Toolkit simplifies transfer learning for researchers.
problem Making transfer learning accessible to non-experts.
method Wraps 17 models, provides easy-to-use interfaces.
result Simplifies model selection for real-world applications.
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48 results for “R toolkit”
This paper presents an open-source enforcement learning toolkit named CytonRL (https://github.com/arthurxlw/cytonRL). The toolkit implements four recent advanced deep Q-learning algorithms from scratch using C++ and NVIDIA's GPU-accelerated libraries. The code is simple and elegant, owing to an open-source general-purp…
Paper reviews and synthesizes methods for evaluating dimensionality reduction techniques.
problem Evaluating and comparing dimensionality reduction techniques.
method Framework and toolkit in R for exploring and evaluating dimensionality reduction quality through visual insights.
result Helps researchers compare and select dimensionality reduction techniques using visual insights.
A toolkit for path-norms enhances neural network generalization bounds.
problem Establishing generalization bounds for modern neural networks.
method Introducing a comprehensive toolkit for path-norms in ReLU networks with various operations.
result Established generalization bounds for modern neural networks that are the most widely applicable and recover/beat the sharpest known bounds.
Toolkit evaluates causal inference models for real-world data.
problem Validating causal inference models for observational data.
method Developed a comprehensive evaluation toolkit for machine learning models in causal inference.
result Improves reproducibility and ensures data extraction criteria drive model selection.
Toolkit and taxonomy for diverse AI explainability methods.
problem Diverse stakeholder needs for AI explanations.
method Open-source software toolkit with eight explainability methods and evaluation metrics.
result Taxonomy helps navigate explanation methods.
We describe Sockeye (version 1.12), an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNet, the toolkit offers scalable training a…
sktime toolkit benchmarks time series classification algorithms for correctness and efficiency.
problem Benchmarking correctness and efficiency of time series classification algorithms.
method Implementation and comparison of six classifiers in sktime with their tsml equivalents.
result Significant differences in accuracy and efficiency between algorithms, with one causing debugging issues.
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields. Among various point process models, Hawkes process and its variants attract many researchers in statistics and computer science these years because they capt…
Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative to toolkits that operate on statically declared computations (e.g., TensorFlow, CNTK, and Theano). However, existing toolkits - both static…
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then exampl…
New toolkit for directed distances improves flexibility of OT problems.
problem Optimal transport problems with constraints.
method Directed distances between quantile functions.
result Flexibility in solving OT problems enhanced.
Flexible tree ensemble learning framework supports arbitrary loss functions and multi-task learning.
problem Limited modeling capabilities of existing tree ensemble learning toolkits.
method Differentiable tree ensembles with tensor-based formulation for efficient training.
result Our framework leads to 100x more compact and 23% more expressive tree ensembles.
CAT is a new ASR toolkit using CRF and CTC for state-of-the-art speech recognition.
problem Improving automatic speech recognition systems.
method CRF-based discriminative training with CTC-inspired state topology.
result CAT achieves state-of-the-art results with fewer parameters and is competitive with hybrid models.
CAT toolkit combines hybrid and E2E approaches for efficient speech recognition.
problem Improving speech recognition efficiency and latency.
method CTC-CRF based framework with contextualized soft forgetting.
result CAT achieves state-of-the-art results with simpler training and streaming ASR.
GluonCV and GluonNLP simplify deep learning for CV and NLP.
problem Lack of easy-to-use deep learning tools for CV and NLP.
method Developed modular APIs and pre-trained models for rapid prototyping.
result Facilitates rapid prototyping and reproducible research in CV and NLP.
Python toolkit for symbolic Poisson geometry calculations.
problem Computing Poisson-Nijenhuis structures on manifolds.
method Symbolic algorithms implemented in Python.
result Examples of gauge transformations and parametric bivector fields.
Sparklen is a Python toolkit for high-dimensional Hawkes processes.
problem Efficiently analyzing high-dimensional Hawkes processes.
method Combines Python for ease of use with C++ for performance.
result Provides state-of-the-art tools for estimating and classifying Hawkes processes.
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.
MOGPTK simplifies multi-channel data modeling with Gaussian processes.
problem Modeling multi-channel data efficiently and accurately.
method Python package with TensorFlow backend, supporting various GP kernels and parameter initialization strategies.
result Enables GPU-accelerated training and comprehensive GP modeling pipeline.
TensorFlow Probability MCMC toolkit improves MCMC efficiency for modern hardware.
problem Inefficient MCMC algorithms on modern hardware.
method Design and implementation of a new MCMC toolkit for TensorFlow.
result Improved MCMC efficiency on modern hardware.
HuSpaCy offers an industrial-grade Hungarian NLP toolkit.
problem Lack of suitable open-source Hungarian NLP pipelines.
method Built on spaCy, HuSpaCy includes lemmatization, morphosyntactic analysis, entity recognition, and word embeddings.
result HuSpaCy achieves high accuracy with resource-efficient prediction.
OMLT combines ML and optimization for solving complex problems.
problem Solving complex decision-making problems in computer science and engineering.
method OMLT integrates neural networks and gradient-boosted trees into optimization problems using machine learning.
result OMLT seamlessly integrates with Pyomo and solves real-world problems.
