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Title: Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement Abstract: Cycle reconstruction regularized adversarial training -- e.g., CycleGAN, DiscoGAN, and DualGAN -- has been widely used for image style transfer with unpaired training data. Several recent works, however, have shown that... |
Title: GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection Abstract: Social media in present times has a significant and growing influence. Fake news being spread on these platforms have a disruptive and damaging impact on our lives. Furthermore, as multimedia content improves the visibilit... |
Title: Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection Abstract: Program representation, which aims at converting program source code into vectors with automatically extracted features, is a fundamental problem in programming language processing (PLP). Rece... |
Title: Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity Abstract: Interpretable machine learning has demonstrated impressive performance while preserving explainability. In particular, neural additive models (NAM) offer the interpretability to the black-box deep learni... |
Title: Learn From the Past: Experience Ensemble Knowledge Distillation Abstract: Traditional knowledge distillation transfers "dark knowledge" of a pre-trained teacher network to a student network, and ignores the knowledge in the training process of the teacher, which we call teacher's experience. However, in realisti... |
Title: Consolidated Adaptive T-soft Update for Deep Reinforcement Learning Abstract: Demand for deep reinforcement learning (DRL) is gradually increased to enable robots to perform complex tasks, while DRL is known to be unstable. As a technique to stabilize its learning, a target network that slowly and asymptotically... |
Title: A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation Abstract: Proactive evacuation traffic management largely depends on real-time monitoring and prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic prediction is challenging due ... |
Title: On the Effectiveness of Dataset Watermarking in Adversarial Settings Abstract: In a data-driven world, datasets constitute a significant economic value. Dataset owners who spend time and money to collect and curate the data are incentivized to ensure that their datasets are not used in ways that they did not aut... |
Title: Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision Abstract: Learning useful node and graph representations with graph neural networks (GNNs) is a challenging task. It is known that deep GNNs suffer from over-smoothing where, as the number of layers increases, node representations become nea... |
Title: MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest Abstract: The broad adoption of Machine Learning (ML) in security-critical fields demands the explainability of the approach. However, the research on understanding ML models, such as Random Forest (RF), is still in its infant s... |
Title: Improved Dual Correlation Reduction Network Abstract: Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task. However, we observed that the existing methods suffer from the representa... |
Title: An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data Abstract: Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Traditional machine learning algorithms have been eminently useful in crafting prognostic m... |
Title: Reachability analysis in stochastic directed graphs by reinforcement learning Abstract: We characterize the reachability probabilities in stochastic directed graphs by means of reinforcement learning methods. In particular, we show that the dynamics of the transition probabilities in a stochastic digraph can be ... |
Title: Raman Spectrum Matching with Contrastive Representation Learning Abstract: Raman spectroscopy is an effective, low-cost, non-intrusive technique often used for chemical identification. Typical approaches are based on matching observations to a reference database, which requires careful preprocessing, or supervis... |
Title: 6D Rotation Representation For Unconstrained Head Pose Estimation Abstract: In this paper, we present a method for unconstrained end-to-end head pose estimation. We address the problem of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data and propose a continuous 6D ... |
Title: Towards Learning Causal Representations from Multi-Instance Bags Abstract: Multi-instance learning (MIL) deals with objects represented as bags of instances and can predict instance labels from bag-level supervision. However, significant performance gaps exist between instance-level MIL algorithms and supervised... |
Title: NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs Abstract: NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three different series of Knowledge Graph Embedding (KGE) methods, including conventional KGEs,... |
Title: Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression Abstract: We consider learning a trading agent acting on behalf of the treasury of a firm earning revenue in a foreign currency (FC) and incurring expenses in the home currency (HC). The goal of the agent is to maximize the expected HC a... |
Title: Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting Abstract: Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing. Recently, Graph Neural Network truly... |
Title: Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs Abstract: This paper presents a solution to a predict then optimise problem which goal is to reduce the electricity cost of a university campus. The proposed methodology combines a multi-dimensional time... |
