OML-AD detects anomalies in non-stationary time series data.
problem Anomaly detection in non-stationary time series data.
method Online machine learning for anomaly detection.
result OML-AD outperforms state-of-the-art methods in accuracy and efficiency.
Paper presents a GAN model for realistic river image synthesis.
problem Generating high-quality river images for hydrological research.
method Used a Progressive Growing GAN (PGGAN) architecture to overcome training challenges.
result Demonstrated the effectiveness of GANs in generating high-resolution river images.
Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effect…
This study examines whether PCA can effectively identify nitrogen pollution sources in rivers.
problem Identifying pollution sources in rivers for effective environmental management.
method Principal Component Analysis and its modifications, along with Independent Component Analysis and Factor Analysis, are applied to nitrogen pollution source identification.
result PCA and related techniques can be powerful tools for uncovering nitrogen pollution sources in rivers.
HydroNets use river structure to improve hydrologic predictions.
problem Scalable and accurate hydrologic models are needed for climate change impacts.
method HydroNets are deep neural networks that incorporate river network structure.
result HydroNets improve predictions with fewer data, especially at longer horizons.
Online algorithms for identifying river pollution sources.
problem Real-time estimation of river pollution sources from downstream data.
method Gradient-based online learning algorithms with adaptive step sizes and escaping from saddle points module.
result High estimation accuracy in three dimensions, superior to existing methods.
Deep learning improves probabilistic river discharge forecasting for hydroelectric power.
problem Uncertain river discharges due to climate variability.
method Modified recurrent neural network architecture conditioned on global circulation model projections.
result Generates parameterized probability distributions for realistic long-term discharge scenarios.
Looped transformers outperform standard transformers in complex reasoning tasks due to a specific loss landscape geometry.
problem Understanding why looped transformers outperform standard transformers in complex reasoning tasks.
method Explained through loss landscape geometry, distinguishing between U-shaped and V-shaped valleys, and proposing SHIFT training strategy.
result Looped transformers' recursive architecture induces a River-V-Valley landscape, leading to better loss convergence and complex pattern learning.
We consider remodeling the planar search patterns, in the presence of the river-type perturbation represented by the weak vector field, basing on the time-optimal paths as Finslerian solutions to the Zermelo navigation problem via Randers metric.
Study uses AUVs and RL to map river plumes over multiple days.
problem Long-term mapping of dynamic river plumes with multiple AUVs.
method Multi-agent reinforcement learning with spatiotemporal GPR.
result Multi-agent approach outperforms single-agent and benchmarks.
Modeling daily river flow distribution with seasonal and long-term trends.
problem Capturing both seasonal and gradual long-term changes in environmental variables.
method Distributional regression using GAMLSS framework to estimate daily distribution of river flows.
result Model successfully captures seasonal variation and long-term trends in river flow data.
WSD schedule improves model training efficiency by adapting learning rates dynamically.
problem Fixed compute budgets limit training efficiency of language models.
method Introduces a WSD schedule that uses a constant learning rate followed by a rapid decay phase.
result WSD schedule generates a non-traditional loss curve with stable and decay phases.
New method combines simple forecasting techniques for river flow predictions.
problem Improving accuracy of long-term hydrological forecasts.
method Combines at least two forecasting methods using median combiner.
result Performs well in long-term forecasts, especially with multiple methods.
The study analyzes river water quality using statistical and machine learning methods.
problem Analyzing spatio-temporal dynamics of dissolved oxygen in the River Thames.
method Superstatistical methods and machine learning (e.g., Light Gradient Boosting Machine, Informer model).
result The Informer model outperforms others in long-term dissolved oxygen concentration forecasting.
Flooding is a destructive and dangerous hazard and climate change appears to be increasing the frequency of catastrophic flooding events around the world. Physics-based flood models are costly to calibrate and are rarely generalizable across different river basins, as model outputs are sensitive to site-specific parame…
New framework reveals thermodynamic principles for LLM training.
problem Understanding the training dynamics of large language models.
method Introducing Neural Thermodynamic Laws (NTL) under river-valley loss landscape assumptions.
result Key thermodynamic quantities and principles naturally emerge in LLM training.
Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river discharge measurements, which can be quite scarce and unreliable, particularly in regions where floods cause the most damage every year. Accordi…
Method reduces model bias in water temperature prediction using physics-guided GNNs.
problem Model bias in traditional physics-based models across different income and education levels.
method Physics-guided GNNs with refined neighbor selection and weights.
result Preserves equitable performance across different sensitive groups in the Delaware River Basin.
CausalRivers benchmarks causal discovery methods on real-world river discharge data.
problem Lack of in-the-wild evaluation of causal discovery methods on complex, real-world data.
method Introduces CausalRivers, a large-scale dataset of river discharge data for benchmarking.
result Demonstrates the utility of CausalRivers in evaluating causal discovery methods.
Dataset for rainfall modeling in central Europe from 1981-2011.
problem Improving rainfall streamflow modeling beyond simple catchments.
method Spatially resolved meteorological and ancillary data compilation.
result Dataset for neural network-driven hydrological modeling.
Proposes standards for evaluating online machine learning methods in evolving data streams.
problem Difficulty in evaluating online machine learning methods under realistic conditions.
method Proposes comprehensive evaluation standards, performance measures, and evaluation strategies.
result Provides a new Python framework (float) for modular integration of libraries and custom code.
We consider the problem of developing suitable learning representations (embeddings) for library packages that capture semantic similarity among libraries. Such representations are known to improve the performance of downstream learning tasks (e.g. classification) or applications such as contextual search and analogica…
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.…
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.
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 …
Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are with prescribed size, and can degenerate the …
In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we…
In this work, we address the problem of tuning communication libraries by using a deep reinforcement learning approach. Reinforcement learning is a machine learning technique incredibly effective in solving game-like situations. In fact, tuning a set of parameters in a communication library in order to get better perfo…
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.
Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes. However, there is a need to analyze vast amounts of social media data. This study presents a computational approach to explore the content of twe…
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.
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.
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.
This paper uses VAE to generate extreme events from multivariate data.
problem Generating accurate extremes from observational data for risk assessment.
method Variational Autoencoder (VAE) approach for multivariate heavy-tailed distributions.
result Improves learning of dependency structure between extremes.
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.
We generalize Conway's approach to integral binary quadratic forms on Q to study integral binary hermitian forms on quadratic imaginary extensions of Q. In Conway's case, an indefinite form that doesn't represent 0 determines a line ("river") in the spine T associated with SL(2,Z) in the hyperbolic plane. In our genera…
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.
Machine learning and deep learning infer surface/groundwater exchange from temperature data.
problem Inferring surface/groundwater exchange from temperature data with high temporal resolution.
method Application of machine learning and deep learning algorithms to infer surface/groundwater exchange flux from subsurface temperature observations.
result DL methods outperform ML methods in interpreting noisy temperature data, especially with a smoothing filter.
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.
New method SF-AdamW trains large models without decay phases or memory overhead.
problem Inadequate fixed compute budgets for large-scale training.
method Schedule-Free (SF) method revisited and refined.
result SF-AdamW effectively navigates loss landscape without decay phases or memory overhead.
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.
Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learning. We focus on matrix regression tasks where the rank of the weight matrix is constrained to reduce sample complexity. We introduce the comm…