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Title: DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment Abstract: Proteins interact to form complexes to carry out essential biological functions. Computational methods have been developed to predict the structures of protein complexes. However, an important challenge in protein complex structu... |
Title: User-Interactive Offline Reinforcement Learning Abstract: Offline reinforcement learning algorithms still lack trust in practice due to the risk that the learned policy performs worse than the original policy that generated the dataset or behaves in an unexpected way that is unfamiliar to the user. At the same t... |
Title: Symmetry Teleportation for Accelerated Optimization Abstract: Existing gradient-based optimization methods update the parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that allows the parameters to travel a large distance on the loss level... |
Title: Transformer-based out-of-distribution detection for clinically safe segmentation Abstract: In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular appro... |
Title: Tensor Shape Search for Optimum Data Compression Abstract: Various tensor decomposition methods have been proposed for data compression. In real world applications of the tensor decomposition, selecting the tensor shape for the given data poses a challenge and the shape of the tensor may affect the error and the... |
Title: Are Graph Neural Networks Really Helpful for Knowledge Graph Completion? Abstract: Knowledge graphs (KGs) facilitate a wide variety of applications due to their ability to store relational knowledge applicable to many areas. Despite great efforts invested in creation and maintenance, even the largest KGs are far... |
Title: Equivariant Mesh Attention Networks Abstract: Equivariance to symmetries has proven to be a powerful inductive bias in deep learning research. Recent works on mesh processing have concentrated on various kinds of natural symmetries, including translations, rotations, scaling, node permutations, and gauge transfo... |
Title: Temporal Domain Generalization with Drift-Aware Dynamic Neural Network Abstract: Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the chan... |
Title: MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest Abstract: Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. These embeddings can then be used for several tasks such as recommendation an... |
Title: Individual Topology Structure of Eye Movement Trajectories Abstract: Traditionally, extracting patterns from eye movement data relies on statistics of different macro-events such as fixations and saccades. This requires an additional preprocessing step to separate the eye movement subtypes, often with a number o... |
Title: Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations Abstract: This paper suggests a direction of coreference resolution for online decoding on actively generated input such as dialogue, where the model accepts an utterance and its past context, then finds m... |
Title: Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets Abstract: We present a family $\{\hat{\pi}\}_{p\ge 1}$ of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell_p$ norms, where $\hat{\pi}_2$ correspon... |
Title: NS3: Neuro-Symbolic Semantic Code Search Abstract: Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to str... |
Title: On the problem of entity matching and its application in automated settlement of receivables Abstract: This paper covers automated settlement of receivables in non-governmental organizations. We tackle the problem with entity matching techniques. We consider setup, where base algorithm is used for preliminary ra... |
Title: A Novel Markov Model for Near-Term Railway Delay Prediction Abstract: Predicting the near-future delay with accuracy for trains is momentous for railway operations and passengers' traveling experience. This work aims to design prediction models for train delays based on Netherlands Railway data. We first develop... |
Title: Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy Abstract: Large convolutional neural networks (CNN) can be difficult to train in the differentially private (DP) regime, since the optimization algorithms require a computationally expensive operation, known as the p... |
Title: Diversity Preference-Aware Link Recommendation for Online Social Networks Abstract: Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recomme... |
Title: Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection Abstract: Advance in medical imaging is an important part in deep learning research. One of the goals of computer vision is development of a holistic, comprehensive model which can identify tumors from histol... |
Title: All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs Abstract: Integrated Development Environments (IDE) are designed to make users more productive, as well as to make their work more comfortable. To achieve this, a lot of diverse tools are embedded into IDEs, and the develop... |
Title: The Selectively Adaptive Lasso Abstract: Machine learning regression methods allow estimation of functions without unrealistic parametric assumptions. Although they can perform exceptionally in prediction error, most lack theoretical convergence rates necessary for semi-parametric efficient estimation (e.g. TMLE... |
Title: Active Source Free Domain Adaptation Abstract: Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces an effect bottleneck due to the absence of source data and target supervised information, a... |
