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Title: An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments Abstract: In the last few years, the research activity around reinforcement learning tasks formulated over environments with sparse rewards has been especially notable. Among the numerous a... |
Title: How Powerful are Spectral Graph Neural Networks Abstract: Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on graph signal filters. Some models able to learn arbitrary spectral filters have emerged recently. However, few works analyze the expressive power of spectral GNNs. This paper s... |
Title: Logarithmic regret bounds for continuous-time average-reward Markov decision processes Abstract: We consider reinforcement learning for continuous-time Markov decision processes (MDPs) in the infinite-horizon, average-reward setting. In contrast to discrete-time MDPs, a continuous-time process moves to a state a... |
Title: Time-series Transformer Generative Adversarial Networks Abstract: Many real-world tasks are plagued by limitations on data: in some instances very little data is available and in others, data is protected by privacy enforcing regulations (e.g. GDPR). We consider limitations posed specifically on time-series data... |
Title: Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning Abstract: Learning to coordinate is a daunting problem in multi-agent reinforcement learning (MARL). Previous works have explored it from many facets, including cognition between agents, credit assignment, communication... |
Title: Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization Abstract: In recent years, decentralized learning has emerged as a powerful tool not only for large-scale machine learning, but also for preserving privacy. One of the key challenges in decentralized learning is th... |
Title: Collaborative Adversarial Training Abstract: The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to local non-smoothness and steepness of normally obtained loss landscapes. Training augmented with adversa... |
Title: KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation Abstract: Leveraging graphs on recommender systems has gained popularity with the development of graph representation learning (GRL). In particular, knowledge graph embedding (KGE) and graph neural networks (GNNs) are... |
Title: Split personalities in Bayesian Neural Networks: the case for full marginalisation Abstract: The true posterior distribution of a Bayesian neural network is massively multimodal. Whilst most of these modes are functionally equivalent, we demonstrate that there remains a level of real multimodality that manifests... |
Title: OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization Abstract: As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hardware platforms, e.g., smartphones. Numerous networ... |
Title: Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation Abstract: We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where instead of receiving a numeric reward at each step, the agent only receives preferences over trajec... |
Title: GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection Abstract: In recent years, the emergence and development of third-party platforms have greatly facilitated the growth of the Online to Offline (O2O) business. However, the large amount of transaction data raises new challe... |
Title: KRNet: Towards Efficient Knowledge Replay Abstract: The knowledge replay technique has been widely used in many tasks such as continual learning and continuous domain adaptation. The key lies in how to effectively encode the knowledge extracted from previous data and replay them during current training procedure... |
Title: PyRelationAL: A Library for Active Learning Research and Development Abstract: In constrained real-world scenarios where it is challenging or costly to generate data, disciplined methods for acquiring informative new data points are of fundamental importance for the efficient training of machine learning (ML) mo... |
Title: DistilCamemBERT: a distillation of the French model CamemBERT Abstract: Modern Natural Language Processing (NLP) models based on Transformer structures represent the state of the art in terms of performance on very diverse tasks. However, these models are complex and represent several hundred million parameters ... |
Title: Meta-Learning Regrasping Strategies for Physical-Agnostic Objects Abstract: Grasping inhomogeneous objects, practical use in real-world applications, remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction. In this study, we propose a vision-based m... |
Title: Learning to branch with Tree MDPs Abstract: State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule. The idea of learning branching rules from data has received increasing attention recently, and promising re... |
Title: An improved neural network model for treatment effect estimation Abstract: Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions. The key for addressing these problems is the wealth of observational data and the processes fo... |
Title: Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization Abstract: At first sight it may seem straightforward to use recurrent layers in Deep Reinforcement Learning algorithms to enable agents to make use of memory in the setting of partially observable environments. Starting from widely used... |
Title: FL Games: A federated learning framework for distribution shifts Abstract: Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each hold data from a different distribution, whereby predictive... |
Title: Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization Abstract: In this paper, we propose a strategy to construct a multi-objective optimization algorithm from a single-objective optimization algorithm by using the B\'ezier simplex model. Also, we extend the stability of optimizati... |
Title: PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection Abstract: The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, these applications usually have an... |
Title: FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation Abstract: Given the high incidence and effective treatment options for liver diseases, they are of great socioeconomic importance. One of the most common methods for analyzing CT and MRI images for diagnosis and follow... |
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... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Cardiomegaly Detection using Deep Convolutional Neural Network with U-Net Abstract: Cardiomegaly is indeed a medical disease in which the heart is enlarged. Cardiomegaly is better to handle if caught early, so early detection is critical. The chest X-ray, being one of the most often used radiography examinations... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Deep Low-Density Separation for Semi-Supervised Classification Abstract: Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (SSL) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised ... |
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: 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: 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: 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: 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: Edge Graph Neural Networks for Massive MIMO Detection Abstract: Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems. While traditional Belief Propagation (BP) detectors perform poorly on loopy graphs, the recent Graph Neural Networks (GNNs)-based ... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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... |
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