Automates quality control for synthetic CTs generated from MR images.
problem Prevent downstream errors in RT treatment planning from synthetic CTs.
method Ensemble of sCT generators and uncertainty measure based on their disagreement.
result Uncertainty measure can detect input images outside expected MR distribution and sCT images with potential errors.
A framework assesses the quality of crowdsourced weather data.
problem Quality control and assessment of crowdsourced weather data from third-party stations.
method Proposes a simple, scalable, and interpretable AI/Stats/ML framework to assess TPAWS data.
result Demonstrates the performance of the framework using synthetic and real data.
ControlVAE improves VAE performance by adding a controller to tune hyperparameters.
problem Existing VAE models struggle with KL vanishing and low reconstruction quality.
method ControlVAE combines a controller inspired by automatic control theory with VAE to improve performance.
result ControlVAE achieves better disentangling and reconstruction quality than existing methods.
QC methods improve reliability of machine learning-based image segmentation.
problem Inaccuracies in machine learning algorithms limit their clinical applicability.
method Analysis and validation of QC approaches for automatic segmentation.
result Aggregation of uncertainty and Dice prediction methods improved segmentation reliability.
This paper predicts registration error in medical images using a random regression forest.
problem Predicting registration error in medical images without a ground truth.
method Random regression forest trained on features related to transformation model and dissimilarity after registration.
result The method achieves good performance in automatic quality control of large-scale image analysis.
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating fr…
Existing model-based reinforcement learning methods often study perception modeling and decision making separately. We introduce joint Perception and Control as Inference (PCI), a general framework to combine perception and control for partially observable environments through Bayesian inference. Based on the fact that…
Paper characterizes early-stage dementia signatures from sensor data.
problem Detecting early-stage dementia from sensor data.
method Developed bespoke behavioural models from longitudinal sensor data.
result Found subtle differences in sleep quality and wandering between dementia patients and controls.
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the g…
We continue to develop our neural network (NN) based forecasting approach to anomaly detection (AD) using the Secure Water Treatment (SWaT) industrial control system (ICS) testbed dataset. We propose genetic algorithms (GA) to find the best NN architecture for a given dataset, using the NAB metric to assess the quality…
Automatically assesses the quality of online health articles.
problem Lack of automated tools to evaluate the quality of online health information.
method Data mining approach using 10 quality criteria and feature selection.
result Classifier achieved 84%-90% accuracy on 10 criteria.
RevGAN generates personalized reviews with given sentiment and style.
problem Creating reviews that are both personalized and of high quality.
method RevGAN combines self-attentive recursive autoencoders, conditional discriminators, and personalized decoders.
result RevGAN outperforms state-of-the-art models in sentence quality, coherence, and personalization.
A popular tool for unsupervised modelling and mining multi-aspect data is tensor decomposition. In an exploratory setting, where and no labels or ground truth are available how can we automatically decide how many components to extract? How can we assess the quality of our results, so that a domain expert can factor th…
Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss,…
Generative adversarial networks (GANs) have been shown to produce realistic samples from high-dimensional distributions, but training them is considered hard. A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fa…
Re-speaking is a mechanism for obtaining high quality subtitles for use in live broadcast and other public events. Because it relies on humans performing the actual re-speaking, the task of estimating the quality of the results is non-trivial. Most organisations rely on humans to perform the actual quality assessment, …
Framework creates fast, interpretable surrogates for stochastic simulators with unbounded randomness.
problem Creating accurate and fast approximations for stochastic simulators with unbounded randomness.
method Probabilistic surrogate networks that retain structure of reference simulators and enable amortized inference.
result Surrogates accurately model stochastic programs with unbounded random variables and significantly speed up inference.
Unified framework uses all data to improve multiple testing efficiency.
problem Improving predictive uncertainty control in decision-making.
method Uses all available data (null, alternative, unlabelled) for score construction and calibration.
result Significantly improves power and adaptability across diverse scenarios.
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the …
Automatically finds strong neural network topologies for continuous control tasks.
problem Handcrafted neural network architectures limit the performance of Deep Reinforcement Learning.
method Combines Neuroevolution with off-policy training and proposes a novel architecture mutation operator.
result The proposed Actor-Critic Neuroevolution algorithm often outperforms strong baseline methods.
Online crowdsourcing provides a scalable and inexpensive means to collect knowledge (e.g. labels) about various types of data items (e.g. text, audio, video). However, it is also known to result in large variance in the quality of recorded responses which often cannot be directly used for training machine learning syst…
Paper proposes a method to automatically detect drift in machine learning models.
problem Detecting changes in class-label data distributions that affect model predictions.
method Self-evaluating predictive model degradation to detect concept drift.
result Effectiveness in automatically detecting and describing concept drift.
Automatically evaluates image quality based on human judgment.
problem Difficulty in rigorously evaluating generated image quality.
method Generative model embeddings, human labels regression, and statistical matching.
result 66% accuracy in predicting human scores of image realism.
GShard enables scaling of large neural networks with automatic sharding and lightweight APIs.
problem Scaling neural networks to handle vast training data and compute efficiently.
method GShard uses lightweight annotation APIs and XLA compiler extensions for parallel computation.
result GShard successfully trained a 600 billion parameter model on 2048 TPUs in 4 days.
