################################################################################### # # # README # # # #---------------------------------------------------------------------------------# # NOMAD - Nonlinear Optimization by Mesh Adaptive Direct Search - # # # # NOMAD - Version 4 has been created by # # Viviane Rochon Montplaisir - Polytechnique Montreal # # Christophe Tribes - Polytechnique Montreal # # # # The copyright of NOMAD - version 4 is owned by # # Charles Audet - Polytechnique Montreal # # Sebastien Le Digabel - Polytechnique Montreal # # Viviane Rochon Montplaisir - Polytechnique Montreal # # Christophe Tribes - Polytechnique Montreal # # # # NOMAD 4 has been funded by Rio Tinto, Hydro-Québec, Huawei-Canada, # # NSERC (Natural Sciences and Engineering Research Council of Canada), # # InnovÉÉ (Innovation en Énergie Électrique) and IVADO (The Institute # # for Data Valorization) # # # # NOMAD v3 was created and developed by Charles Audet, Sebastien Le Digabel, # # Christophe Tribes and Viviane Rochon Montplaisir and was funded by AFOSR # # and Exxon Mobil. # # # # NOMAD v1 and v2 were created and developed by Mark Abramson, Charles Audet, # # Gilles Couture, and John E. Dennis Jr., and were funded by AFOSR and # # Exxon Mobil. # # # # Contact information: # # Polytechnique Montreal - GERAD # # C.P. 6079, Succ. Centre-ville, Montreal (Quebec) H3C 3A7 Canada # # e-mail: [email protected] # # # # This program is free software: you can redistribute it and/or modify it # # under the terms of the GNU Lesser General Public License as published by # # the Free Software Foundation, either version 3 of the License, or (at your # # option) any later version. # # # # This program is distributed in the hope that it will be useful, but WITHOUT # # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or # # FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License # # for more details. # # # # You should have received a copy of the GNU Lesser General Public License # # along with this program. If not, see <http://www.gnu.org/licenses/>. # # # # You can find information on the NOMAD software at www.gerad.ca/nomad # #---------------------------------------------------------------------------------# DESCRIPTION: NOMAD is a C++ implementation of the Mesh Adaptive Direct Search (MADS) algorithm, designed for constrained optimization of black-box functions. The algorithms implemented are based on the book "Derivative-Free and Blackbox Optimization", by Charles Audet and Warren Hare, Springer 2017. WEB PAGE: https://www.gerad.ca/nomad/ CONTACT: [email protected] VERSION WARNING: This repository is for NOMAD 4. NOMAD 3 is not on GitHub. NOMAD 4 is similar in usage to NOMAD 3. It does not have all functionalities from NOMAD 3 yet. NOMAD 4 has a new software architecture, uses OpenMP to run evaluations in parallel, and also has some new functionalities. COMPILATION (Release): On Linux, Unix, Windows and Mac OS X, NOMAD can be compiled using CMake. The minimum version of CMake is 3.14. Older versions will trigger an error. A recent C++ compiler is also required. The procedure is the following. On the command line in the $NOMAD_HOME directory: cmake -S . -B build/release ---> Create the CMake files and directories for building (-B) in build/release. The source (-S) CMakeLists.txt file is in the $NOMAD_HOME directory. To enable time stats build: cmake -DTIME_STATS=ON -S . -B build/release To enable interfaces (C and Python) building: cmake -DBUILD_INTERFACES=ON -S . -B build/release Python and Cython need to be available; using Anaconda is recommended. To deactivate compilation with OpenMP: cmake -DTEST_OPENMP=OFF -S . -B build/release cmake --build build/release ---> Build all the libraries and applications Option --parallel xx can be added for faster build. Option --config