Title: Policy-based Primal-Dual Methods for Convex Constrained Markov Decision Processes Abstract: We study convex Constrained Markov Decision Processes (CMDPs) in which the objective is concave and the constraints are convex in the state-action visitation distribution. We propose a policy-based primal-dual algorithm t... |
Title: TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks Abstract: Foodborne illness is a serious but preventable public health problem -- with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public safety hazards, and even loss of life. While... |
Title: Neural Lyapunov Differentiable Predictive Control Abstract: We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees. The neural Lyapunov differentiable predictive control (NLDPC) learns the policy by construc... |
Title: Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path Abstract: We revisit the incremental autonomous exploration problem proposed by Lim & Auer (2012). In this setting, the agent aims to learn a set of near-optimal goal-conditioned policies to reach the $L$-controllable stat... |
Title: Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee Abstract: Conformal prediction aims to determine precise levels of confidence in predictions for new objects using past experience. However, the commonly used exchangeable assumptions between the training data and testing data limit its usage... |
Title: GraB: Finding Provably Better Data Permutations than Random Reshuffling Abstract: Random reshuffling, which randomly permutes the dataset each epoch, is widely adopted in model training because it yields faster convergence than with-replacement sampling. Recent studies indicate greedily chosen data orderings can... |
Title: Should Models Be Accurate? Abstract: Model-based Reinforcement Learning (MBRL) holds promise for data-efficiency by planning with model-generated experience in addition to learning with experience from the environment. However, in complex or changing environments, models in MBRL will inevitably be imperfect, and... |
Title: Offline Policy Comparison with Confidence: Benchmarks and Baselines Abstract: Decision makers often wish to use offline historical data to compare sequential-action policies at various world states. Importantly, computational tools should produce confidence values for such offline policy comparison (OPC) to acco... |
Title: Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data? Abstract: Foreign Exchange (FOREX) is a decentralised global market for exchanging currencies. The Forex market is enormous, and it operates 24 hours a day. Along with country-specific factors, Forex trading i... |
Title: All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass Abstract: Multi-Task Learning (MTL) models have shown their robustness, effectiveness, and efficiency for transferring learned knowledge across tasks. In real industrial applications such as web content classif... |
Title: Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications Abstract: In the intelligent communication field, deep learning (DL) has attracted much attention due to its strong fitting ability and data-driven learning capability. Compared with the typical DL feedforward network structures, an enha... |
Title: Covariance Matrix Adaptation MAP-Annealing Abstract: Single-objective optimization algorithms search for the single highest-quality solution with respect to an objective. In contrast, quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection... |
Title: Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling Abstract: Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses.... |
Title: Residual Channel Attention Network for Brain Glioma Segmentation Abstract: A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of interclass ambiguities in tumor regions. Recently, deep learning appr... |
Title: Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities Abstract: In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling m... |
Title: CNNs are Myopic Abstract: We claim that Convolutional Neural Networks (CNNs) learn to classify images using only small seemingly unrecognizable tiles. We show experimentally that CNNs trained only using such tiles can match or even surpass the performance of CNNs trained on full images. Conversely, CNNs trained ... |
Title: How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts Abstract: Neural Machine Translation systems built on top of Transformer-based architectures are routinely improving the state-of-the-art in translation quality according... |
Title: A Deep Gradient Correction Method for Iteratively Solving Linear Systems Abstract: We present a novel deep learning approach to approximate the solution of large, sparse, symmetric, positive-definite linear systems of equations. These systems arise from many problems in applied science, e.g., in numerical method... |
Title: Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication Abstract: Intent-based networks that integrate sophisticated machine reasoning technologies will be a cornerstone of future wireless 6G systems. Intent-based communication requires the network to consider the semantics (meanings)... |
Title: Fast Instrument Learning with Faster Rates Abstract: We investigate nonlinear instrumental variable (IV) regression given high-dimensional instruments. We propose a simple algorithm which combines kernelized IV methods and an arbitrary, adaptive regression algorithm, accessed as a black box. Our algorithm enjoys... |
