NeuroNER simplifies ANN-based NER for non-experts.
problem Challenging use of ANNs for NER by non-experts.
method Graphical web-based user interface for easy annotation, training, and prediction of entities.
result NeuroNER streamlines NER process for non-expert users.
A user-friendly interface constructs effective background knowledge from ER diagrams.
problem Inefficient construction of background knowledge by domain experts in ILP systems.
method Design of a graphical user interface to interact with Entity Relationship diagrams to construct modes for a probabilistic logic learning system.
result Domain experts can construct effective background knowledge on par with experts using the graphical interface.
Barista simplifies deep neural network design and training.
problem Complexity and error-proneness of editing deep learning frameworks.
method Graphical user interface for Caffe framework.
result Researchers can focus on solving problems without coding.
Benchpress streamlines benchmarking structure learning algorithms for probabilistic models.
problem Benchmarking structure learning algorithms for probabilistic graphical models.
method Snakemake workflow for scalable, reproducible benchmarks.
result Demonstrates applicability in five typical data scenarios.
LIBTwinSVM offers a free library for efficient Twin Support Vector Machines.
problem Large-scale classification problems.
method Efficient implementation of Twin Support Vector Machines.
result Effectiveness demonstrated through benchmarks.
RAMANMETRIX simplifies Raman spectroscopy data analysis.
problem Complexity of Raman spectroscopy data processing.
method User-friendly software with chemometric analysis workflow.
result Facilitates integration of Raman spectroscopy into clinical routines.
stCEG models spatial events using Chain Event Graphs in R.
problem Modeling events over spatial areas.
method Full specification of CEG models from data, interactive plots, web GUI.
result First software for CEGs with full model customisation.
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
problem Reducing the cost of user interface experimentation in online retail.
method Modeling user interface experimentation as an opportunistic bandit problem, reducing the cost of exploration.
result Significant regret reduction and improved contextual information for testing.
Study creates web interface to elicit user-preferred metrics.
problem Eliciting classification metrics that align with user preferences.
method Developed a web-based interface and conducted a user study.
result Users preferred metrics that align with their task and context.
Agents learn user preferences with less explicit feedback via spatial interface valuing.
problem Learning user preferences with high cognitive load feedback.
method Spatial Interface Valuing for reduced explicit feedback.
result Agents learn faster with spatial interface valuing compared to explicit feedback.
TODS automates time series outlier detection with customizable pipelines.
problem Automated detection of outliers in time series data.
method Modular system with 70 primitives for data processing, time series analysis, and detection algorithms. GUI and data-driven searcher for pipeline design.
result Automated discovery and construction of effective outlier detection pipelines.
Seq2Seq models perform well in generating If-Then programs from natural language.
problem Creating If-Then programs for business process automation without technical expertise.
method Modeling If-Then programs as a sequence learning task using Seq2Seq approaches.
result Seq2Seq models can effectively generate If-Then programs from natural language.
RNNs improve predictive precompute for faster user interface response times.
problem Improving user interface response times in mobile and web applications.
method Application of recurrent neural networks (RNNs) for predictive precompute.
result RNN models improve prediction accuracy and reduce computational cost.
This chapter bridges the gap between expert and lay users in explainable AI.
problem Lack of user-friendly explanations in deep learning models.
method Analysis of user concerns, taxonomy of explanation methods, and evaluation of adequacy.
result Explanation methods are inadequate for lay users and address bias and unfair outcomes poorly.
New interface explains contextual bandits to non-experts.
problem Interpreting and managing contextual bandits for non-expert operators.
method Developed a metric 'value gain' for off-policy evaluation and designed an interface to explain bandit behavior.
result Empowered non-experts to manage complex machine learning systems through accessible presentation.
Graphical Lasso algorithm segments occupants' energy usage behaviors in HC-CP systems.
problem Improving sustainability and energy efficiency in HC-CP systems.
method Introduced a gamification framework and applied Graphical Lasso for energy usage segmentation.
result Characterized different energy usage behaviors in HC-CP systems.
New model generates code from GUI screenshots using attention and hierarchical structure.
problem Challenges in converting abstract image features to code.
method Attention-based hierarchical decoder trained end-to-end.
result Model outperforms state-of-the-art methods on GUI-code datasets.
New BMI interface stabilizes user experience over long periods.
problem Inconsistent neural signal data leads to interface recalibration.
method Adversarial Domain Adaptation Network to match residual distributions.
result Adversarial Domain Adaptation Network outperforms other methods.
OrbTouch uses deep learning to interpret touch gestures on stretchable surfaces.
problem Efficiently extracting information from soft, deformable interfaces.
method Convolutional neural networks applied to stretchable capacitor signals.
result Successfully mapped human touch gestures to categorical labels and touch location.
This study creates a new conceptual framework for news aggregation.
problem Aggregating and presenting diverse news sources in a unified and accessible way.
method Developed a mobile app that analyzes unstructured data patterns to create a conceptual framework for news.
result Users can easily find and navigate through updated news using a new conceptual multilevel structure.
SimTensor generates synthetic tensor data for research.
problem Reproducible research on tensor factorization algorithms.
method Multi-platform software for generating artificial tensor data with various configurations.
result Generates temporal tensors with periodic waves, seasonal effects, and streaming structure.
NIMFA is a Python library for nonnegative matrix factorization.
problem Efficiently factorizing nonnegative matrices for various applications.
method Unified interface, state-of-the-art methods, initialization approaches, quality scoring, supports dense and sparse matrices.
result Unified and efficient implementation of nonnegative matrix factorization methods.
