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 new ABC method simplifies Bayesian inference for complex models.
problem Computational difficulty in Bayesian inference for models without analytical likelihoods.
method Empirical likelihood ABC method that requires only summary statistics and simulation.
result The posterior obtained is consistent and performs well across various examples.
IllinoisSL is a Java library for learning structured prediction models. It supports structured Support Vector Machines and structured Perceptron. The library consists of a core learning module and several applications, which can be executed from command-lines. Documentation is provided to guide users. In Comparison to …
Agent-based models have demonstrated their power and flexibility in Econophysics. However their major challenge is still to devise more realistic simulation scenarios. The complexity of Economy makes appealing the idea of introducing chaotic number generators as simulation engines in these models. Chaos based number ge…
Approximate Incremental Value-at-Risk formulae provide an easy-to-use preliminary guideline for risk allocation. Both the cases of risk adding and risk pooling are examined and beta-based formulae achieved. Results highlight how much the conditions for adding new risky positions are stronger than those required for ris…
GraSPy simplifies graph analysis in Python.
problem Analyzing and understanding graphs.
method Scikit-learn compliant API for statistical inference and machine learning.
result Flexible algorithms for graph statistics.
hyppo simplifies multivariate hypothesis testing in Python.
problem Inconsistent multivariate hypothesis testing interfaces in Python.
method Unified library for multivariate testing procedures.
result Easy-to-use and flexible for future extensions.
We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. G-CNNs use G-convolutions, a new type of layer that enjoys a substantially higher degree of weight sharing than regular convolution la…
A software library for constructing and learning probabilistic models is presented. The library offers a set of building blocks from which a large variety of static and dynamic models can be built. These include hierarchical models for variances of other variables and many nonlinear models. The underlying variational B…
Paper simplifies Gittins indices calculation for bandits.
problem Difficulty in calculating Gittins indices for multi-armed bandits.
method Accessible general methodology for calculating Gittins indices.
result Removes computation barrier for Gittins indices.
This paper studies the performance of a recently proposed preconditioned stochastic gradient descent (PSGD) algorithm on recurrent neural network (RNN) training. PSGD adaptively estimates a preconditioner to accelerate gradient descent, and is designed to be simple, general and easy to use, as stochastic gradient desce…
DeepRobust is a PyTorch library for adversarial attacks and defenses.
problem Addressing adversarial attacks and defenses in deep learning.
method Contains over 20 attack and defense algorithms for various deep learning architectures.
result Fosters research in adversarial learning with comprehensive and easy-to-use platform.
pystacked combines machine learning models for improved predictions.
problem Improving machine learning model performance through stacking.
method Stacked generalization using Python's scikit-learn with various base learners.
result Enhanced predictive models through combining multiple machine learning algorithms.
jLDADMM offers Java tools for topic modeling on texts.
problem Topic modeling on texts, especially short texts.
method Collaborated Gibbs sampling for LDA and DMM models.
result Alternative topic modeling solutions for normal or short texts.
Kymatio simplifies scattering transforms for Python.
problem Signal processing and machine learning applications.
method Wavelet scattering transform implemented in Python.
result Efficient, GPU-accelerated implementation.
DeepPurpose simplifies DL for DTI prediction.
problem Accurate prediction of drug-target interactions.
method Comprehensive deep learning library with 15 compound and protein encoders and 50 neural architectures.
result State-of-the-art performance on benchmark datasets.
New models avoid lookahead bias by training on past data only.
problem Lookahead bias in language models.
method Chronologically consistent training on data before a knowledge-cutoff date.
result Elimination of lookahead bias in predictions.
Python library for causal discovery from observational data.
problem Revealing causal relations from observational data.
method Comprehensive collection of causal discovery methods in Python.
result Ease of use for non-specialists and modular building blocks for developers.
DynamicGEM learns node representations for evolving graphs.
problem Learning node representations for dynamic graphs.
method State-of-the-art algorithms for dynamic graph embedding.
result Evaluation framework for various downstream tasks.
This article reviews machine learning methods for behavioral sciences.
problem Applying machine learning to behavioral science data.
method Overview of classical and recent algorithms for tabular, behavioral, and textual data.
result Practical guidance for using machine learning in behavioral science.
combo library simplifies model combination for various machine learning tasks.
problem Facilitating model combination in machine learning.
method Easy-to-use Python toolkit for aggregating models and scores.
result Unified and consistent way to combine models from multiple libraries.
Improves neural semantic parsers using human feedback.
problem Improving neural semantic parsers through human feedback.
method Counterfactual learning from human bandit feedback, reweighting estimator, stochastic gradient optimization.
result Significant improvement in semantic parsers achieved.
SNAP improves robust computation by emphasizing trustworthy items and downweighting outliers.
problem Improving robustness in computation, especially in high-dimensional settings.
method SNAP assigns weights based on mutual agreement, suppressing outlier contributions.
result SNAP ensures outliers contribute negligibly to computations, even in high-dimensional settings.
Bayesian MS-VAR process improves option pricing models.
problem Improving option pricing models for better accuracy.
method Bayesian Markov-Switching Vector Autoregressive (MS-BVAR) process with risk-neutral valuation.
result Derived pricing formulas for various options.
Python tool assesses European agricultural production resilience.
problem Estimating resilience of European agricultural production systems.
method Python software to compute annual production resilience indicator.
result Demonstrates resilience of European agricultural production.