Title: A Domain-adaptive Pre-training Approach for Language Bias Detection in News Abstract: Media bias is a multi-faceted construct influencing individual behavior and collective decision-making. Slanted news reporting is the result of one-sided and polarized writing which can occur in various forms. In this work, we ... |
Title: Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies Abstract: Sleep posture is linked to several health conditions such as nocturnal cramps and more serious musculoskeletal issues. However, in-clinic sleep assessments are often limited to vital signs (e... |
Title: Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems Abstract: In grant-free sparse code multiple access system, joint optimization of contention resources for users and active user detection (AUD) at the receiver is a complex combinatorial problem. To this end, we... |
Title: A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning Abstract: While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interference when faced with lifelong... |
Title: PAC-Wrap: Semi-Supervised PAC Anomaly Detection Abstract: Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefit from provable bounds on various errors in anomaly detection. To achieve t... |
Title: Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner Abstract: Inverse reinforcement learning (IRL) deals with estimating an agent's utility function from its actions. In this paper, we consider how an agent can hide its strategy and mitigate an adversari... |
Title: GraphMAE: Self-Supervised Masked Graph Autoencoders Abstract: Self-supervised learning (SSL) has been extensively explored in recent years. Particularly, generative SSL has seen emerging success in natural language processing and other fields, such as the wide adoption of BERT and GPT. Despite this, contrastive ... |
Title: Deep Learning-Based Synchronization for Uplink NB-IoT Abstract: We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT). The i... |
Title: Chain of Thought Imitation with Procedure Cloning Abstract: Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning problem in which one fits a function approximator to the input-output mapping e... |
Title: A Convolutional Dispersion Relation Preserving Scheme for the Acoustic Wave Equation Abstract: We propose an accurate numerical scheme for approximating the solution of the two dimensional acoustic wave problem. We use machine learning to find a stencil suitable even in the presence of high wavenumbers. The prop... |
Title: What Do Compressed Multilingual Machine Translation Models Forget? Abstract: Recently, very large pre-trained models achieve state-of-the-art results in various natural language processing (NLP) tasks, but their size makes it more challenging to apply them in resource-constrained environments. Compression techni... |
Title: Neural Inverse Kinematics Abstract: Inverse kinematic (IK) methods recover the parameters of the joints, given the desired position of selected elements in the kinematic chain. While the problem is well-defined and low-dimensional, it has to be solved rapidly, accounting for multiple possible solutions. In this ... |
Title: Self-supervised U-net for few-shot learning of object segmentation in microscopy images Abstract: State-of-the-art segmentation performances are achieved by deep neural networks. Training these networks from only a few training examples is challenging while producing annotated images that provide supervision is ... |
Title: Addressing Strategic Manipulation Disparities in Fair Classification Abstract: In real-world classification settings, individuals respond to classifier predictions by updating their features to increase their likelihood of receiving a particular (positive) decision (at a certain cost). Yet, when different demogr... |
Title: Robust Quantity-Aware Aggregation for Federated Learning Abstract: Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning framework. However, classical FL faces serious security and robustness... |
Title: RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language Learning Abstract: Lifelong Language Learning (LLL) aims to train a neural network to learn a stream of NLP tasks while retaining knowledge from previous tasks. However, previous works which followed data-free constraint still suffer from ... |
Title: Positioning Fog Computing for Smart Manufacturing Abstract: We study machine learning systems for real-time industrial quality control. In many factory systems, production processes must be continuously controlled in order to maintain product quality. Especially challenging are the systems that must balance in r... |
Title: Federated Learning Aggregation: New Robust Algorithms with Guarantees Abstract: Federated Learning has been recently proposed for distributed model training at the edge. The principle of this approach is to aggregate models learned on distributed clients to obtain a new more general "average" model (FedAvg). The... |
Title: Memory-efficient Reinforcement Learning with Knowledge Consolidation Abstract: Artificial neural networks are promising as general function approximators but challenging to train on non-independent and identically distributed data due to catastrophic forgetting. Experience replay, a standard component in deep re... |
Title: Fusion Subspace Clustering for Incomplete Data Abstract: This paper introduces {\em fusion subspace clustering}, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data. The main idea is to assign each datum to a subspace of its own, and minimize the distance be... |