Release should be used on *Windows* to compile Release configuration. The default configuration is Debug. cmake --install build/release ---> Copy binaries and headers in build/release/[bin, include, lib] and in the examples/tests directories. Option --config Release should be used on Windows to install Release configuration. The default configuration is Debug. The executable "nomad" will installed into the directory: build/release/bin/ (build/debug/bin/ when in debug mode). It is possible to build only a single application in its working directory: (with NOMAD_HOME environment variable properly set) cd $NOMAD_HOME/examples/basic/library/example1 cmake --build $NOMAD_HOME/build/release --target example1_lib.exe cmake --install $NOMAD_HOME/build/release COMPILATION (Debug): The procedure to build the debug version is the following. On the command line in the $NOMAD_HOME directory: cmake -S . -B build/debug -D CMAKE_BUILD_TYPE=Debug ---> On Windows, all 4 configurations are always build Debug, RelWithDebugInfo, MinSizeRel, Release); flag CMAKE_BUILD_TYPE is ignored. cmake --build build/debug ---> Build the libraries and applications Option --parallel xx can be added for faster build. On Windows, the default configuration is Debug. cmake --install build/debug ---> Copy binaries and headers in build/debug/[bin, include, lib] and in the examples/tests directories EXAMPLES OF OPTIMIZATION: Batch Mode: There are examples in batch mode in examples/basic/batch/. In each directory, the blackbox functions (usually named bb) are compiled by default. The problem may be resolved using NOMAD and the parameter file: nomad param.txt Library Mode: There are examples in library mode in examples/basic/library/. In each directory, the executable may be compiled when building Nomad application. The problems may be resolved by execution, for instance: example_lib.exe
NOMAD - A blackbox optimization software
Overview
Comments
-
Installation error in Windows
The command
cmake --install build/release
in Windows powershellReturns
CMake Error at build/release/ext/sgtelib/cmake_install.cmake:39 (file): file INSTALL cannot find "C:/......../nomad-v.4.2.0/build/release/ext/sgtelib/Release/sgtelib_main.exe":
A temporary fix consists of replacing the file CMakeLists.txt in C:/..../nomad-v.4.2.0/ext/sgtelib by the one in attachment. I just commented "sgtelib_main.exe" which is not necessary for Nomad optimization. CMakeLists.txt
-
cmake error during Configuration step
I am on windows server 2019. The command
cmake -S . -B build/release
returns an error.CMake Error at CMakeLists.txt:9 (project): Running 'nmake' '-?' failed with: The system cannot find the file specified CMake Error: CMAKE_C_COMPILER not set, after EnableLanguage CMake Error: CMAKE_CXX_COMPILER not set, after EnableLanguage -- Configuring incomplete, errors occurred! See also "C:/Nomad/nomad-4.1.0/build/release/CMakeFiles/CmakeOutput.log".
Any ideas?
-
fix mingw compat
see https://dev.azure.com/JuliaPackaging/Yggdrasil/_build/results?buildId=20791&view=logs&jobId=eb643228-59a0-57e5-a6a5-6cb55400d5ea&j=26a97fd6-2070-5ccc-98fe-466416439df2&t=abe13601-88ab-5e8d-6817-533311385b0c
-
Java wrapper build error
OS: Debian "bullseye" Nomad version: 4.2
When building the Java wrapper, following the instructions in
interfaces/jNomad/Readme
, I get the following error:[ 93%] Built target jNomad [ 93%] Linking CXX executable NMOpt.exe Scanning dependencies of target jNomad_jar gmake[2]: *** No rule to make target 'interfaces/jNomad/SWIGTYPE_p_NOMAD_4_1__EvalParameters.java', needed by 'interfaces/jNomad/CMakeFiles/jNomad_jar.dir/java_compiled_jNomad_jar'. Stop. gmake[1]: *** [CMakeFiles/Makefile2:1366: interfaces/jNomad/CMakeFiles/jNomad_jar.dir/all] Error 2 gmake[1]: *** Waiting for unfinished jobs....
and cmake exits with error code 2.
