Multi-label MFoM framework for DCASE 2016: Task 4
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Updated
Sep 29, 2018 - Python
Multi-label MFoM framework for DCASE 2016: Task 4
Provisioning of distributed infrastructure for learning as an optimization problem. An implementation of proof-of-concept iteration components is provided.
The 8-queens problems asks us to place 8 queens on a chessboard so that no two can capture one another; that is, no two are on the same row, column, or diagonal.
Simulation experiments for optimizing objective function with Differential Evolution, Evolution Strategies and Cross Entropy Method (2 versions)
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering (WWW'22)
PyTorch implementations of the beta divergence loss.
[ICCV 2021]Code for the the bias loss and evaluation of SkipblockNet model on ImageNet validation set
Customization of objective functions of gradient boosting tree algorithms such as Xgboost / LightGbm / CatBoost...
A TensorFlow loss function based on a approximation of the normalized Wilcoxon-Mann-Whitney (WMW) statistic.
Usage of SnapKit, UIButtons, Objective-C Functions and Animation
a gradient-based optimisation routine for highly parameterised non-linear dynamical models
A collection and visualization of single objective black-box functions for optimization benchmarking.
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