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Title: A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis Abstract: The Epithelial Dysplasia (ED) is a tissue alteration commonly present in lesions preceding oral cancer, being its presence one of the most important factors in the progression toward carcinoma. This study proposes a m... |
Title: An optimized hybrid solution for IoT based lifestyle disease classification using stress data Abstract: Stress, anxiety, and nervousness are all high-risk health states in everyday life. Previously, stress levels were determined by speaking with people and gaining insight into what they had experienced recently ... |
Title: Learning to Compose Soft Prompts for Compositional Zero-Shot Learning Abstract: We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) without the overhead of fine-tuning the entir... |
Title: Risk-based regulation for all: The need and a method for a wide adoption solution for data-driven inspection targeting Abstract: Access to data and data processing, including the use of machine learning techniques, has become significantly easier and cheaper in recent years. Nevertheless, solutions that can be w... |
Title: Heterogeneous Target Speech Separation Abstract: We introduce a new paradigm for single-channel target source separation where the sources of interest can be distinguished using non-mutually exclusive concepts (e.g., loudness, gender, language, spatial location, etc). Our proposed heterogeneous separation framew... |
Title: Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning Abstract: The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal. A popular class of approach infers the (unknown) reward function via inverse reinforcemen... |
Title: Pin the Memory: Learning to Generalize Semantic Segmentation Abstract: The rise of deep neural networks has led to several breakthroughs for semantic segmentation. In spite of this, a model trained on source domain often fails to work properly in new challenging domains, that is directly concerned with the gener... |
Title: Unified Contrastive Learning in Image-Text-Label Space Abstract: Visual recognition is recently learned via either supervised learning on human-annotated image-label data or language-image contrastive learning with webly-crawled image-text pairs. While supervised learning may result in a more discriminative repr... |
Title: Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field Inference Abstract: Second-order semantic parsing with end-to-end mean-field inference has been shown good performance. In this work we aim to improve this method by modeling label correlations between adjacent arcs. However... |
Title: Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses Abstract: In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer repr... |
Title: The Effects of Regularization and Data Augmentation are Class Dependent Abstract: Regularization is a fundamental technique to prevent over-fitting and to improve generalization performances by constraining a model's complexity. Current Deep Networks heavily rely on regularizers such as Data-Augmentation (DA) or... |
Title: Class-Incremental Learning with Strong Pre-trained Models Abstract: Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a ... |
Title: Equivariance Discovery by Learned Parameter-Sharing Abstract: Designing equivariance as an inductive bias into deep-nets has been a prominent approach to build effective models, e.g., a convolutional neural network incorporates translation equivariance. However, incorporating these inductive biases requires know... |
Title: Unsupervised Image-to-Image Translation with Generative Prior Abstract: Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. Despite the recent progress in image translation models, it remains challenging to build mappings between complex domains w... |
Title: Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder Abstract: The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the... |
Title: Learning to Walk Autonomously via Reset-Free Quality-Diversity Abstract: Quality-Diversity (QD) algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills. However, the generation of behavioural repertoires has mainly been limited to simulation environ... |
Title: Qade: Solving Differential Equations on Quantum Annealers Abstract: We present a general method, called Qade, for solving differential equations using a quantum annealer. The solution is obtained as a linear combination of a set of basis functions. On current devices, Qade can solve systems of coupled partial di... |
Title: TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates Abstract: We propose a novel approach to generate temporally coherent UV coordinates for loose clothing. Our method is not constrained by human body outlines and can capture loose garments and hair. We implemented a differentiable pipel... |
Title: Adaptive-Gravity: A Defense Against Adversarial Samples Abstract: This paper presents a novel model training solution, denoted as Adaptive-Gravity, for enhancing the robustness of deep neural network classifiers against adversarial examples. We conceptualize the model parameters/features associated with each cla... |
Title: Physics-assisted Generative Adversarial Network for X-Ray Tomography Abstract: X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be ... |
