double Descent in Finite-Width Neural Networks Abstract: `Double descent' delineates the generalization behaviour of models depending on the regime they belong to: under- or over-parameterized. The current theoretical understanding behind the occurrence of this phenomenon is primarily based on l... |
Title: Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies Abstract: Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decision-maker's ability to perform a task, e.g. to alert them to pote... |
Title: Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases Abstract: Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on dive... |
Title: A Supervised Learning Approach to Rankability Abstract: The rankability of data is a recently proposed problem that considers the ability of a dataset, represented as a graph, to produce a meaningful ranking of the items it contains. To study this concept, a number of rankability measures have recently been prop... |
Title: The Efficacy of Pessimism in Asynchronous Q-Learning Abstract: This paper is concerned with the asynchronous form of Q-learning, which applies a stochastic approximation scheme to Markovian data samples. Motivated by the recent advances in offline reinforcement learning, we develop an algorithmic framework that ... |
Title: Enhancing crowd flow prediction in various spatial and temporal granularities Abstract: Thanks to the diffusion of the Internet of Things, nowadays it is possible to sense human mobility almost in real time using unconventional methods (e.g., number of bikes in a bike station). Due to the diffusion of such techn... |
Title: Automated fault tree learning from continuous-valued sensor data: a case study on domestic heaters Abstract: Many industrial sectors have been collecting big sensor data. With recent technologies for processing big data, companies can exploit this for automatic failure detection and prevention. We propose the fi... |
Title: From Big to Small: Adaptive Learning to Partial-Set Domains Abstract: Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared across domains hinders appl... |
Title: Dawn of the transformer era in speech emotion recognition: closing the valence gap Abstract: Recent advances in transformer-based architectures which are pre-trained in self-supervised manner have shown great promise in several machine learning tasks. In the audio domain, such architectures have also been succes... |
Title: Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks Abstract: Given any deep fully connected neural network, initialized with random Gaussian parameters, we bound from above the quadratic Wasserstein distance between its output distribution and a suitable Gaussian process. Our explic... |
Title: Respecting causality is all you need for training physics-informed neural networks Abstract: While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhibits multi-scale, chaotic or turbulent b... |
Title: On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel Abstract: Deep Gaussian Process (DGP) as a model prior in Bayesian learning intuitively exploits the expressive power in function composition. DGPs also offer diverse modeling capabilities,... |
Title: Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning Abstract: We propose SwitchTT, a multi-task extension to Trajectory Transformer but enhanced with two striking features: (i) exploiting a sparsely activated model to reduce computation cost in multi-task o... |
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