Title: Context-Hierarchy Inverse Reinforcement Learning Abstract: An inverse reinforcement learning (IRL) agent learns to act intelligently by observing expert demonstrations and learning the expert's underlying reward function. Although learning the reward functions from demonstrations has achieved great success in va... |
Title: Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation Abstract: Objective. Deep neural networks (DNNs) have shown unprecedented success in various brain-machine interface applications such as epileptic seizure prediction. However, existing approaches typi... |
Title: Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screening Abstract: The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly reduce the impact of this disease. However, current state-of-the-art ... |
Title: Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains Abstract: Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks. But current MTGPs are usually limited to the multi-task scenario defin... |
Title: Do autoencoders need a bottleneck for anomaly detection? Abstract: A common belief in designing deep autoencoders (AEs), a type of unsupervised neural network, is that a bottleneck is required to prevent learning the identity function. Learning the identity function renders the AEs useless for anomaly detection.... |
Title: Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection Abstract: Despite numerous studies of deep autoencoders (AEs) for unsupervised anomaly detection, AEs still lack a way to express uncertainty in their predictions, crucial for ensuring safe and trustworthy machine learni... |
Title: Machine Learning based refinement strategies for polyhedral grids with applications to Virtual Element and polyhedral Discontinuous Galerkin methods Abstract: We propose two new strategies based on Machine Learning techniques to handle polyhedral grid refinement, to be possibly employed within an adaptive framew... |
Title: PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine Abstract: Machine learning algorithms must be able to efficiently cope with massive data sets. Therefore, they have to scale well on any modern system and be able to exploit the computing power of accelerators independent of their vendor. I... |
Title: Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain Abstract: In this article, we first give an introduction to artificial intelligence and its applications in biology and medicine in Section 1. Deep learning methods are then described in Section 2. We nar... |
Title: Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems Abstract: We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99\% training process reduct... |
Title: Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and health Abstract: Background: An advantageous property of behavioural signals ,e.g. handwriting, in contrast to morphological ones, such as iris, fingerprint, hand geometry, etc., is the possibility to ... |
Title: The effect of fatigue on the performance of online writer recognition Abstract: Background: The performance of biometric modalities based on things done by the subject, like signature and text-based recognition, may be affected by the subject state. Fatigue is one of the conditions that can significantly affect ... |
Title: Novel techniques for improvement the NNetEn entropy calculation for short and noisy time series Abstract: Entropy is a fundamental concept of information theory. It is widely used in the analysis of analog and digital signals. Conventional entropy measures have drawbacks, such as sensitivity to the length and am... |
Title: State-of-the-art in speaker recognition Abstract: Recent advances in speech technologies have produced new tools that can be used to improve the performance and flexibility of speaker recognition While there are few degrees of freedom or alternative methods when using fingerprint or iris identification technique... |
Title: Benchmarking Generative Latent Variable Models for Speech Abstract: Stochastic latent variable models (LVMs) achieve state-of-the-art performance on natural image generation but are still inferior to deterministic models on speech. In this paper, we develop a speech benchmark of popular temporal LVMs and compare... |
Title: HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction Abstract: With the development of temporal networks such as E-commerce networks and social networks, the issue of temporal link prediction has attracted increasing attention in recent years. The Temp... |
Title: Early Disease Stage Characterization in Parkinson's Disease from Resting-state fMRI Data Using a Long Short-term Memory Network Abstract: Parkinson's disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to cl... |
Title: Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection Abstract: Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those forecasts (prediction intervals). One exam... |
Title: Learning Relative Return Policies With Upside-Down Reinforcement Learning Abstract: Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems. Recent work in this area has largely focused on learning command-conditioned policies. We investigate the pote... |
Title: FedCAT: Towards Accurate Federated Learning via Device Concatenation Abstract: As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy. However, for non-IID scenarios, the clas... |
Title: Data refinement for fully unsupervised visual inspection using pre-trained networks Abstract: Anomaly detection has recently seen great progress in the field of visual inspection. More specifically, the use of classical outlier detection techniques on features extracted by deep pre-trained neural networks have b... |