Title: Introducing a Framework and a Decision Protocol to Calibrate Recommender Systems Abstract: Recommender Systems use the user's profile to generate a recommendation list with unknown items to a target user. Although the primary goal of traditional recommendation systems is to deliver the most relevant items, such ... |
Title: Using Multiple Self-Supervised Tasks Improves Model Robustness Abstract: Deep networks achieve state-of-the-art performance on computer vision tasks, yet they fail under adversarial attacks that are imperceptible to humans. In this paper, we propose a novel defense that can dynamically adapt the input using the ... |
Title: A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework Abstract: Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of da... |
Title: Automated Design of Salient Object Detection Algorithms with Brain Programming Abstract: Despite recent improvements in computer vision, artificial visual systems' design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still o... |
Title: A Kernel Method to Nonlinear Location Estimation with RSS-based Fingerprint Abstract: This paper presents a nonlinear location estimation to infer the position of a user holding a smartphone. We consider a large location with $M$ number of grid points, each grid point is labeled with a unique fingerprint consist... |
Title: T4PdM: a Deep Neural Network based on the Transformer Architecture for Fault Diagnosis of Rotating Machinery Abstract: Deep learning and big data algorithms have become widely used in industrial applications to optimize several tasks in many complex systems. Particularly, deep learning model for diagnosing and p... |
Title: Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds Abstract: A recent emphasis of distributed learning research has been on federated learning (FL), in which model training is conducted by the data-collecting devices. Existing research on FL has mostly focused on a star ... |
Title: GreaseVision: Rewriting the Rules of the Interface Abstract: Digital harms can manifest across any interface. Key problems in addressing these harms include the high individuality of harms and the fast-changing nature of digital systems. As a result, we still lack a systematic approach to study harms and produce... |
Title: Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition Abstract: With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature extraction and classification to aut... |
Title: BankNote-Net: Open dataset for assistive universal currency recognition Abstract: Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including optical character recognition, scene identification, person recognition, an... |
Title: Brain-Inspired Hyperdimensional Computing: How Thermal-Friendly for Edge Computing? Abstract: Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning (ML) methods. It is based on large vectors of binary or bipolar symbols and a few simple mathematical operations. The promise of HDC is a h... |
Title: Compositional Generalization and Decomposition in Neural Program Synthesis Abstract: When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabi... |
Title: Quantum version of the k-NN classifier based on a quantum sorting algorithm Abstract: In this work we introduce a quantum sorting algorithm with adaptable requirements of memory and circuit depth, and then use it to develop a new quantum version of the classical machine learning algorithm known as k-nearest neig... |
Title: Global ECG Classification by Self-Operational Neural Networks with Feature Injection Abstract: Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of bot... |
Title: Q-learning with online random forests Abstract: $Q$-learning is the most fundamental model-free reinforcement learning algorithm. Deployment of $Q$-learning requires approximation of the state-action value function (also known as the $Q$-function). In this work, we provide online random forests as $Q$-function a... |
Title: Free Energy Evaluation Using Marginalized Annealed Importance Sampling Abstract: The evaluation of the free energy of a stochastic model is considered to be a significant issue in various fields of physics and machine learning. However, the exact free energy evaluation is computationally infeasible because it in... |
Title: Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition Abstract: Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile devices and smart home speak... |
Title: A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis Abstract: Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint... |
Title: Federated Learning with Partial Model Personalization Abstract: We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately on the devices. Both algorithms have been proposed in the literature,... |
Title: Exploring the Universality of Hadronic Jet Classification Abstract: The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when ... |
Title: DiversiTree: A New Method to Efficiently Compute Diverse Sets of Near-Optimal Solutions to Mixed-Integer Optimization Problems Abstract: While most methods for solving mixed-integer optimization problems compute a single optimal solution, a diverse set of near-optimal solutions can often be more useful. We prese... |
