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Title: Approximating Permutations with Neural Network Components for Travelling Photographer Problem Abstract: Most of the current inference techniques rely upon Bayesian inference on Probabilistic Graphical Models of observations and do predictions and classification on observations. However, there is very little lite... |
Title: PGD: A Large-scale Professional Go Dataset for Data-driven Analytics Abstract: Lee Sedol is on a winning streak--does this legend rise again after the competition with AlphaGo? Ke Jie is invincible in the world championship--can he still win the title this time? Go is one of the most popular board games in East ... |
Title: Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning Abstract: Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods... |
Title: Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound Abstract: State-of-the-art neural network verifiers are fundamentally based on one of two paradigms: either encoding the whole verification problem via tight multi-neuron convex relaxations or applying a Branch-and-Bound (BaB) procedure le... |
Title: Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data Abstract: Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic inf... |
Title: Understanding the Generalization Performance of Spectral Clustering Algorithms Abstract: The theoretical analysis of spectral clustering mainly focuses on consistency, while there is relatively little research on its generalization performance. In this paper, we study the excess risk bounds of the popular spectr... |
Title: Leveraging Emotion-specific Features to Improve Transformer Performance for Emotion Classification Abstract: This paper describes the approach to the Emotion Classification shared task held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on building models which can predict a multi-class emotio... |
Title: Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal Particles Abstract: We construct a reduced, data-driven, parameter dependent effective Stochastic Differential Equation (eSDE) for electric-field mediated colloidal crystallization using data obtained from Brownian Dynamics Simulations. We u... |
Title: TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning Abstract: We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle. We demonstrate the... |
Title: SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities Abstract: As deep learning advances, there is an ever-growing demand for models capable of synthesizing information from multi-modal resources to address the complex tasks raised from real-life applications. Recently... |
Title: Learning to Get Up Abstract: Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not resemble human get-up strategies, or are based on tracking recorded human get-up motions. In this paper, w... |
Title: FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings Abstract: Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmic biases in t... |
Title: Foundational Models for Continual Learning: An Empirical Study of Latent Replay Abstract: Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of downstream tasks and domains. Motivated by this, we study the efficacy of pre-trai... |
Title: Engineering flexible machine learning systems by traversing functionally invariant paths in weight space Abstract: Deep neural networks achieve human-like performance on a variety of perceptual and decision making tasks. However, deep networks perform poorly when confronted with changing tasks or goals, and broa... |
Title: End-to-End Signal Classification in Signed Cumulative Distribution Transform Space Abstract: This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport-based generative model to define the classification problem. We then make ... |
Title: Orthogonal Statistical Learning with Self-Concordant Loss Abstract: Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We establish non-asymptotic bounds on the excess risk of orthogonal stat... |
Title: Graph Anisotropic Diffusion Abstract: Traditional Graph Neural Networks (GNNs) rely on message passing, which amounts to permutation-invariant local aggregation of neighbour features. Such a process is isotropic and there is no notion of `direction' on the graph. We present a new GNN architecture called Graph An... |
Title: Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees Abstract: Influence estimation analyzes how changes to the training data can lead to different model predictions; this analysis can help us better understand these predictions, the models making those predictions, and the da... |
Title: Combined Learning of Neural Network Weights for Privacy in Collaborative Tasks Abstract: We introduce CoLN, Combined Learning of Neural network weights, a novel method to securely combine Machine Learning models over sensitive data with no sharing of data. With CoLN, local hosts use the same Neural Network archi... |
Title: A Simple Duality Proof for Wasserstein Distributionally Robust Optimization Abstract: We present a short and elementary proof of the duality for Wasserstein distributionally robust optimization, which holds for any arbitrary Kantorovich transport distance, any arbitrary measurable loss function, and any arbitrar... |
Title: Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF Abstract: The sharing of fake news and conspiracy theories on social media has wide-spread negative effects. By designing and applying different machine learning models, researchers have made progress in detecting fake news from text. However, e... |
Title: Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting Abstract: There exists a phenomenon that subjectivity highly lies in the daily evaluation process. Our research primarily concentrates on a multi-person evaluation system with anomaly detection to minimize the possible inaccuracy that su... |
Title: Neural Network Optimal Feedback Control with Guaranteed Local Stability Abstract: Recent research shows that deep learning can be an effective tool for designing optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of these neural network (NN) controllers is still not wel... |
Title: A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness Abstract: Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular appr... |