Here is the complete Dockerfile I'm using:
FROM openjdk:8u332-bullseye ENV DEBIAN_FRONTEND=noninteractive # Install dependencies RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential \ m4 \ cmake \ nano \ curl WORKDIR / # First install SWIG # Install dependencies RUN apt-get update && apt-get install -y --no-install-recommends \ git \ autotools-dev \ automake \ byacc \ libpcre2-dev RUN git clone https://github.com/swig/swig.git WORKDIR swig RUN ./autogen.sh RUN ./configure --prefix=/swig RUN make RUN make install RUN ln -s /swig/bin/swig /usr/local/bin/swig # Compile and install NOMAD WORKDIR / RUN curl https://codeload.github.com/bbopt/nomad/tar.gz/refs/tags/v.4.2.0 --output NOMAD42.tar.gz RUN tar -xf NOMAD42.tar.gz && mv nomad-v.4.2.0 NOMAD42 && rm -r NOMAD42.tar.gz ENV NOMAD_HOME="/NOMAD42" WORKDIR /NOMAD42 RUN cmake -DTEST_OPENMP=OFF -DBUILD_INTERFACE_C=ON -DBUILD_INTERFACE_JAVA=ON -S . -B build/release && \ cmake --build build/release --parallel 16 && \ cmake --install build/release ENV LD_LIBRARY_PATH="/NOMAD42/build/release/lib" WORKDIR / CMD /bin/bash
I was able to build the C and Python interfaces without a problem.
Any help would be greatly appreciated!
-
Failure in solving recurrent problems using PyNOMAD
Hi, I'm devising an application for which I'm using Nomad in python as an MIP solver. I want to use PyNomad recurrently to solve several subproblems but I get the following message from the second execution of Nomad (it works well in the first one) :
How can I avoid such problem?
Best regards, Juan José
-
Some examples run unsucessfully in NOMAD4, but successuly in NOMAD3.9.1
Hi, I am new using the NOMAD to do a evaluation of a problem. When I download the NOMAD 4 and modify the single_obj file to a simple objective function like y=sin(x) * x *x, every parameter seems set good. NOMAD4 will run into a dead loop while NOMAD3.9.1 will run with best feasible solution immediately. They are the same project in different libraries.
I am not sure this is normal for using NOMAD. Thank you
-
Repository size
The size of this repository is over 300 Mb, mostly because of the compiled docs. I'm wondering if you could shrink it, by making the compiled docs available by other means. This might benefit users with poor connection, by reducing network traffic and storage.
Thanks for making this great tool available here!
-
Inconsistency in AllParameters::readParamLine
The comment claims the function AllParameters::readParamLine throws for unknown parameters.
The implementation, however, does not throw anything. Instead, the errors are printed out to the standard error stream.
-
Make Nomad 4 installer for end users
Hello Nomad team,
for Nomad 3.9.1, there is an installer for end user so the user can install with a simple click of the installer file.
Could we also have an installer for Nomad 4 for end user? Typically I am asking for Windows OS.
Thank you, Jenny
-
Add Julia interface in doc
Would it be pertinent to have a section about the Julia interface Nomad.jl in this section https://nomad-4-user-guide.readthedocs.io/en/latest/LibraryMode.html ?
documentation -
Is there a way to terminate the costly blackbox evaluation in advance?
Hi, I am using the NOMAD3.9.1 to evaluate a blackbox task. It will drop to obvious local optimum somethings. I notice that the outputs are nearly same or even get very small differences at the end of the evaluation. In some conditions, it will last for hundres of iterations. More compuating resources and time have been put into the final result, but the result has satisfied the requirements. So, my question is how can I alter some arguments or is there a mechanism to terminate the evaluaion in NOMAD to solve this problem? I know user can use CTRL-C to terminate the algorithm mamually. Thank you very much.
-
MATLAB interface documentation inconsistency
The documentation for the MATLAB interface diverges from the actual implementation.
- According to the documentation,
nomadOpt
returns[ x, fval, exitflag, iter, nfval ]
, however, the actual implementation returns[ x, fval, hinf, exitflag, nfval ]
. The example reflects the actual implementation. - Values of the
exitflag
do not follow the documented scheme. Instead of the declared spectrum of return values, only two values are returned and their meaning differs from the documentation. The value0
represents generic success, whereas-1
indicates no feasible result was found.