Title: Does the Market of Citations Reward Reproducible Work? Abstract: The field of bibliometrics, studying citations and behavior, is critical to the discussion of reproducibility. Citations are one of the primary incentive and reward systems for academic work, and so we desire to know if this incentive rewards repro... |
Title: Data-Driven Evaluation of Training Action Space for Reinforcement Learning Abstract: Training action space selection for reinforcement learning (RL) is conflict-prone due to complex state-action relationships. To address this challenge, this paper proposes a Shapley-inspired methodology for training action space... |
Title: Decompositional Generation Process for Instance-Dependent Partial Label Learning Abstract: Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that ... |
Title: Controllable Missingness from Uncontrollable Missingness: Joint Learning Measurement Policy and Imputation Abstract: Due to the cost or interference of measurement, we need to control measurement system. Assuming that each variable can be measured sequentially, there exists optimal policy choosing next measureme... |
Title: CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning Abstract: Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model. Despite the recent progress, it remains challenging to deal... |
Title: Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 1 Abstract: In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time. The proposed approa... |
Title: A posteriori learning for quasi-geostrophic turbulence parametrization Abstract: The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subg... |
Title: SuperNet in Neural Architecture Search: A Taxonomic Survey Abstract: Deep Neural Networks (DNN) have made significant progress in a wide range of visual recognition tasks such as image classification, object detection, and semantic segmentation. The evolution of convolutional architectures has led to better perf... |
Title: Network Shuffling: Privacy Amplification via Random Walks Abstract: Recently, it is shown that shuffling can amplify the central differential privacy guarantees of data randomized with local differential privacy. Within this setup, a centralized, trusted shuffler is responsible for shuffling by keeping the ident... |
Title: Global Update Guided Federated Learning Abstract: Federated learning protects data privacy and security by exchanging models instead of data. However, unbalanced data distributions among participating clients compromise the accuracy and convergence speed of federated learning algorithms. To alleviate this proble... |
Title: Does Robustness on ImageNet Transfer to Downstream Tasks? Abstract: As clean ImageNet accuracy nears its ceiling, the research community is increasingly more concerned about robust accuracy under distributional shifts. While a variety of methods have been proposed to robustify neural networks, these techniques o... |
Title: Study of a committee of neural networks for biometric hand-geometry recognition Abstract: This Paper studies different committees of neural networks for biometric pattern recognition. We use the neural nets as classifiers for identification and verification purposes. We show that a committee of nets can improve ... |
Title: Channel model for end-to-end learning of communications systems: A survey Abstract: The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end pe... |
Title: Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent Large Spatial-Temporal Data-Driven Approach -- Part 2 Abstract: In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing weighted average turnaround time. The proposed approach is... |
Title: Blockchain as an Enabler for Transfer Learning in Smart Environments Abstract: The knowledge, embodied in machine learning models for intelligent systems, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labelling, network training, and fine-tuning of mode... |
Title: Disability prediction in multiple sclerosis using performance outcome measures and demographic data Abstract: Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for disease identification. Ho... |
Title: KGI: An Integrated Framework for Knowledge Intensive Language Tasks Abstract: In a recent work, we presented a novel state-of-the-art approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. In this paper... |
Title: The Complexity of Markov Equilibrium in Stochastic Games Abstract: We show that computing approximate stationary Markov coarse correlated equilibria (CCE) in general-sum stochastic games is computationally intractable, even when there are two players, the game is turn-based, the discount factor is an absolute co... |
Title: ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation Abstract: Electrocardiograms (ECGs) have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits, such as difficulty to counterfeit, liveness detection, and ubi... |
Title: Labeling-Free Comparison Testing of Deep Learning Models Abstract: Various deep neural networks (DNNs) are developed and reported for their tremendous success in multiple domains. Given a specific task, developers can collect massive DNNs from public sources for efficient reusing and avoid redundant work from sc... |
