Import2vec creates embeddings for software libraries to improve learning tasks.
problem Developing semantic representations for software libraries.
method Applied word embedding techniques from NLP to library packages.
result Library vectors capture meaningful relationships among libraries.
CSLVAE generates large chemical libraries efficiently.
problem Navigating ultra-large combinatorial synthesis libraries.
method Hierarchically-organized database with molecular encoder and decoder.
result Generates valid molecular graphs without autoregression.
MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript.…
AITuning uses reinforcement learning to optimize communication libraries.
problem Optimizing communication libraries for better performance in parallel applications.
method Deep reinforcement learning applied to tuning communication libraries.
result Demonstrates the effectiveness of reinforcement learning for tuning communication libraries.
MRCpy implements minimax risk classifiers with performance guarantees and distribution shift adaptability.
problem Classical risk minimization approaches are not robust to distribution shifts.
method Robust risk minimization approach for minimax risk classifiers.
result MRCs provide performance guarantees and adapt to distribution shifts.
Study analyzes tweets from Northeastern libraries to understand public relations efforts.
problem Analyzing vast social media data for public relations.
method Computational methods to extract and analyze tweets.
result Libraries use Twitter to represent services and disseminate information.
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.
TrueLearn Python library for personalized educational recommendations.
problem Building educational recommendation systems with humanly-intuitive user representations.
method Online learning Bayesian models and open learner concept.
result Library includes models and representations for user control and interpretability.
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 …
Fast and flexible library for Bayesian optimization.
problem Optimizing expensive, gradient-less functions.
method Highly flexible and fast C++11 library.
result 2 times faster than BayesOpt for similar accuracy.
Modified BFGS and LBFGS++ libraries boost performance for non-parallelizable functions.
problem Improving performance of non-parallelizable functions using SIMD and AAD.
method Modifications to BFGS and LBFGS++ libraries, utilizing SIMD and Automatic Differentiation (AAD).
result Up to 3.8 times faster for European Swaption curve calibration and 1.4 times faster for LMM model calibration.
Automatically extracts hyperparameter schemas from AI library documentation.
problem Lack of machine-readable hyperparameter schemas for AI automation tools.
method Automatically mines Python docstrings in AI libraries to extract JSON Schemas.
result Effective at extracting machine-readable schemas from 119 AI models.
FCL3 learns from multiple representative models to adapt to new tasks.
problem Degradation of performance in lifelong learning models for new task environments.
method Two knowledge libraries: feature learning and model knowledge. Self-selection of representative models for new tasks.
result FCL3 achieves better performance than most lifelong learning frameworks.
Python library for conformal prediction, licensed under MIT.
problem Improving prediction accuracy with uncertainty quantification.
method Conformal prediction framework implemented in Python.
result Stable API and algorithms for conformal prediction.
L2SC improves spectral clustering performance by selectively transferring knowledge across tasks.
problem L2SC tackles the challenge of incorporating new spectral clustering tasks without relearning all previous tasks.
method L2SC uses an orthogonal basis library and feature embedding library to selectively transfer knowledge from previously learned tasks to new tasks.
result L2SC outperforms state-of-the-art spectral clustering algorithms on real-world benchmark datasets.
BayesDLL offers a PyTorch library for Bayesian deep learning with large models.
problem Bayesian inference for large-scale deep networks.
method Variational inference, MC-dropout, stochastic-gradient MCMC, Laplace approximation.
result BayesDLL can handle Vision Transformers and pre-trained model weights as priors.
This study analyzes Twitter posts from US west coast libraries.
problem Lack of automated methods to analyze thousands of library tweets.
method Used Twitter API to collect and analyze 138,000 tweets.
result Identified 20 topics in library tweets, categorized into public relations, book, event, training, and social good.
Kernelmethods library simplifies kernel-based ML in Python.
problem Lack of a well-defined Python library for kernel-based ML.
method Develops a Python library that supports various kernel types and customizations.
result Enables efficient, modular, and interoperable kernel-based operations.
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.
Auto-Surprise automates recommender system selection and optimization.
problem Finding the best algorithm and hyperparameters for recommender systems.
method Extends Surprise library with TPE optimization for algorithm selection and hyperparameter tuning.
result Significantly faster in finding optimal hyperparameters compared to grid search.
Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we g…
Captum library simplifies model interpretability for PyTorch.
problem Improving model interpretability for various PyTorch models.
method Unified, open-source library with generic attribution algorithms and metrics.
result Unified and scalable model interpretability for multimodal inputs.
Disentanglement-PyTorch library facilitates disentangled representation learning.
problem Unsupervised learning of disentangled representations.
method Modular library for variational algorithms, decoupling architectures, latent space, and training algorithms.
result Achieved 3rd rank in NeurIPS 2019 Disentanglement Challenge.
Picasso is a new library for sparse learning problems in R and Python.
problem Sparse learning problems in high-dimensional data analysis.
method Unified framework of pathwise coordinate optimization with efficient active set selection strategies.
result picasso can efficiently handle large-scale problems.
Python library for boosting statistical relational models.
problem Expressing learning and inference problems in statistical relational models.
method Adapting scikit-learn interface for boosted statistical relational models.
result Provides examples for using srlearn.