A new imputation method MissARF uses adversarial random forests for fast and accurate missing value imputation.
problem Handling missing values in biostatistical analyses.
method Adversarial Random Forests (ARF) for density estimation and data synthesis.
result MissARF performs comparably to state-of-the-art methods in imputation quality and runtime.
R package psvmSDR simplifies SDR computation for machine learning.
problem Sufficient dimension reduction in machine learning.
method Principal machine (PM) generalized from PSVM.
result Efficient computation of SDR estimators in various scenarios.
HIVE-COTE v1.0 improves time series classification with enhanced usability.
problem Improving time series classification accuracy and usability.
method Presented a walkthrough guide and extensive experimental evaluation of HIVE-COTE v1.0.
result HIVE-COTE v1.0 outperforms three recently proposed algorithms in predictive performance and resource usage.
SOL is a scalable library for online learning algorithms.
problem Scalable online learning for high-dimensional data.
method Family of regular and sparse online learning algorithms implemented in C++.
result High efficiency and scalability for large-scale machine learning.
ZOOpt toolbox simplifies derivative-free optimization for machine learning tasks.
problem Efficiently solving optimization problems with complex functions.
method Derivative-free solvers, parallel and distributed optimization.
result ZOOpt toolbox simplifies optimization for machine learning tasks.
JAXFit speeds up curve fitting on GPUs.
problem Nonlinear least squares curve fitting problems.
method Trust region method on GPU with automatic differentiation.
result Significantly faster than CPU and other GPU libraries.
New method improves fault detection by adding unsupervised learning to Monte Carlo dropout models.
problem Detecting and diagnosing incipient and unknown faults in deep neural networks.
method Augmenting Monte Carlo dropout models with unsupervised learning tasks.
result Improved fault detection and diagnosis performance, especially on out-of-distribution examples.
Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth is difficult, even for the most experienced clinicians. The most well-designed clinical study thus far reaches a modest sensitivity of 18.2-…
A CBR approach helps fraud analysts trust machine learning predictions.
problem Understanding the trustworthiness of machine learning predictions for fraud analysts.
method Case-based reasoning (CBR) approach to visualize similar previous instances and their local post-hoc explanations.
result Empirically, the visualization of similar previous instances is useful and easy to use for fraud analysts.
Lumbermark clusters data robustly, slicing limbs of mutual reachability trees.
problem Detecting clusters of varying sizes, densities, and shapes in datasets.
method Iteratively chops limbs of a mutual reachability minimum spanning tree.
result Lumbermark produces partitions with user-specified sizes.
OpenML provides standardized machine learning benchmarks.
problem Objective, comparable, reproducible benchmarks for machine learning.
method Curated benchmarking suites with standardized data formats and APIs.
result OpenML-CC18 benchmark suite for classification.
Benchmark improves object detection robustness in winter weather.
problem Assessing object detection models' performance under image corruptions.
method Developed three benchmark datasets with various image corruptions; used data augmentation to improve robustness.
result Simple data augmentation significantly enhances model robustness across different corruptions and datasets.
Machine learning improves neural decoding performance.
problem Traditional neural decoding methods are inefficient.
method Apply modern machine learning algorithms (neural networks, gradient boosting) for neural decoding.
result Modern methods significantly outperform traditional approaches.
The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possi…
Robust Lasso-Zero handles missing covariates and sparse corruptions.
problem Sparse corruptions and missing covariates in sparse linear models.
method Extension of Lasso-Zero to handle sparse corruptions, with theoretical guarantees on sign recovery.
result Robust Lasso-Zero can handle missing values without specifying a parametric model.
Google Research Football: A new 3D physics-based game for reinforcement learning.
problem Training reinforcement learning algorithms in complex, realistic environments.
method Developed a new 3D physics-based football simulator environment.
result Reported baseline results for various reinforcement algorithms.
Paper proposes transparent reporting of algorithmic energy usage to promote environmental sustainability.
problem Need for transparent reporting of algorithmic energy usage for environmental sustainability.
method Developed a Python package to make analyses of energy usage accessible to individual researchers, localized to specific power grids, and compared with global benchmarks.
result Demonstrated the use of automatically-generated Energy Usage Reports in model-choice for machine learning.
The elbow method for choosing k in k-means is unreliable and should be avoided.
problem Choosing the optimal number of clusters k in k-means clustering.
method Alternative methods to the elbow method for selecting k.
result Alternative methods often perform better than the elbow method.
DECOMPOSE improves accuracy and robustness in blind source separation.
problem Extracting sources from large-scale data using blind source separation.
method Probabilistic blind source separation framework (DECOMPOSE) that adapts to individual sources and handles large-scale data.
result Substantial improvements in accuracy and robustness on artificial and real data.
Ensemble decoders to capture latent space topology in deep generative models.
problem Topological mismatch between latent space geometry and data manifolds.
method Using ensembles of decoders to compute geodesics on the expected manifold.
result Ensemble approach provides a simple and reliable way to capture model uncertainty in latent space.
New algorithms improve time series prediction with uncertainty quantification.
problem Uncertainty quantification for time series prediction.
method Combines conformal prediction and control theory for online, adaptive forecasting.
result Improves coverage over ensemble forecasters in real-world applications.
New method visualizes tabular feature semantics for better model understanding.
problem Lack of feature interaction interpretation in tabular ML models.
method Feature Vectors method for global tabular dataset interpretability.
result Visualizes semantic relationships among tabular features.
QuASE uses QA data to improve non-QA tasks like NER.
problem Can QA data help other NLP tasks?
method Proposes QuASE framework to learn sentence encodings from QA data.
result QuASE encodings improve downstream tasks like NER.