I believe it may be possible to modify nomadmex.cpp to approach the documented behavior. The following pseudo-code might do that.
bool hasConverged = (nbBestFeas > 0); bool hasInfeasible = (nbBestInf > 0); bool exitInitializationError = AllStopReasons::testIf(INITIALIZATION_FAILED); bool exitUser = ( AllStopReasons::testIf(BaseStopType::CTRL_C) || AllStopReasons::testIf(BaseStopType::USER_STOPPED)); bool exitNomadError = ( AllStopReasons::testIf(BaseStopType::ERROR) || AllStopReasons::testIf(BaseStopType::UNKNOWN_STOP_REASON)); bool exitExceededEvaluations = ( AllStopReasons::testIf(EvalGlobalStopType::MAX_EVAL_REACHED) || AllStopReasons::testIf(EvalGlobalStopType::MAX_BB_EVAL_REACHED) || AllStopReasons::testIf(EvalGlobalStopType::MAX_BLOCK_EVAL_REACHED) || AllStopReasons::testIf(EvalGlobalStopType::MAX_SURROGATE_EVAL_OPTIMIZATION_REACHED)); if (exitUser) { * exitflag = -5; } else if (exitNomadError) { * exitflag = -3; } else if (exitInitializationError) { * exitflag = -2; } else if (hasConverged) { * exitflag = 1; } else if (exitExceededEvaluations) { * exitflag = 0; } else if (hasInfeasible) { * exitflag = -1; } else { // Something else must have happened. // There are neither feasible nor infeasible points and there is no clear reason for termination. // Let's chalk that up as 'nomad error'. * exitflag = -3; }
REMARKS
- I see a potential issue regarding the definition of converged / target reached within the above code: should we consider the search successful if at least one feasible point was found even though the optimizer eventually ran out of evaluations?
- I currently do not have access to MATLAB-compatible version of GCC. Once I do, and if desired, I can eventually turn this into a pull request.
- Essentially identical code could be used to resolve https://github.com/bbopt/nomad/issues/104.
- According to the documentation,
-
Install PyNomad in virtual environment
The current installation instructions make very hard to install PyNomad in virtual enviroment, in fact the
--user
option in the lineCOMMAND python setup_PyNomad.py ${CMAKE_BINARY_DIR} ${NOMAD_VERSION} install
insideinterfaces/PyNomad/CMakeLists.txt
overwrite the default setting of a virtual environment.A separate parameter
PYTHON_DIR
defined in CMake could allow the user to specify the correct Python location, otherwise the--user
flag could be removed to let the system the best location by itself.Is there a way to install PyNomad in a virtual environment with the current code?
-
4.x Constraint Tolerance Parameter Equivalent
What are the NOMAD 4.x equivalent parameters to the 3.x parameters "h_min" and "h_norm"? I don't see anything that is roughly the same as these in the 4.x parameter list.
-
Add pip installation
I found an unofficial
pip
installation path to an older and possibly modified version of NOMAD. Would be convenient to have an officialpip
installation path to the latest version. -
Matlab and python interface exit status
Both python and matlab interfaces should provide complete exit status range: % 1 - converged / target reached % 0 - maximum iterations / function evaluations exceeded % -1 - infeasible / mesh limit reached % -2 - initialization error % -3 - nomad error % -5 - user exit
This issue has been updated using issue #124. Maybe we need more exit statuses (and clarify the meaning):
- (feasible+max eval/iter reached) from (infeasible+max eval/iter reached).
- (feasible+mesh limit reached-> equivalent to 1-converged) from (infeasible+mesh limit reached).
-
MATLAB building fails on macOS
My problem
I am building nomad on macOS for MATLAB. I hence ran
cmake -DBUILD_INTERFACE_MATLAB=ON -DMatlab_ROOT_DIR=/Applications/MATLAB_R2022b.app -S . -B build/release
This completed successfully. However, when building the library with
cmake --build build/release
I get the following error.