Title: Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health Abstract: Background- This paper summarizes the state-of-the-art and applications based on online handwritting signals with special emphasis on e-security and e-health fields. Methods- In particular, we focus on the main achievemen... |
Title: Towards Safe, Real-Time Systems: Stereo vs Images and LiDAR for 3D Object Detection Abstract: As object detectors rapidly improve, attention has expanded past image-only networks to include a range of 3D and multimodal frameworks, especially ones that incorporate LiDAR. However, due to cost, logistics, and even ... |
Title: Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice Abstract: One of the main tasks of actuaries and data scientists is to build good predictive models for certain phenomena such as the claim size or the number of claims in insurance... |
Title: Behaviorally Grounded Model-Based and Model Free Cost Reduction in a Simulated Multi-Echelon Supply Chain Abstract: Amplification and phase shift in ordering signals, commonly referred to as bullwhip, are responsible for both excessive strain on real world inventory management systems, stock outs, and unnecessar... |
Title: Towards Optimal Lower Bounds for k-median and k-means Coresets Abstract: Given a set of points in a metric space, the $(k,z)$-clustering problem consists of finding a set of $k$ points called centers, such that the sum of distances raised to the power of $z$ of every data point to its closest center is minimized... |
Title: Equilibrium Aggregation: Encoding Sets via Optimization Abstract: Processing sets or other unordered, potentially variable-sized inputs in neural networks is usually handled by \emph{aggregating} a number of input tensors into a single representation. While a number of aggregation methods already exist from simp... |
Title: Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach Abstract: Dynamic mechanism design studies how mechanism designers should allocate resources among agents in a time-varying environment. We consider the problem where the agents interact with the mechanism designer according t... |
Title: High-Dimensional Sparse Bayesian Learning without Covariance Matrices Abstract: Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem. However, the most popular inference algorithms for SBL become too expensive for high-dimensional settings, due to the need to store and co... |
Title: Improving generalization with synthetic training data for deep learning based quality inspection Abstract: Automating quality inspection with computer vision techniques is often a very data-demanding task. Specifically, supervised deep learning requires a large amount of annotated images for training. In practic... |
Title: Gen\'eLive! Generating Rhythm Actions in Love Live! Abstract: A rhythm action game is a music-based video game in which the player is challenged to issue commands at the right timings during a music session. The timings are rendered in the chart, which consists of visual symbols, called notes, flying through the... |
Title: A Systematic Literature Review about Idea Mining: The Use of Machine-driven Analytics to Generate Ideas Abstract: Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unp... |
Title: Convergence of a New Learning Algorithm Abstract: A new learning algorithm proposed by Brandt and Lin for neural network [1], [2] has been shown to be mathematically equivalent to the conventional back-propagation learning algorithm, but has several advantages over the backpropagation algorithm, including feedba... |
Title: Benchmark Assessment for DeepSpeed Optimization Library Abstract: Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance metrics. The size of such datasets and the complexity of DL models cause s... |
Title: Dynamic Regret of Online Mirror Descent for Relatively Smooth Convex Cost Functions Abstract: The performance of online convex optimization algorithms in a dynamic environment is often expressed in terms of the dynamic regret, which measures the decision maker's performance against a sequence of time-varying com... |
Title: An initial alignment between neural network and target is needed for gradient descent to learn Abstract: This paper introduces the notion of "Initial Alignment" (INAL) between a neural network at initialization and a target function. It is proved that if a network and target function do not have a noticeable INA... |
Title: Building a 3-Player Mahjong AI using Deep Reinforcement Learning Abstract: Mahjong is a popular multi-player imperfect-information game developed in China in the late 19th-century, with some very challenging features for AI research. Sanma, being a 3-player variant of the Japanese Riichi Mahjong, possesses uniqu... |
Title: ARIA: Adversarially Robust Image Attribution for Content Provenance Abstract: Image attribution -- matching an image back to a trusted source -- is an emerging tool in the fight against online misinformation. Deep visual fingerprinting models have recently been explored for this purpose. However, they are not ro... |
Title: Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes Abstract: The simultaneous stochastic optimization of mining complexes (SSOMC) is a large-scale stochastic combinatorial optimization problem that simultaneously manages the extraction of materials from multiple mine... |
Title: AutoFR: Automated Filter Rule Generation for Adblocking Abstract: Adblocking relies on filter lists, which are manually curated and maintained by a small community of filter list authors. This manual process is laborious and does not scale well to a large number of sites and over time. We introduce AutoFR, a rei... |