The change of two orders of magnitude in the 'new DCF' of NIST's SRE'10, relative to the 'old DCF' evaluation criterion, posed a difficult challenge for participants and evaluator alike. Initially, participants were at a loss as to how to calibrate their systems, while the evaluator underestimated the required number o…
This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a framework of convex (or loss) functions, convex optimization algorithms (including Gradient Descent, L-BFGS, Stochastic Gradient Descent, Conjugate Gradient, etc.), …
State evolution beyond first-order methods I: Rigorous predictions and finite-sample guaranteesmath.ST
New method predicts state evolution for non-first-order algorithms on nonconvex problems.
problem Analyzing nonconvex optimization problems with random data.
method Developed a state evolution for a broader class of algorithms including first-order and saddle point updates.
result Established rigorous state evolution predictions and finite-sample guarantees for non-first-order methods.
Paper identifies a shared toolkit of strategies for risk management across fields.
problem Uncertainty and risk management in various fields.
method Systematic identification and categorization of 110 strategies.
result RDOT: Risk-reducing Design and Operations Toolkit provides versatile responses to uncertainty.
Unified toolkit for comparing neural representations using SRTD and NTS.
problem Heuristic asymmetry and unbounded scores in existing divergences.
method Developed SRTD and NTS to address these issues.
result Unified, robust, and scale-invariant metric for comparing neural representations.
Software tool enhances sampling efficiency in neural networks.
problem Efficiently sampling from posterior distributions in machine learning models.
method Ensemble quasi-Newton preconditioning method for Langevin dynamics.
result Significant improvement in sampling efficiency demonstrated.
Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments. However, many of the existing environments provide either unrealistic visuals, inaccurate physics, low task complexity, restricted agent perspective, or a limited capacity for intera…
MusPy is a toolkit for symbolic music generation, providing tools for dataset management and analysis.
problem Facilitating the creation and analysis of symbolic music datasets.
method Development of an open-source Python library (MusPy) with features for dataset management, data I/O, preprocessing, and model evaluation. Demonstrated through statistical analysis and cross-dataset generalizability experiments.
result MusPy's dataset analysis reveals varying degrees of cross-genre representation across different music datasets.
FANN-on-MCU enables efficient neural network inference on IoT devices.
problem Energy-efficient neural network inference on resource-constrained IoT devices.
method Open-source toolkit for ARM Cortex-M and RISC-V microcontrollers.
result Efficient neural network execution with low latency and power consumption.
New method improves reliability of object detection models.
problem Lack of reliability in neural networks for safety-critical applications.
method Sequential Conformal Risk Control (SeqCRC) for object detection.
result Valid statistical guarantees for any dataset size without prior knowledge.
DPpack offers R tools for private data analysis and machine learning.
problem Ensuring privacy in statistical analysis and machine learning.
method Differential privacy mechanisms (Laplace, Gaussian, exponential).
result User-friendly implementation of privacy-preserving models.
VerifAI toolkit improves neural network-based aircraft taxiing system safety.
problem Improving safety of autonomous aircraft taxiing systems using neural networks.
method Unified approach to formal analysis and retraining of AI systems, including falsification, debugging, and retraining.
result Improved neural network performance and reduced failure cases in aircraft taxiing system.
tsflex speeds up time series processing and feature extraction.
problem Inefficient and inflexible time series processing tools.
method Flexible Python toolkit for multivariate, asynchronous time series.
result Significantly faster and more memory-efficient than existing packages.
We describe the connected components of the complement of a natural "diagonal" of real codimension 1 in a stratum of quadratic differentials on CP1. We establish a natural bijection between the set of these connected components and the set of generic configurations that appear on such "flat spheres". We also prove that…
Topological data analysis and its main method, persistent homology, provide a toolkit for computing topological information of high-dimensional and noisy data sets. Kernels for one-parameter persistent homology have been established to connect persistent homology with machine learning techniques. We contribute a kernel…
Autoencoder identifies cancer cells from normal ones using gene expression data.
problem Distinguishing between normal and cancer cells using gene expression profiles.
method Autoencoder trained on large tumor dataset to capture latent representations, using HPC toolkit for efficiency.
result Autoencoder node saliency identifies key differentiating features between normal and cancer cells.
In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomi…
ADMM solves constrained CASH problems by breaking them into smaller, manageable pieces.
problem Handling black-box constraints in CASH problems.
method Leverages ADMM optimization framework to decompose CASH problems.
result ADMM facilitates incorporation of black-box constraints.
Comprisk simplifies competing-risks analysis in Python.
problem Analyzing medical time-to-event data with competing risks.
method A scikit-learn-compatible toolkit for competing-risks survival analysis.
result Comprisk provides a unified API for various competing-risks methods.
Sparse linear models improve neural network debuggability.
problem Improving neural network interpretability and debugging.
method Using sparse linear models over learned deep feature representations.
result The approach leads to more debuggable and accurate neural networks.
This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using o…
Interactive tools help teach economics online.
problem Teaching macroeconomic models online.
method Browser-based simulation toolkits.
result Intuitive approach to comparative statics.
This dissertation investigates the use of one-sided classification algorithms in the application of separating hazardous chlorinated solvents from other materials, based on their Raman spectra. The experimentation is carried out using a new one-sided classification toolkit that was designed and developed from the groun…
Python library for integrating TDA with machine learning.
problem Data exploration and interpretability in machine learning.
method Integrates TDA with scikit-learn API, uses C++ for performance.
result Enhanced data exploration and interpretability in machine learning.
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithmsstat.CO
The -regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communities. Recent work has shown that modern mixed integer optimization (MIO) solvers can be used to addre…