[ 99%] Linking CXX shared library nomadOpt.mexmaci64 Undefined symbols for architecture x86_64: "_utIsInterruptPending", referenced from: matlabEval::eval_x(NOMAD_4_2::EvalPoint&, NOMAD_4_2::Double const&, bool&) const in nomadmex.cpp.o "_utSetInterruptPending", referenced from: matlabEval::eval_x(NOMAD_4_2::EvalPoint&, NOMAD_4_2::Double const&, bool&) const in nomadmex.cpp.o ld: symbol(s) not found for architecture x86_64 clang: error: linker command failed with exit code 1 (use -v to see invocation) make[2]: *** [interfaces/Matlab_MEX/nomadOpt.mexmaci64] Error 1 make[1]: *** [interfaces/Matlab_MEX/CMakeFiles/nomadOpt.dir/all] Error 2 make: *** [all] Error 2
I set up mex to run with
clang
.My architecture
- macOS: version 12.6 (Monterey, x86_64)
- clang: version 14.0.0 (clang-1400.0.29.102)
- MATLAB: version 9.13.0.2049777 (R2022b)
- CMake: version 3.24.1
Do you have any idea how I can fix my problem? Thank you very much for your help :grin:
fix
Releases(v.4.3.1)
-
v.4.3.1(Dec 23, 2022)
NOMAD 4 is available as open-source code, under the LGPL license. Compilation on MacOS, Windows and Linux is done using cmake. Binary packages are also available.
We are looking forward for user feedback. For more information, reach out to [email protected].
The user guide is available here.
We recommend to download the complete NOMAD source files and examples and build the project for your platform. The user guide describes the complete steps for building.
For users who do not follow the previous recommendation, a compact version with binaries (zipped) are available for Windows, Mac-OSX and Linux Ubuntu in the Assets section below. Please note, that you will need to do manual modifications to execute the binaries (see the end Note in the README file).
New in Release 4.3: DMultiMads algorithm for multiobjective optimization problems. A Template algorithm to help users implement their own method in Nomad. Static surrogate can be used in library mode. Enable control the display precision.
Source code(tar.gz)
Source code(zip)
MacOS.zip(2.71 MB)
Ubuntu.zip(3.35 MB)
Windows--MSVC.zip(1.47 MB)
-
v.4.3.0(Dec 20, 2022)
NOMAD 4 is available as open-source code, under the LGPL license. Compilation on MacOS, Windows and Linux is done using cmake. Binary packages are also available.
We are looking forward for user feedback. For more information, reach out to [email protected].
The user guide is available here.
We recommend to download the complete NOMAD source files and examples and build the project for your platform. The user guide describes the complete steps for building.
For users who do not follow the previous recommendation, a compact version with binaries (zipped) are available for Windows, Mac-OSX and Linux Ubuntu in the Assets section below. Please note, that you will need to do manual modifications to execute the binaries (see the end Note in the README file).
New in Release 4.3: DMultiMads algorithm for multiobjective optimization problems. A Template algorithm to help users implement their own method in Nomad. Static surrogate can be used in library mode. Enable control the display precision.
Source code(tar.gz)
Source code(zip)
nomad4.3.0_binaries_macos.zip(2.71 MB)
nomad4.3.0_binaries_ubuntu.zip(3.34 MB)
nomad4.3.0_binaries_windows.zip(1.47 MB)
-
v.4.2.0(Feb 9, 2022)
NOMAD 4 is available as open-source code, under the LGPL license. Compilation on MacOS, Windows and Linux is done using cmake. Binary packages are also available.
We are looking forward for user feedback. For more information, reach out to [email protected].
The user guide is available here.
We recommend to download the complete NOMAD source files and examples and build the project for your platform. The user guide describes the complete steps for building.
For users who do not follow the previous recommendation, a compact version with binaries (zipped) are available for Windows, Mac-OSX and Linux Ubuntu in the Assets section below. Please note, that you may need to change the permissions to be able to execute the binaries.
New in Release 4.2:
- Poll direction ORTHO N+1 QUAD is now supported for parameters DIRECTION_TYPE.
- Default evaluation points sorting uses quadratic model.
- PSD-Mads has been implemented.
- Java interface is supported using Swig.
- Building PyNomad interface is supported for all versions using cmake.
- Building Matlab interface is done with cmake.