Title: DataLab: A Platform for Data Analysis and Intervention Abstract: Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data. In this paper, we propose DataLab, a unified data-oriented platform... |
Title: Learning to Identify Perceptual Bugs in 3D Video Games Abstract: Automated Bug Detection (ABD) in video games is composed of two distinct but complementary problems: automated game exploration and bug identification. Automated game exploration has received much recent attention, spurred on by developments in fie... |
Title: Biological error correction codes generate fault-tolerant neural networks Abstract: It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error corr... |
Title: Meta-Learning for Simple Regret Minimization Abstract: We develop a meta-learning framework for simple regret minimization in bandits. In this framework, a learning agent interacts with a sequence of bandit tasks, which are sampled i.i.d.\ from an unknown prior distribution, and learns its meta-parameters to per... |
Title: Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects Abstract: Estimating heterogeneous treatment effects is an important problem across many domains. In order to accurately estimate such treatment effects, one typically relies on data from observational studies or randomize... |
Title: Incremental Inference on Higher-Order Probabilistic Graphical Models Applied to Constraint Satisfaction Problems Abstract: Probabilistic graphical models (PGMs) are tools for solving complex probabilistic relationships. However, suboptimal PGM structures are primarily used in practice. This dissertation presents... |
Title: Refining Self-Supervised Learning in Imaging: Beyond Linear Metric Abstract: We introduce in this paper a new statistical perspective, exploiting the Jaccard similarity metric, as a measure-based metric to effectively invoke non-linear features in the loss of self-supervised contrastive learning. Specifically, o... |
Title: A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling Abstract: As a counterpoint to classical stochastic particle methods for linear diffusion equations, we develop a deterministic particle method for the weighted porous medium equation (WPME) and prove its convergence ... |
Title: OptGAN: Optimizing and Interpreting the Latent Space of the Conditional Text-to-Image GANs Abstract: Text-to-image generation intends to automatically produce a photo-realistic image, conditioned on a textual description. It can be potentially employed in the field of art creation, data augmentation, photo-editi... |
Title: Modulation and signal class labelling using active learning and classification using machine learning Abstract: Supervised learning in machine learning (ML) requires labelled data set. Further real-time data classification requires an easily available methodology for labelling. Wireless modulation and signal cla... |
Title: Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations Abstract: End-to-end learning of dynamical systems with black-box models, such as neural ordinary differential equations (ODEs), provides a flexible framework for learning dynamics from data without prescribing a mathematical mo... |
Title: Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild Abstract: Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since the stress annotation usually relies on self-reports during t... |
Title: Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals Abstract: While Parkinson's disease (PD) is typically characterized by motor disorder, there is evidence of diminished emotion perception in PD patients. This study examines the utility of affective Electroencephalography (... |
Title: An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload Abstract: Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. Thi... |
Title: Assessing the State of Self-Supervised Human Activity Recognition using Wearables Abstract: The emergence of self-supervised learning in the field of wearables-based human activity recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the field, namely to exploit unlabeled data ... |
Title: Digital Signal Analysis based on Convolutional Neural Networks for Active Target Time Projection Chambers Abstract: An algorithm for digital signal analysis using convolutional neural networks (CNN) was developed in this work. The main objective of this algorithm is to make the analysis of experiments with activ... |
Title: Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization Abstract: Cardiovascular disease (CVDs) is one of the universal deadly diseases, and the detection of it in the early stage is a challenging task to tackle. Recently, deep learning and convolutional ... |
Title: DAGAM: A Domain Adversarial Graph Attention Model for Subject Independent EEG-Based Emotion Recognition Abstract: One of the most significant challenges of EEG-based emotion recognition is the cross-subject EEG variations, leading to poor performance and generalizability. This paper proposes a novel EEG-based em... |
Title: Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition Abstract: As a fundamental problem in ubiquitous computing and machine learning, sensor-based human activity recognition (HAR) has drawn extensive attention and made great progress in recent years. HAR aims to recognize human activities ba... |
Title: 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets Abstract: Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because current meth... |
Title: Off-Policy Evaluation with Policy-Dependent Optimization Response Abstract: The intersection of causal inference and machine learning for decision-making is rapidly expanding, but the default decision criterion remains an \textit{average} of individual causal outcomes across a population. In practice, various op... |