CrystalCandle creates user-friendly explanations for machine learning models.
problem Low trust in predictive models due to lack of interpretability.
method End-to-end pipeline for model interpretation, including Model Importer, Interpreter, Narrative Generator, and Exporter.
result CrystalCandle leads to higher adoption rates and improved downstream metrics.
A new method selects important variables for clustering from dependency networks.
problem Variable selection for clustering in high-cost data scenarios.
method Create dependency networks, rank variables by centrality, select top-n variables.
result Top-n variables improve clustering performance compared to existing methods.
Regina is a software package for studying 3-manifold triangulations and normal surfaces. It includes a graphical user interface and Python bindings, and also supports angle structures, census enumeration, combinatorial recognition of triangulations, and high-level functions such as 3-sphere recognition, unknot recognit…
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly…
Automates UI implementation from designer images.
problem Automating UI implementation from designer images.
method Generative model training and imitation learning.
result 92.5% accuracy on Android Button attribute inference.
Method tracks finger movements to render shapes on display devices.
problem Designing touchless user interfaces for electronic devices.
method Leap Motion controller tracks finger movements, analyzes trajectories, and uses HMM for gesture recognition.
result Method achieves 92.87% accuracy in rendering shapes on display devices.
New method tests independence and learns graphs without manual choices.
problem Testing independence of multivariate random variables is hard.
method Link between independence testing and supervised learning.
result Predictive independence tests outperform current methods.
Research maps body movements to sound using advanced algorithms.
problem Detect and classify user commands with minimal latency.
method Vector Autoregressive Hierarchical Hidden Markov Models (VAR-HHMM) with wireless sensor nodes.
result VAR-HHMM algorithm outperforms K-nearest neighbors in detection and classification.
Open-source Vizier optimizes complex systems for Google and beyond.
problem Optimizing large-scale systems with multiple objectives and constraints.
method Distributed, fault-tolerant, flexible API for blackbox optimization.
result OSS Vizier supports a wide range of optimization problems and is available as open-source.
Interactive interface for specifying complex tasks through demonstrations.
problem Challenges in specifying high-dimensional reward functions for complex tasks.
method Interactive interface with a set of increasingly complex policies trained on demonstrations.
result Successfully learned a complex task (Lunar Lander) with interactive demonstrations.
Proposes new feature transformation methods for brain interface models.
problem Sub-optimality of feature ranking and selection in brain interface models.
method Introduces maximum mutual information linear and nonlinear transformations.
result Significantly better performance in binary and multi-class decoding analyses.
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recomme…
A novel method visualizes higher-dimensional spaces using hyperbolic geometry.
problem Challenges in visualizing higher-dimensional spaces.
method Interactive visualization of higher-dimensional grids based on hyperbolic geometry.
result Our method shows the whole higher-dimensional space at once and avoids disadvantages of previous methods.
Model extraction attacks exploit MLaaS query interfaces to steal models.
problem Model extraction attacks compromise MLaaS systems by stealing models through query interfaces.
method Formalized model extraction, compared it to active learning, and investigated defense strategies.
result Recent active learning advancements can be used to implement powerful model extraction attacks.
OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions are imposed on the factor graph to allow for higher-order factors and arbitrary neighborhood structures. Large models with repetitive struc…
Developed a robust classification framework for swimming paths in Morris Water Maze.
problem Limited use of classification techniques in Morris Water Maze studies.
method Majority voting to boost classification performance and nullify manual tuning.
result Generalised and robust classification methodology implemented successfully.
GAMA is a user-friendly AutoML system for machine learning pipeline optimization.
problem Empowering users to control and optimize machine learning pipelines.
method Modular AutoML system with three search algorithms and two post-processing steps.
result GAMA allows users to track and control the search process for optimal machine learning pipelines.
Interactive learning explained to users improves trust and model understanding.
problem Lack of user understanding and trust in interactive learning models.
method Proposes a framework where learners explain interactive queries and predictions to users, using visual explanations.
result Boosts predictive and explanatory powers of and user trust in learned models.
Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learnin…
CHOPT optimizes hyperparameters in cloud ML platforms efficiently.
problem Efficient hyperparameter optimization in cloud-based machine learning platforms.
method Cloud-based HyperOpt framework with web interfaces and analysis tools.
result CHOPT finds hyperparameter configurations competitive with previous work.
New method steers protein design towards desired properties.
problem Challenges in designing proteins with specific structures and properties.
method Feynman-Kac framework applied to RFdiffusion models with guiding potentials.
result Significant improvement in predicted interface energetics and binder designability.
Users create melodies with AI, harmonized in the style of Bach.
problem Making music composition accessible to non-experts.
method Simplified interface, machine learning model Coconet, optimized for web.
result Users spent 350 years worth of time playing with the Bach Doodle.
A method to prevent overfitting by modeling user knowledge in interactive machine learning.
problem Overfitting due to user reinforcement of noisy patterns in training data.
method User modelling based on rational behavior to correct overfitting.
result Improves predictive performance in sentiment analysis tasks.
Framework for interactive learning to minimize user experience regressions.
problem Sub-optimal user experiences due to frequent exploration of options.
method Explore-Exploit framework for online learning operators.
result Efficiencies achieved in integrating online learning with run-time services.
Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decom…
Flexible Cox model for time-dependent covariates with complex sparsity patterns.
problem Lack of flexibility in enforcing specific sparsity patterns in time-dependent Cox models.
method Proposes a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules.
result Achieves accurate estimation with low false alarm rates for complex covariate structures.