Title: Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions Abstract: In recent years, progress in NLU has been driven by benchmarks. These benchmarks are typically collected by crowdsourcing, where annotators write examples based on annotation instructions crafted by dataset creators. In this ... |
Title: Uniform Manifold Approximation with Two-phase Optimization Abstract: We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately. In UMATO, optimization is di... |
Title: TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources Abstract: Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of ... |
Title: Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation? Abstract: This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential ... |
Title: Adaptive Online Optimization with Predictions: Static and Dynamic Environments Abstract: In the past few years, Online Convex Optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this paper, we propose new step... |
Title: Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks Abstract: Despite being the main tool to visualize molecules at the atomic scale, AFM with CO-functionalized metal tips is unable to chemically identify the observed molecules. Here we present... |
Title: An Analysis of the Features Considerable for NFT Recommendations Abstract: This research explores the methods that NFTs can be recommended to people who interact with NFT-marketplaces to explore NFTs of preference and similarity to what they have been searching for. While exploring past methods that can be adopt... |
Title: Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation Abstract: Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs due to their non-differentiabili... |
Title: Reward Systems for Trustworthy Medical Federated Learning Abstract: Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these models is essential. Especially bias, defined as a disparity in the... |
Title: A Survey of Decentralized Online Learning Abstract: Decentralized online learning (DOL) has been increasingly researched in the last decade, mostly motivated by its wide applications in sensor networks, commercial buildings, robotics (e.g., decentralized target tracking and formation control), smart grids, deep ... |
Title: None Class Ranking Loss for Document-Level Relation Extraction Abstract: Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express an... |
Title: Ridgeless Regression with Random Features Abstract: Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization. In this paper, we investigate the statistical properties of ridgeless regression with random features and stoch... |
Title: Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs Abstract: Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models, both of which can be represented as factor graph grammars (FGGs), a powerful formalism capable of d... |
Title: On the speed of uniform convergence in Mercer's theorem Abstract: The classical Mercer's theorem claims that a continuous positive definite kernel $K({\mathbf x}, {\mathbf y})$ on a compact set can be represented as $\sum_{i=1}^\infty \lambda_i\phi_i({\mathbf x})\phi_i({\mathbf y})$ where $\{(\lambda_i,\phi_i)\}... |
Title: Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation Abstract: Machine learning models are commonly used to detect toxicity in online conversations. These models are trained on datasets annotated by human raters. We explore how raters' self-described identities impact how t... |
Title: Domain Adaptation meets Individual Fairness. And they get along Abstract: Many instances of algorithmic bias are caused by distributional shifts. For example, machine learning (ML) models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we leverage this con... |
Title: Preserve Pre-trained Knowledge: Transfer Learning With Self-Distillation For Action Recognition Abstract: Video-based action recognition is one of the most popular topics in computer vision. With recent advances of selfsupervised video representation learning approaches, action recognition usually follows a two-... |
Title: An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture Abstract: Early fault detection (EFD) of rotating machines is important to decrease the maintenance cost and improve the mechanical system stability. One of the key points of EFD is developing a gener... |
Title: Accurate non-stationary short-term traffic flow prediction method Abstract: Precise and timely traffic flow prediction plays a critical role in developing intelligent transportation systems and has attracted considerable attention in recent decades. Despite the significant progress in this area brought by deep l... |
Title: Deep Learning with Logical Constraints Abstract: In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the backgr... |
Title: Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection Abstract: While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as black box models they do not provide an easy means to unders... |
Title: Generalized Reference Kernel for One-class Classification Abstract: In this paper, we formulate a new generalized reference kernel hoping to improve the original base kernel using a set of reference vectors. Depending on the selected reference vectors, our formulation shows similarities to approximate kernels, r... |
Title: Can Information Behaviour Inform Machine Learning? Abstract: The objective of this paper is to explore the opportunities for human information behaviour research to inform and influence the field of machine learning and the resulting machine information behaviour. Using the development of foundation models in ma... |
Title: Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks Abstract: Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from access... |
Title: Experimental quantum pattern recognition in IBMQ and diamond NVs Abstract: One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noi... |
Title: Thermodynamically Consistent Machine-Learned Internal State Variable Approach for Data-Driven Modeling of Path-Dependent Materials Abstract: Characterization and modeling of path-dependent behaviors of complex materials by phenomenological models remains challenging due to difficulties in formulating mathematica... |