The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically se…
Paper proposes a method to generate task-oriented dialogue queries.
problem Scarcity of training data for task-oriented dialogue systems.
method Conditional variational autoencoder for intent-specific sentence generation and query transfer protocol.
result Our method improves the diversity of generated queries without compromising quality.
A simple method reduces bias in LLM auto-evaluators by controlling output length.
problem Bias in LLM auto-annotators, particularly length bias.
method Simple regression analysis to control for length difference and other mediators.
result Improved robustness and increased correlation with human preferences.
This paper proposes a method to safely adjust exploration in RL to satisfy constraints.
problem Unsafe exploration in reinforcement learning violates constraints on controlled object states.
method Automatic adjustment of exploration inputs and variance-covariance matrix for safety.
result The method guarantees satisfaction of joint chance constraints with specified probability.
Automatically identifies RRLyrae stars from VVV survey data.
problem Classifying RRLyrae stars from a large dataset of light curves.
method Developed an automatic ML-based procedure to identify RRLs, using features like period and intensity, and pseudo-colors.
result Constructed an ensemble classifier with Recall of 0.48 and Precision of 0.86 over 15 tiles.
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely bas…
Extracts high-quality monolingual datasets from web crawl data.
problem Improving text representation quality through larger corpora.
method Automated pipeline using deduplication and language identification, augmented with filtering for high-quality documents.
result Extracted massive high-quality monolingual datasets from Common Crawl.
Novel framework monitors cardiac image segmentation models in real-time.
problem Ensuring continuous high model performance and segmentation results in clinics.
method Formulated as anomaly detection, the framework derives surrogate quality measures for segmentation.
result Demonstrated accurate, fast, and scalable quality control monitoring.
A novel ν-SVQR model for automatic quantile estimation.
problem Quantile estimation with automatic accuracy control.
method Uses ν fraction of training data points and asymmetric ε-insensitive zone. result Automatic control over accuracy achieved through ν-SVQR model. This paper explores how theories of the planning fallacy and the outside view may be used to conduct quality control and due diligence in project management. First, a much-neglected issue in project management is identified, namely that the front-end estimates of costs and benefits--used in the business cases, cost-ben…
Framework for automatically assessing and correcting data quality issues without domain knowledge.
problem Ensuring data quality in datasets across various domains.
method Hybrid approach combining statistical and machine learning methods.
result Effective detection and correction of missing values, duplicates, and typographical errors.
Automated quality control for seismic data reduces human labor and time.
problem Costly and time-consuming manual QC of seismic data.
method Active learning to select and label relevant seismic data.
result Active learning technique reduces QC time and improves accuracy.
Paper proposes personalized climate control for driver comfort.
problem Limited research on in-vehicle climate control and driver preferences.
method IoT platform for data collection, machine learning for driver behavior recognition, and personalized preference recommendation.
result Prototype demonstrates effective and accurate climate control for driver comfort.
A novel method automates quality control of fMRI scans, improving accuracy and generalizability.
problem Lack of automated QC for fMRI scans limits clinical neuroscience research.
method Train machine learning classifiers using runtime log features to predict scan quality.
result Classifiers trained on FLAG-QC features outperform previous methods (AUC=0.79 vs AUC=0.56).
Robot solves Rubik's cube using simulation-trained models.
problem Solving Rubik's cube with a robot hand.
method Automatic domain randomization (ADR) and a custom robot platform.
result Control policies and vision state estimators trained with ADR exhibit improved sim2real transfer.
Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be ins…
CNN improves frame selection for ultrasound elastography.
problem Choosing suitable frames for accurate strain estimation in ultrasound elastography.
method Convolutional Neural Network (CNN) for frame selection.
result CNN selects frames in 5.4 ms for high-quality strain images.
Paper uses CNN to predict process parameters from molten pool data in WLAM.
problem Achieving high quality in WLAM through real-time process control.
method Proposes a multi-modality CNN architecture to predict process parameters from molten pool sensor data.
result Improved prediction performance of multi-modal CNN compared to uni-modal approach.
GP-ALPS automatically selects latent processes for multi-output GPs.
problem Manual selection of latent processes in multi-output GPs is time-consuming and prone to biases.
method Developed a variational inference scheme to automatically choose latent processes.
result Demonstrated suitability of GP-ALPS in preliminary experiments.
Unified AI system for data quality control and governance in regulated environments.
problem Isolated data quality control steps in existing systems.
method AI-driven framework integrating rule-based, statistical, and AI methods.
result Empirical gains in anomaly detection, reduced manual remediation, improved auditability.
SynthBH uses synthetic data to control FDR in multiple testing.
problem Controlling false discovery rate in multiple hypothesis testing.
method SynthBH, a synthetic-powered multiple testing procedure.
result SynthBH guarantees FDR control with synthetic data.
Spectral normalization stabilizes GANs by controlling gradient explosion and vanishing.
problem Stability and sample quality issues in GAN training.
method Spectral normalization controls gradient explosion and vanishing, improving GAN training stability and sample quality.
result Bidirectional Scaled Spectral Normalization (BSSN) outperforms standard spectral normalization in sample quality and training stability.
Existing methods for retrieving k-nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by most existing methods. We devise a new strategy that avoids partitioning the vector space and present a n…
Unified model for audio control and style transfer.
problem Explicit control and style transfer in music generation.
method Diffusion autoencoders for semantic feature extraction, disentanglement using adversarial criterion.
result Model generates audio matching timbre targets with specified structure.