Title: Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates Abstract: Environments with sparse rewards and long horizons pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging control tasks is that th... |
Title: Does Label Differential Privacy Prevent Label Inference Attacks? Abstract: Label differential privacy (LDP) is a popular framework for training private ML models on datasets with public features and sensitive private labels. Despite its rigorous privacy guarantee, it has been observed that in practice LDP does n... |
Title: FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment Abstract: We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces without requiring training on those faces. We derive a novel i... |
Title: Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference Abstract: Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensionality reduction, extending classical Gaussian processes to an unsupervised learning context. The Bayesian ... |
Title: Projective Ranking-based GNN Evasion Attacks Abstract: Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial attacks. Two primary limitations of the current evasion attack methods are highlighted: (1) The current GradArgmax ignores the "lo... |
Title: Near Optimal Reconstruction of Spherical Harmonic Expansions Abstract: We propose an algorithm for robust recovery of the spherical harmonic expansion of functions defined on the d-dimensional unit sphere $\mathbb{S}^{d-1}$ using a near-optimal number of function evaluations. We show that for any $f \in L^2(\mat... |
Title: Self-Supervised and Interpretable Anomaly Detection using Network Transformers Abstract: Monitoring traffic in computer networks is one of the core approaches for defending critical infrastructure against cyber attacks. Machine Learning (ML) and Deep Neural Networks (DNNs) have been proposed in the past as a too... |
Title: Integrated multimodal artificial intelligence framework for healthcare applications Abstract: Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to... |
Title: Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms Abstract: We study a sequential decision problem where the learner faces a sequence of $K$-armed stochastic bandit tasks. The tasks may be designed by an adversary, but the adversary is constrained to choose the optimal arm of each task in... |
Title: Sign and Basis Invariant Networks for Spectral Graph Representation Learning Abstract: We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if $v$ is an eigenvector then so is $-v$; and (ii) more general basis symm... |
Title: Generalized Label Shift Correction via Minimum Uncertainty Principle: Theory and Algorithm Abstract: As a fundamental problem in machine learning, dataset shift induces a paradigm to learn and transfer knowledge under changing environment. Previous methods assume the changes are induced by covariate, which is le... |
Title: A Scalable Graph-Theoretic Distributed Framework for Cooperative Multi-Agent Reinforcement Learning Abstract: The main challenge of large-scale cooperative multi-agent reinforcement learning (MARL) is two-fold: (i) the RL algorithm is desired to be distributed due to limited resource for each individual agent; (... |
Title: Missing Value Knockoffs Abstract: One limitation of the most statistical/machine learning-based variable selection approaches is their inability to control the false selections. A recently introduced framework, model-x knockoffs, provides that to a wide range of models but lacks support for datasets with missing... |
Title: Automated Identification of Toxic Code Reviews: How Far Can We Go? Abstract: Toxic conversations during software development interactions may have serious repercussions on a Free and Open Source Software (FOSS) development project. For example, victims of toxic conversations may become afraid to express themselv... |
Title: Initialization of Latent Space Coordinates via Random Linear Projections for Learning Robotic Sensory-Motor Sequences Abstract: Robot kinematics data, despite being a high dimensional process, is highly correlated, especially when considering motions grouped in certain primitives. These almost linear correlation... |
Title: Variational Inference with Gaussian Mixture by Entropy Approximation Abstract: Variational inference is a technique for approximating intractable posterior distributions in order to quantify the uncertainty of machine learning. Although the unimodal Gaussian distribution is usually chosen as a parametric distrib... |
Title: Graph Attention Retrospective Abstract: Graph-based learning is a rapidly growing sub-field of machine learning with applications in social networks, citation networks, and bioinformatics. One of the most popular type of models is graph attention networks. These models were introduced to allow a node to aggregat... |
Title: Adversarial Contrastive Self-Supervised Learning Abstract: Recently, learning from vast unlabeled data, especially self-supervised learning, has been emerging and attracted widespread attention. Self-supervised learning followed by the supervised fine-tuning on a few labeled examples can significantly improve la... |
Title: Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations Abstract: While end-to-end training of Deep Neural Networks (DNNs) yields state of the art performance in an increasing array of applications, it does not provide insight into, or control over, the features being extracted. We report here on a p... |
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