Title: Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning Abstract: Deep reinforcement learning (RL) is a data-driven method capable of discovering complex control strategies for high-dimensional systems, making it promising for flow control applications. I... |
Title: Using a novel fractional-order gradient method for CNN back-propagation Abstract: Computer-aided diagnosis tools have experienced rapid growth and development in recent years. Among all, deep learning is the most sophisticated and popular tool. In this paper, researchers propose a novel deep learning model and a... |
Title: Forecasting Market Changes using Variational Inference Abstract: Though various approaches have been considered, forecasting near-term market changes of equities and similar market data remains quite difficult. In this paper we introduce an approach to forecast near-term market changes for equity indices as well... |
Title: Physics-aware Reduced-order Modeling of Transonic Flow via $\beta$-Variational Autoencoder Abstract: Autoencoder-based reduced-order modeling (ROM) has recently attracted significant attention, owing to its ability to capture underlying nonlinear features. However, two critical drawbacks severely undermine its s... |
Title: LoopStack: a Lightweight Tensor Algebra Compiler Stack Abstract: We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack enables us to compile entire neural networks and generate code targetin... |
Title: Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization Abstract: Networks provide a powerful tool to model complex systems where the different entities in the system are presented by nodes and their interactions by edges. Recently, there has been a growing interest in ... |
Title: The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks Abstract: The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or eve... |
Title: Skeptical binary inferences in multi-label problems with sets of probabilities Abstract: In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributionally robust, we mean that we consider a set of... |
Title: Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study) Abstract: Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important technique to handle the typically very large test datasets efficiently, saving computation and labeling... |
Title: From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model Abstract: Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing methods on noisy label have focused ... |
Title: A Multi-stage deep architecture for summary generation of soccer videos Abstract: Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in the field of video analytics, due to the massive p... |
Title: Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN Abstract: Nosie is an important cause of low quality Optical coherence tomography (OCT) image. The neural network model based on Convolutional neural networks(CNNs) has demonstrated its excellent performance in image denoisi... |
Title: DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data Abstract: Gravitational lensing is the relativistic effect generated by massive bodies, which bend the space-time surrounding them. It is a deeply investigated topic in astrophysics and allows validating theoretical relativistic... |
Title: FedDKD: Federated Learning with Decentralized Knowledge Distillation Abstract: The performance of federated learning in neural networks is generally influenced by the heterogeneity of the data distribution. For a well-performing global model, taking a weighted average of the local models, as done by most existin... |
Title: Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor Abstract: In this paper, we investigate an online prediction strategy named as Discounted-Normal-Predictor (Kapralov and Panigrahy, 2010) for smoothed online convex optimization (SOCO), in which the learner needs to minimize not only the hi... |
Title: VICE: Variational Interpretable Concept Embeddings Abstract: A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding objec... |
Title: Data-driven emotional body language generation for social robotics Abstract: In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration, since humans attribute, and perhaps subconsciously anticipate, such traces to ... |
Title: Large Neighborhood Search based on Neural Construction Heuristics Abstract: We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem with time windows (VRPTW). Our method uses graph neural net... |
Title: BSRA: Block-based Super Resolution Accelerator with Hardware Efficient Pixel Attention Abstract: Increasingly, convolution neural network (CNN) based super resolution models have been proposed for better reconstruction results, but their large model size and complicated structure inhibit their real-time hardware... |
Title: Sparse Compressed Spiking Neural Network Accelerator for Object Detection Abstract: Spiking neural networks (SNNs), which are inspired by the human brain, have recently gained popularity due to their relatively simple and low-power hardware for transmitting binary spikes and highly sparse activation maps. Howeve... |
Title: Zebra: Memory Bandwidth Reduction for CNN Accelerators With Zero Block Regularization of Activation Maps Abstract: The large amount of memory bandwidth between local buffer and external DRAM has become the speedup bottleneck of CNN hardware accelerators, especially for activation maps. To reduce memory bandwidth... |
Title: Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs Abstract: Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. To address this problem, it has been recentl... |
Title: Deep-Attack over the Deep Reinforcement Learning Abstract: Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack. Some work tries to design an attack evalu... |
Title: Exploration in Deep Reinforcement Learning: A Survey Abstract: This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find th... |
Title: Online Learning in Fisher Markets with Unknown Agent Preferences Abstract: In a Fisher market, agents (users) spend a budget of (artificial) currency to buy goods that maximize their utilities, and producers set prices on capacity-constrained goods such that the market clears. The equilibrium prices in such a ma... |
Title: Gradient Descent, Stochastic Optimization, and Other Tales Abstract: The goal of this paper is to debunk and dispel the magic behind black-box optimizers and stochastic optimizers. It aims to build a solid foundation on how and why the techniques work. This manuscript crystallizes this knowledge by deriving from... |