Title: Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation Abstract: Gaussian processes (GPs) are Bayesian non-parametric models useful in a myriad of applications. Despite their popularity, the cost of GP predictions (quadratic storage and cubic complexity with respect to the ... |
Title: Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output Abstract: Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities. The primary foc... |
Title: Contextual Information-Directed Sampling Abstract: Information-directed sampling (IDS) has recently demonstrated its potential as a data-efficient reinforcement learning algorithm. However, it is still unclear what is the right form of information ratio to optimize when contextual information is available. We in... |
Title: Improved Modeling of Persistence Diagram Abstract: High-dimensional reduction methods are powerful tools for describing the main patterns in big data. One of these methods is the topological data analysis (TDA), which modeling the shape of the data in terms of topological properties. This method specifically tra... |
Title: Power and accountability in reinforcement learning applications to environmental policy Abstract: Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques avai... |
Title: Limitations of a proposed correction for slow drifts in decision criterion Abstract: Trial history biases in decision-making tasks are thought to reflect systematic updates of decision variables, therefore their precise nature informs conclusions about underlying heuristic strategies and learning processes. Howe... |
Title: Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited Abstract: Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism. We give a unified view on both classes of graph kernels. We st... |
Title: Test-Time Robust Personalization for Federated Learning Abstract: Federated Learning (FL) is a machine learning paradigm where many clients collaboratively learn a shared global model with decentralized training data. Personalization on FL model additionally adapts the global model to different clients, achievin... |
Title: Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class Classification Abstract: The work in ICML'09 showed that the derivatives of the classical multi-class logistic regression loss function could be re-written in terms of a pre-chosen "base class" and applied the new derivatives in the ... |
Title: Argumentative Explanations for Pattern-Based Text Classifiers Abstract: Recent works in Explainable AI mostly address the transparency issue of black-box models or create explanations for any kind of models (i.e., they are model-agnostic), while leaving explanations of interpretable models largely underexplored.... |
Title: AutoJoin: Efficient Adversarial Training for Robust Maneuvering via Denoising Autoencoder and Joint Learning Abstract: As a result of increasingly adopted machine learning algorithms and ubiquitous sensors, many 'perception-to-control' systems have been deployed in various settings. For these systems to be trust... |
Title: On Elimination Strategies for Bandit Fixed-Confidence Identification Abstract: Elimination algorithms for bandit identification, which prune the plausible correct answers sequentially until only one remains, are computationally convenient since they reduce the problem size over time. However, existing eliminatio... |
Title: muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems Abstract: Most uses of machine learning today involve training a model from scratch for a particular task, or sometimes starting with a model pretrained on a related task and then fine-tuning on a downstream task. Both ap... |
Title: Toward smart composites: small-scale, untethered prediction and control for soft sensor/actuator systems Abstract: We present a suite of algorithms and tools for model-predictive control of sensor/actuator systems with embedded microcontroller units (MCU). These MCUs can be colocated with sensors and actuators, ... |
Title: Deep Discriminative Direct Decoders for High-dimensional Time-series Analysis Abstract: Dynamical latent variable modeling has been significantly invested over the last couple of decades with established solutions encompassing generative processes like the state-space model (SSM) and discriminative processes lik... |
Title: Incentivizing Federated Learning Abstract: Federated Learning is an emerging distributed collaborative learning paradigm used by many of applications nowadays. The effectiveness of federated learning relies on clients' collective efforts and their willingness to contribute local data. However, due to privacy con... |
Title: Analysis of functional neural codes of deep learning models Abstract: Deep neural networks (DNNs), the agents of deep learning (DL), require a massive number of parallel/sequential operations. This makes it extremely challenging to comprehend DNNs' operations and hinders proper diagnosis. Consequently, DNNs cann... |
Title: CYRUS Soccer Simulation 2D Team Description Paper 2022 Abstract: Soccer Simulation 2D League is one of the major leagues of RoboCup competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one coach compete against each other. The players are only allowed to communicate with the server th... |
Title: Investigating classification learning curves for automatically generated and labelled plant images Abstract: In the context of supervised machine learning a learning curve describes how a model's performance on unseen data relates to the amount of samples used to train the model. In this paper we present a datas... |