Source code(zip)
nomad4.2.0_binaries_macos.zip(3.42 MB)
nomad4.2.0_binaries_ubuntu.zip(4.31 MB)
nomad4.2.0_binaries_windows.zip(1.77 MB)
-
v4.1.0(Jul 7, 2021)
NOMAD 4 is available as open-source code, under the LGPL license. Compilation on MacOS, Windows and Linux is done using cmake.
We are looking forward for user feedback. For more information, reach out to [email protected].
The doc is available here https://nomad-4-user-guide.readthedocs.io/en/v4.1.0/
New in Release 4.1:
- It is possible to use a static surrogate executable to sort points before evaluating them with the blackbox. See parameter EVAL_QUEUE_SORT with value SURROGATE.
- It is also possible to do a full optimization using only the surrogate executable instead of the blackbox executable. See parameter EVAL_SURROGATE_OPTIMIZATION.
- Poll direction ORTHO N+1 NEG is now supported for parameters DIRECTION_TYPE and DIRECTION_TYPE_SECONDARY_POLL. It is also now possible to define several direction types for these parameters.
- Variable Neighborhood Search, using Mads for sub-optimization, is now supported. See parameter VNS_MADS_SEARCH.
- NOMAD can now be compiled on Windows. Follow the instructions in the README. PyNomad interface is not supported for this version.
Source code(zip)
-
v.4.0.2(Apr 26, 2021)
NOMAD 4 official release.
NOMAD 4 is available as open-source code, under the LGPL license. Compilation on MacOS and Linux is done using cmake. The code has not been compiled on Windows.
We are looking forward for user feedback. For more information, reach out to [email protected].
The doc is available here https://nomad-4-user-guide.readthedocs.io/en/v.4.0.2/
New in Release 4.0:
- Secondary Poll center
- Speculative Search as in NOMAD 3
- Sort on direction of last success
- Latin hypercube may be used when X0 is not provided
- Updated parameter names
- User guide
Features already available in NOMAD 4, Beta 2:
- Quadratic Model Search (QUAD_MODEL_SEARCH)
- Groups of variables (VARIABLE_GROUP)
- Direction types (DIRECTION_TYPE)
- Sort evaluation queue before evaluating points; randomize point evaluation
- PSD-Mads
- Additional statistics, HISTORY_FILE, SOLUTION_FILE
- Compilation using CMake
- Parameter USE_CACHE
Features already available in NOMAD 4, Beta 1:
- Parameter syntax for parameter file is the same as in NOMAD 3
- Search implementations: Nelder Mead, Speculative, Latin Hypercube, SgtelibModel
- Usage of library Sgtelib
- Management of PB (progressive barrier) and EB (extreme barrier) constraints
- Python interface
- Callbacks
- Block evaluations
- Evaluations are done in parallel, using OpenMP
- Hot restart: See parameter HOT_RESTART_ON_USER_INTERRUPT
Source code(zip)
-
v.4.0.1(Apr 21, 2021)
Please use NOMAD 4.0.2 or later
Source code(tar.gz)
Source code(zip)
-
v.4.0.0(Apr 21, 2021)
Please use NOMAD 4.0.2 or later
Source code(tar.gz)
Source code(zip)
-
v.4.0.0-beta.2.0.1(Dec 18, 2020)
NOMAD 4 Beta 2 has numerous improvements since Beta 1 (December 2019).
NOMAD 4 is available as open-source code, under the LGPL license. Makefiles are present for compilation on MacOS and Linux. The code has not been compiled on Windows.
We are looking forward for user feedback. For more information, reach out to [email protected].