Title: SnapMode: An Intelligent and Distributed Large-Scale Fashion Image Retrieval Platform Based On Big Data and Deep Generative Adversarial Network Technologies Abstract: Fashion is now among the largest industries worldwide, for it represents human history and helps tell the worlds story. As a result of the Fourth ... |
Title: Mel-spectrogram features for acoustic vehicle detection and speed estimation Abstract: The paper addresses acoustic vehicle detection and speed estimation from single sensor measurements. We predict the vehicle's pass-by instant by minimizing clipped vehicle-to-microphone distance, which is predicted from the me... |
Title: Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment Abstract: Speech intelligibility assessment plays an important role in the therapy of patients suffering from pathological speech disorders. Automatic and objective measures are desirable to assist therapists in their... |
Title: Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage Abstract: Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases s... |
Title: Engagement Detection with Multi-Task Training in E-Learning Environments Abstract: Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly... |
Title: Ontology Matching Through Absolute Orientation of Embedding Spaces Abstract: Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are emb... |
Title: Checking HateCheck: a cross-functional analysis of behaviour-aware learning for hate speech detection Abstract: Behavioural testing -- verifying system capabilities by validating human-designed input-output pairs -- is an alternative evaluation method of natural language processing systems proposed to address th... |
Title: C-NMT: A Collaborative Inference Framework for Neural Machine Translation Abstract: Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for other tasks, CI has never been applied to the seque... |
Title: KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media Abstract: Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to iden... |
Title: GPSAF: A Generalized Probabilistic Surrogate-Assisted Framework for Constrained Single- and Multi-objective Optimization Abstract: Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into opti... |
Title: Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings Abstract: One intriguing property of adversarial attacks is their "transferability" -- an adversarial example crafted with respect to one deep neural network (DNN) model is often found effective against other DNNs as well.... |
Title: Predicting Berth Stay for Tanker Terminals: A Systematic and Dynamic Approach Abstract: Given the trend of digitization and increasing number of maritime transport, prediction of vessel berth stay has been triggered for requirements of operation research and scheduling optimization problem in the era of maritime... |
Title: Neural Tangent Generalization Attacks Abstract: The remarkable performance achieved by Deep Neural Networks (DNNs) in many applications is followed by the rising concern about data privacy and security. Since DNNs usually require large datasets to train, many practitioners scrape data from external sources such ... |
Title: Karaoker: Alignment-free singing voice synthesis with speech training data Abstract: Existing singing voice synthesis models (SVS) are usually trained on singing data and depend on either error-prone time-alignment and duration features or explicit music score information. In this paper, we propose Karaoker, a m... |
Title: EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector Abstract: Due to the importance of Critical Infrastructure (CI) in a nation's economy, they have been lucrative targets for cyber attackers. These critical infrastructures are usually Cyber-Physical Systems (CPS) such as power grids... |
Title: Self-supervised Speaker Diarization Abstract: Over the last few years, deep learning has grown in popularity for speaker verification, identification, and diarization. Inarguably, a significant part of this success is due to the demonstrated effectiveness of their speaker representations. These, however, are hea... |
Title: Ranking with submodular functions on a budget Abstract: Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted... |
Title: Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning Abstract: Contrastive learning enables learning useful audio and speech representations without ground-truth labels by maximizing the similarity between latent representations of similar signal... |
Title: A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation Abstract: The next generation of physical science involves robot scientists - autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop. Such systems have... |
Title: Learning Polynomial Transformations Abstract: We consider the problem of learning high dimensional polynomial transformations of Gaussians. Given samples of the form $p(x)$, where $x\sim N(0, \mathrm{Id}_r)$ is hidden and $p: \mathbb{R}^r \to \mathbb{R}^d$ is a function where every output coordinate is a low-deg... |
Title: Measuring AI Systems Beyond Accuracy Abstract: Current test and evaluation (T&E) methods for assessing machine learning (ML) system performance often rely on incomplete metrics. Testing is additionally often siloed from the other phases of the ML system lifecycle. Research investigating cross-domain approaches t... |