Title: Predicting and Optimizing for Energy Efficient ACMV Systems: Computational Intelligence Approaches Abstract: In this study, a novel application of neural networks that predict thermal comfort states of occupants is proposed with accuracy over 95%, and two optimization algorithms are proposed and evaluated under ... |
Title: Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access Abstract: Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at th... |
Title: Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation Abstract: Convolutional neural network (CNN) based image enhancement methods such as super-resolution and detail enhancement have achieved remarkable performances. However, amounts of operations including ... |
Title: WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models Abstract: WeatherBench is a benchmark dataset for medium-range weather forecasting of geopotential, temperature and precipitation, consisting of preprocessed data, predefined ... |
Title: Family of Two Dimensional Transition Metal Dichlorides Fundamental Properties, Structural Defects, and Environmental Stability Abstract: A large number of novel two-dimensional (2D) materials are constantly discovered and deposed into the databases. Consolidate implementation of machine learning algorithms and d... |
Title: Modeling and mitigation of occupational safety risks in dynamic industrial environments Abstract: Identifying and mitigating safety risks is paramount in a number of industries. In addition to guidelines and best practices, many industries already have safety management systems (SMSs) designed to monitor and rei... |
Title: Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning Abstract: We propose a methodology at the nexus of operations research and machine learning (ML) leveraging generic approximators available from ML to accelerate the solution of mixed-integer linear two-stage stochastic programs. We aim ... |
Title: Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion Abstract: Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph co... |
Title: FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation Abstract: Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect. To achieve superior performance, the majority of existing GCL methods elabo... |
Title: Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters Abstract: Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversar... |
Title: Understanding CNNs from excitations Abstract: For instance-level explanation, in order to reveal the relations between high-level semantics and detailed spatial information, this paper proposes a novel cognitive approach to neural networks, which named PANE. Under the guidance of PANE, a novel saliency map repre... |
Title: CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning Abstract: In reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently been shown to remedy this via encoding invariances from raw pi... |
Title: BERTops: Studying BERT Representations under a Topological Lens Abstract: Proposing scoring functions to effectively understand, analyze and learn various properties of high dimensional hidden representations of large-scale transformer models like BERT can be a challenging task. In this work, we explore a new di... |
Title: Wireless LAN sensing with smart antennas Abstract: The paper targets the problem of human motion detection using Wireless Local Area Network devices (WiFi) equipped with pattern reconfigurable antennas. Motion sensing is obtained by monitoring the body-induced alterations of the ambient WiFi signals originated f... |
Title: Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction Abstract: Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin p... |
Title: A Sharp Memory-Regret Trade-Off for Multi-Pass Streaming Bandits Abstract: The stochastic $K$-armed bandit problem has been studied extensively due to its applications in various domains ranging from online advertising to clinical trials. In practice however, the number of arms can be very large resulting in lar... |
Title: A walk through of time series analysis on quantum computers Abstract: Because of the rotational components on quantum circuits, some quantum neural networks based on variational circuits can be considered equivalent to the classical Fourier networks. In addition, they can be used to predict Fourier coefficients ... |
Title: GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition Abstract: Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a graph-based multi-task self-super... |
Title: Data Justice in Practice: A Guide for Developers Abstract: The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, govern... |
Title: A Survey on Uncertainty Toolkits for Deep Learning Abstract: The success of deep learning (DL) fostered the creation of unifying frameworks such as tensorflow or pytorch as much as it was driven by their creation in return. Having common building blocks facilitates the exchange of, e.g., models or concepts and m... |
Title: RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks Abstract: Learning solutions of partial differential equations (PDEs) with Physics-Informed Neural Networks (PINNs) is an attractive alternative approach to traditional solvers due to its flexibility and ease of incorpora... |
Title: Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes Abstract: Our work aims at developing reinforcement learning algorithms that do not rely on the Markov assumption. We consider the class of Non-Markov Decision Processes where histories can be abstracted into a finite set of stat... |
Title: A Change Dynamic Model for the Online Detection of Gradual Change Abstract: Changes in the statistical properties of a stochastic process are typically assumed to occur via change-points, which demark instantaneous moments of complete and total change in process behavior. In cases where these transitions occur g... |
Title: Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset Abstract: Delirium occurs in about 80% cases in the Intensive Care Unit (ICU) and is associated with a longer hospital stay, increased mortality and other related issues. Delirium does not have any bioma... |
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