Overview:
New in Beta 2:
- Quadratic Model Search (QUAD_MODEL_SEARCH)
- Groups of variables (VARIABLE_GROUP)
- Direction types (DIRECTION_TYPE)
- Sort evaluation queue before evaluating points; randomize point evaluation
- PSD-Mads
- Additional statistics, HISTORY_FILE, SOLUTION_FILE
- Compilation using CMake
- Parameter USE_CACHE
Features already available in NOMAD 4, Beta 1:
- Parameter syntax for parameter file is the same as in NOMAD 3
- Search implementations: Nelder Mead, Speculative, Latin Hypercube, SgtelibModel
- Usage of library Sgtelib
- Management of PB (progressive barrier) and EB (extreme barrier) constraints
- Python interface
- Callbacks
- Block evaluations
- Evaluations are done in parallel, using OpenMP
- Hot restart: See parameter HOT_RESTART_ON_USER_INTERRUPT
Source code(zip)
-
v.4.0.0-beta.2(Dec 1, 2020)
-
v.4.0.0-beta.1.0.2(Dec 19, 2019)
Owner
Blackbox Optimization
This is the code for the paper "Contrastive Clustering" (AAAI 2021)
Contrastive Clustering (CC) This is the code for the paper "Contrastive Clustering" (AAAI 2021) Dependency python=3.7 pytorch=1.6.0 torchvision=0.8
PyTorch implementations of the paper: "DR.VIC: Decomposition and Reasoning for Video Individual Counting, CVPR, 2022"
DRNet for Video Indvidual Counting (CVPR 2022) Introduction This is the official PyTorch implementation of paper: DR.VIC: Decomposition and Reasoning
Unofficial Tensorflow 2 implementation of the paper Implicit Neural Representations with Periodic Activation Functions
Siren: Implicit Neural Representations with Periodic Activation Functions The unofficial Tensorflow 2 implementation of the paper Implicit Neural Repr
Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.
RealTime Sign Language Detection using Action Recognition Approach Real-Time Sign Language is commonly predicted using models whose architecture consi
This is the official implementation of TrivialAugment and a mini-library for the application of multiple image augmentation strategies including RandAugment and TrivialAugment.
Trivial Augment This is the official implementation of TrivialAugment (https://arxiv.org/abs/2103.10158), as was used for the paper. TrivialAugment is
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
DeeBERT This is the code base for the paper DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. Code in this repository is also available
Code for the paper "Query Embedding on Hyper-relational Knowledge Graphs"
Query Embedding on Hyper-Relational Knowledge Graphs This repository contains the code used for the experiments in the paper Query Embedding on Hyper-
A Python package for causal inference using Synthetic Controls
Synthetic Control Methods A Python package for causal inference using synthetic controls This Python package implements a class of approaches to estim
a baseline to practice
ccks2021_track3_baseline a baseline to practice 路径可能会有问题,自己改改 torch==1.7.1 pyhton==3.7.1 transformers==4.7.0 cuda==11.0 this is a baseline, you can fi
Time Delayed NN implemented in pytorch
Pytorch Time Delayed NN Time Delayed NN implemented in PyTorch. Usage kernels = [(1, 25), (2, 50), (3, 75), (4, 100), (5, 125), (6, 150)] tdnn = TDNN
From Perceptron model to Deep Neural Network from scratch in Python.
Neural-Network-Basics Aim of this Repository: From Perceptron model to Deep Neural Network (from scratch) in Python. ** Currently working on a basic N
SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab
CORNELLSASLAB SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab Instructions: This python code can be used to convert SAS out
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)
Self-labelling via simultaneous clustering and representation learning 🆗 🆗 🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvis
The official implementation for "FQ-ViT: Fully Quantized Vision Transformer without Retraining".
FQ-ViT [arXiv] This repo contains the official implementation of "FQ-ViT: Fully Quantized Vision Transformer without Retraining". Table of Contents In
ML course - EPFL Machine Learning Course, Fall 2021
EPFL Machine Learning Course CS-433 Machine Learning Course, Fall 2021 Repository for all lecture notes, labs and projects - resources, code templates
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation (AAAI 2021) Official pytorch implementation of our paper: Discriminative
The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.
VAENAR-TTS This repo contains code accompanying the paper "VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis". Sa
Official repository for the paper F, B, Alpha Matting
FBA Matting Official repository for the paper F, B, Alpha Matting. This paper and project is under heavy revision for peer reviewed publication, and s
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree
Graph Convolutional Networks for Temporal Action Localization (ICCV2019)
Graph Convolutional Networks for Temporal Action Localization This repo holds the codes and models for the PGCN framework presented on ICCV 2019 Graph