Title: Structure-aware Protein Self-supervised Learning Abstract: Protein representation learning methods have shown great potential to yield useful representation for many downstream tasks, especially on protein classification. Moreover, a few recent studies have shown great promise in addressing insufficient labels o... |
Title: Intelligent Sight and Sound: A Chronic Cancer Pain Dataset Abstract: Cancer patients experience high rates of chronic pain throughout the treatment process. Assessing pain for this patient population is a vital component of psychological and functional well-being, as it can cause a rapid deterioration of quality... |
Title: Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization Abstract: Data-free quantization is a task that compresses the neural network to low bit-width without access to original training data. Most existing data-free quantization methods cause severe performance degradation due ... |
Title: Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution Abstract: Super-resolving medical images can help physicians in providing more accurate diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging (MRI) techniques output severa... |
Title: Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis Abstract: Lung cancer has the highest mortality rate of deadly cancers in the world. Early detection is essential to treatment of lung cancer. However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experi... |
Title: Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment Abstract: Deep Neural Networks (DNNs) have gained considerable attention in the past decades due to their astounding performance in different applications, such as natural language modeling, self-driving assistance, and sou... |
Title: Vision-Based American Sign Language Classification Approach via Deep Learning Abstract: Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) is one of the sign languages that most commonl... |
Title: HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection Abstract: The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. The utilisation ... |
Title: Interpretable AI for policy-making in pandemics Abstract: Since the first wave of the COVID-19 pandemic, governments have applied restrictions in order to slow down its spreading. However, creating such policies is hard, especially because the government needs to trade-off the spreading of the pandemic with the ... |
Title: Evaluating the Adversarial Robustness for Fourier Neural Operators Abstract: In recent years, Machine-Learning (ML)-driven approaches have been widely used in scientific discovery domains. Among them, the Fourier Neural Operator (FNO) was the first to simulate turbulent flow with zero-shot super-resolution and s... |
Title: Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures Abstract: Deep learning using neural networks is an effective technique for generating models of complex data. However, training such models can be expensive when networks have large model capacity resulting from a large number of ... |
Title: On Improving Cross-dataset Generalization of Deepfake Detectors Abstract: Facial manipulation by deep fake has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deep fake detection methods have been proposed recently. Most of them model deep fake detection as a bin... |
Title: Multi-objective evolution for Generalizable Policy Gradient Algorithms Abstract: Performance, generalizability, and stability are three Reinforcement Learning (RL) challenges relevant to many practical applications in which they present themselves in combination. Still, state-of-the-art RL algorithms fall short ... |
Title: Learning to modulate random weights can induce task-specific contexts for economical meta and continual learning Abstract: Neural networks are vulnerable to catastrophic forgetting when data distributions are non-stationary during continual online learning; learning of a later task often leads to forgetting of a... |
Title: Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics Abstract: This paper focuses on robotic reinforcement learning with sparse rewards for natural language goal representations. An open problem is the sample-inefficiency that stems from the compositionality of natural language, and... |
Title: Machine learning model to predict solar radiation, based on the integration of meteorological data and data obtained from satellite images Abstract: Knowing the behavior of solar radiation at a geographic location is essential for the use of energy from the sun using photovoltaic systems; however, the number of ... |
Title: Approximate discounting-free policy evaluation from transient and recurrent states Abstract: In order to distinguish policies that prescribe good from bad actions in transient states, we need to evaluate the so-called bias of a policy from transient states. However, we observe that most (if not all) works in app... |
Title: An Adaptive Black-box Backdoor Detection Method for Deep Neural Networks Abstract: With the surge of Machine Learning (ML), An emerging amount of intelligent applications have been developed. Deep Neural Networks (DNNs) have demonstrated unprecedented performance across various fields such as medical diagnosis a... |
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