Title: Global Extreme Heat Forecasting Using Neural Weather Models Abstract: Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore t... |
Title: Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning Abstract: Electric Take-Off and Landing (eVTOL) aircraft is considered as the major aircraft type in the emerging urban air mobility. Accurate power consumption estimation is crucial to eVTOL, support... |
Title: Semi-Decentralized Federated Learning with Collaborative Relaying Abstract: We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every communication round to the PS, each client computes a local ... |
Title: Nonparametric learning of kernels in nonlocal operators Abstract: Nonlocal operators with integral kernels have become a popular tool for designing solution maps between function spaces, due to their efficiency in representing long-range dependence and the attractive feature of being resolution-invariant. In thi... |
Title: Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies Abstract: The current work on reinforcement learning (RL) from demonstrations often assumes the demonstrations are samples from an optimal policy, an unrealistic assumption in p... |
Title: Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization Abstract: In this paper, we introduce a probabilistic model for learning nonnegative matrix factorization (NMF) that is commonly used for predicting missing values and finding hidden patterns in the data, in which the matrix factors ar... |
Title: Distance-Sensitive Offline Reinforcement Learning Abstract: In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, existing offline RL methods are often over-conservative, inevitably hurting ... |
Title: HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization Abstract: Wide applications of differentiable two-player sequential games (e.g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast algorithms. Most of the existing ... |
Title: Body Composition Estimation Based on Multimodal Multi-task Deep Neural Network Abstract: In addition to body weight and Body Mass Index (BMI), body composition is an essential data point that allows people to understand their overall health and body fitness. However, body composition is largely made up of muscle... |
Title: Augmented Newton Method for Optimization: Global Linear Rate and Momentum Interpretation Abstract: We propose two variants of Newton method for solving unconstrained minimization problem. Our method leverages optimization techniques such as penalty and augmented Lagrangian method to generate novel variants of th... |
Title: FLEX: Feature-Logic Embedding Framework for CompleX Knowledge Graph Reasoning Abstract: Current best performing models for knowledge graph reasoning (KGR) are based on complex distribution or geometry objects to embed entities and first-order logical (FOL) queries in low-dimensional spaces. They can be summarize... |
Title: Personalized Federated Learning with Server-Side Information Abstract: Personalized Federated Learning (FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of data heterogeneity among clients. However, one of the main challenges for personalized FL is the heavy re... |
Title: GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model Abstract: High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distribu... |
Title: Flow-based Recurrent Belief State Learning for POMDPs Abstract: Partially Observable Markov Decision Process (POMDP) provides a principled and generic framework to model real world sequential decision making processes but yet remains unsolved, especially for high dimensional continuous space and unknown models. ... |
Title: TempLM: Distilling Language Models into Template-Based Generators Abstract: While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content. In contrast, classic template-based systems provide strong guarantees of faithfulnes... |
Title: Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits Abstract: We consider the problem of falsifying safety requirements of Cyber-Physical Systems expressed in signal temporal logic (STL). This problem can be turned into an optimiz... |
Title: Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems Abstract: We propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks. WOGAN is a general-purpose black-box test generator applicable to any system under test havin... |
Title: WOGAN at the SBST 2022 CPS Tool Competition Abstract: WOGAN is an online test generation algorithm based on Wasserstein generative adversarial networks. In this note, we present how WOGAN works and summarize its performance in the SBST 2022 CPS tool competition concerning the AI of a self-driving car. |
Title: Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures Abstract: The Expectation-Maximization (EM) algorithm has been predominantly used to approximate the maximum likelihood estimation of the location-scale Gaussian mixtures. However, when the models are over-specified, namely, the... |
Title: YouTube Ad View Sentiment Analysis using Deep Learning and Machine Learning Abstract: Sentiment Analysis is currently a vital area of research. With the advancement in the use of the internet, the creation of social media, websites, blogs, opinions, ratings, etc. has increased rapidly. People express their feedb... |
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