CleverHans library standardizes adversarial example construction for machine learning model robustness.
problem Inconsistent benchmarks due to varying implementations of adversarial example construction.
method Provides standardized reference implementations of adversarial example construction techniques and defenses.
result Standardized benchmarks improve comparability and model robustness evaluation.
DLOPT simplifies deep learning hyper-parameter optimization.
problem Finding optimal network configurations in deep learning.
method Introduces a novel library for hyper-parameter optimization.
result Demonstrates effectiveness through use examples.
abess efficiently solves various machine learning problems quickly.
problem Efficiently solving diverse machine learning problems.
method Unified framework of best-subset selection, polynomial time optimal solution.
result Certifiably gets the optimal solution within polynomial times with high probability.
LibAUC optimizes X-risks for AI tasks like CID, LTR, and CLR.
problem Optimizing risk functions in AI for tasks like classification, ranking, and representation learning.
method Developed a new mini-batch pipeline for deep X-risk optimization (DXO) algorithms.
result Achieved great success in solving CID, LTR, and CLR tasks with faster convergence and scalable performance.
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.
GPflux simplifies deep Gaussian processes for Python.
problem Challenges in implementing deep Gaussian processes.
method Python library for Bayesian deep learning with DGPs.
result Efficient, modular, and extensible library for DGPs.
Robots learn movement libraries by segmenting complex trajectories.
problem Segmenting complex robot movement demonstrations for library building.
method Model trajectories as Switching Linear Dynamical Systems and infer segmentation using a nonparametric Bayesian approach.
result Robots can learn movement libraries more effectively by segmenting demonstrations.
FedML aims to improve FL research by providing a library and benchmark.
problem Inconsistent FL algorithm development and performance comparison.
method FedML offers an open research library and benchmark supporting diverse computing paradigms and flexible API design.
result FedML facilitates fair algorithm comparison and development in federated learning.
UncertaintyPlayground simplifies uncertainty estimation in Python.
problem Uncertainty estimation in supervised learning tasks.
method Sparse and Variational Gaussian Process Regressions for normally distributed outcomes, Mixed Density Networks for mixed distributions.
result Fast and simplified uncertainty estimation through Python library.
Neural network predicts electron-ionization mass spectra quickly.
problem Identifying unknown molecules not in existing libraries.
method Lightweight neural network model for predicting mass spectra.
result High accuracy predictions of small molecule mass spectra.
LIBS2ML is a fast, scalable library for second order machine learning.
problem Large-scale machine learning problems in big data.
method Scalable second order learning algorithms using MEX files.
result Efficient solution for large-scale learning problems.
ChainerRL is a deep reinforcement learning library for Python.
problem Training and reproducing deep reinforcement learning algorithms.
method Built on Chainer framework, implements various DRL algorithms.
result Fosters reproducible research and replicates benchmark results.
PettingZoo library accelerates multi-agent reinforcement learning research.
problem Challenges in multi-agent reinforcement learning, especially conceptual models of games.
method Developed PettingZoo library with AEC games model to address multi-agent reinforcement learning challenges.
result AEC games model addresses conceptual issues in multi-agent reinforcement learning environments.
Tick is a Python library for fast time-dependent statistical modeling.
problem Efficient time-dependent statistical modeling.
method Optimization module with C++ implementation and state-of-the-art solvers.
result Very fast computations in a multi-core setting.
ParaMonte simplifies Monte Carlo simulations for various scientific fields.
problem Efficiently performing Monte Carlo simulations for complex models.
method Unified, high-performance, parallelized library for C, C++, Fortran.
result Automates and streamlines Monte Carlo sampling for arbitrary-dimensional functions.
Library learns Bayesian networks from mixed data without discretization.
problem Learning Bayesian networks from mixed data (discrete and continuous variables).
method Proposes an algorithm for structural and parameter learning of Bayesian networks from mixed data using a mixed MI score function and Gaussian approximation. Offers two graph structure enumeration algorithms.
result Advantages in solving approximation and gap recovery problems on synthetic and real datasets.
torchbearer simplifies deep learning model fitting in PyTorch.
problem Simplifying model fitting for researchers in deep learning.
method High-level metric and callback API for model persistence, learning rate decay, logging, and data visualization.
result Extensive documentation and built-in callbacks for various applications.
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
problem Kernel learning and model selection
method GPU acceleration
result Competitive predictive performance with speedups
ZhuSuan is a Python library for Bayesian deep learning.
problem Bayesian deep learning for probabilistic models.
method Bayesian inference, probabilistic programming, Tensorflow.
result Supports various probabilistic models including hierarchical and deep generative models.
Pythae is a Python library for benchmarking VAE models.
problem Improving variational autoencoders for various tasks.
method Unified implementation and framework for 19 generative autoencoder models.
result Benchmarking 19 VAE models across multiple tasks.
EagerPy simplifies writing code for multiple deep learning frameworks.
problem Code duplication and framework lock-in when using different deep learning libraries.
method Integrates multiple frameworks into a single Python framework.
result Automatic compatibility across PyTorch, TensorFlow, JAX, and NumPy.
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.
GP+ is a Python library for Gaussian process learning.
problem Probabilistic learning and inference for complex models.
method Integration of nonlinear manifold learning with Gaussian processes.
result GP+ enables probabilistic data fusion and inverse parameter estimation.