udemirezen

udemirezen's Stars

• mintisan/awesome-kan

  A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold Network field.

  1.4k124

• IBM/simulai

  A toolkit with data-driven pipelines for physics-informed machine learning.

  Language:Python14823

• udemirezen/modulus

  A PyTorch based deep-learning toolkit for developing DL models for physical systems

  1

• NVIDIA/modulus-launch

  Repo of optimized training recipes for accelerating PyTorch workflows of AI driven surrogates for physical systems

  Language:Python5527

• computational-imaging/bacon

  Official respository for "Band-limited Coordinate Networks for Multiscale Scene Representation" | CVPR 2022

  Language:Python16916

• udemirezen/cleanlab

  The standard package for data-centric AI and machine learning with label errors, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.

  1

• udemirezen/scibert

  A BERT model for scientific text.

  1

• ajayarunachalam/msda

  Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

  Language:Jupyter Notebook11731

• cleanlab/cleanlab

  The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

  Language:Python8.9k687

• SeldonIO/alibi-detect

  Algorithms for outlier, adversarial and drift detection

  Language:Python2.1k216

• reservoirpy/reservoirpy

  A simple and flexible code for Reservoir Computing architectures like Echo State Networks

  Language:Python37995

• rbischof/relative_balancing

  Language:Python449

• ChrisRackauckas/universal_differential_equations

  Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

  Language:Julia21559

• barkm/torch-fenics

  PyTorch-FEniCS interface

  Language:Python9122

• jlager/BINNs

  Biologically-informed neural networks

  Language:Jupyter Notebook2315

• differential-machine-learning/appendices

  Complement the article 'Differential Machine Learning' (Huge & Savine, 2020), including mathematical proofs and important implementation details for production

  2713

• HuHaigen/Adaptively-Customizing-Activation-Functions

  To enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data. In this work, a novel methodology is proposed to adaptively customize activation functions only by adding very few parameters to the traditional activation functions such as Sigmoid, Tanh, and ReLU. To verify the effectiveness of the proposed methodology, some theoretical and experimental analysis on accelerating the convergence and improving the performance is presented, and a series of experiments are conducted based on various network models (such as AlexNet, VGGNet, GoogLeNet, ResNet and DenseNet), and various datasets (such as CIFAR10, CIFAR100, miniImageNet, PASCAL VOC and COCO) . To further verify the validity and suitability in various optimization strategies and usage scenarios, some comparison experiments are also implemented among different optimization strategies (such as SGD, Momentum, AdaGrad, AdaDelta and ADAM) and different recognition tasks like classification and detection. The results show that the proposed methodology is very simple but with significant performance in convergence speed, precision and generalization, and it can surpass other popular methods like ReLU and adaptive functions like Swish in almost all experiments in terms of overall performance.

  Language:Python133

• Bellman281/Stat4ML

  Statistics and Mathematics for Machine Learning, Deep Learning , Deep NLP

  Language:Jupyter Notebook8116

• tum-pbs/PhiFlow

  A differentiable PDE solving framework for machine learning

  Language:Python1.2k178

• biomathlab/PDElearning

  Code repository for the paper "Learning partial differential equations for biological transport models from noisy spatiotemporal data"

  Language:Jupyter Notebook107

• bayesian-optimization/BayesianOptimization

  A Python implementation of global optimization with gaussian processes.

  Language:Python7.6k1.5k

• mle-infrastructure/mle-monitor

  Lightweight Experiment & Resource Monitoring 📺

  Language:Python1876

• marcellodebernardi/loss-landscapes

  Approximating neural network loss landscapes in low-dimensional parameter subspaces for PyTorch

  Language:Python27250

• idrl-lab/PINNpapers

  Must-read Papers on Physics-Informed Neural Networks.

  Language:Python763136

• pytorch/functorch

  functorch is JAX-like composable function transforms for PyTorch.

  Language:Jupyter Notebook1.4k102

• adityabalu/DiffNet

  DiffNet: A FEM based neural PDE solver package

  Language:Jupyter Notebook108

• rocketmlhq/sciml

  Scientific Machine Learning Tutorials

  Language:Jupyter Notebook3611

• CognitiveModeling/finn

  The public repository about our joint FINN research project

  Language:Python3514

• PML-UCF/pinn_ode_tutorial

  Language:Python10145

• ucl-bug/jaxdf

  A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations

  Language:Python1087