Alternatives and similar repositories for awesome-optimizers

Users that are interested in awesome-optimizers are comparing it to the libraries listed below

• ayulockin / LossLandscape
  Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective (https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi …
  ☆65Updated 5 years ago
• facebookresearch / LANCER
  Repo for the paper "Landscape Surrogate Learning Decision Losses for Mathematical Optimization Under Partial Information"
  ☆36Updated 2 years ago
• aoiang / LaMOO
  ☆29Updated 2 years ago
• yiminzme / awesome-bayesian-optimization
  a curated list of bayesian optimization tutorials, papers, softwares
  ☆31Updated 4 years ago
• AvivNavon / pareto-hypernetworks
  Official implementation of Learning The Pareto Front With HyperNetworks [ICLR 2021]
  ☆104Updated 3 years ago
• harsh306 / awesome-nn-optimization
  Awesome list for Neural Network Optimization methods.
  ☆80Updated 6 months ago
• fmsnew / nas-bench-nlp-release
  ☆29Updated last year
• samuelstanton / gnosis
  Code to reproduce experiments from 'Does Knowledge Distillation Really Work' a paper which appeared in the 2021 NeurIPS proceedings.
  ☆33Updated last year
• The-AI-Summer / pytorch-ddp
  code for the ddp tutorial
  ☆32Updated 3 years ago
• xiaofangxd / Multi-objective-optimization-and-Multi-task-Learning
  ☆74Updated last year
• markus93 / NN_calibration
  Calibration of Convolutional Neural Networks
  ☆170Updated 2 years ago
• AwesomeLemon / ENCAS
  Neural Architecture Search + Cascades | Best Paper @ GECCO 2022
  ☆15Updated 2 years ago
• facebookresearch / robust_mobo
  Code for "Robust Multi-Objective Bayesian Optimization Under Input Noise"
  ☆55Updated 3 years ago
• yuanwei2019 / EAdam-optimizer
  Some improvements on Adam
  ☆28Updated 4 years ago
• automl / multi-obj-baselines
  ☆23Updated last year
• JingzhaoZhang / why-clipping-accelerates
  A pytorch implementation for the LSTM experiments in the paper: Why Gradient Clipping Accelerates Training: A Theoretical Justification f…
  ☆46Updated 5 years ago
• ondrejbohdal / meta-calibration
  Official PyTorch implementation of "Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error"
  ☆36Updated 2 years ago
• Shen-Lab / Bayesian-L2O
  [ICLR 2022] "Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How" by Yuning You, Yue Cao, Tianl…
  ☆14Updated 3 years ago
• zju-vipa / awesome-neural-trees
  Introduction, selected papers and possible corresponding codes in our review paper "A Survey of Neural Trees"
  ☆83Updated 2 years ago
• automl / DEHB
  ☆88Updated 4 months ago
• ChristophReich1996 / SmeLU
  PyTorch reimplementation of the Smooth ReLU activation function proposed in the paper "Real World Large Scale Recommendation Systems Repr…
  ☆22Updated 3 years ago
• xuyxu / Soft-Decision-Tree
  PyTorch Implementation of "Distilling a Neural Network Into a Soft Decision Tree." Nicholas Frosst, Geoffrey Hinton., 2017.
  ☆103Updated last year
• weigq / neurips2021_stats
  ☆35Updated 3 years ago
• rtu715 / NAS-Bench-360
  Introducing diverse tasks for NAS
  ☆50Updated 2 years ago
• wiseodd / last_layer_laplace
  Last-layer Laplace approximation code examples
  ☆83Updated 3 years ago
• ruchtem / cosmos
  This is the official implementation for COSMOS: a method to learn Pareto fronts that scales to large datasets and deep models.
  ☆38Updated 4 years ago
• xingchenwan / nasbowl
  [ICLR '21] Interpretable Neural Architecture Search using Bayesian Optimisation with Weisfiler-Lehman Kernel (NAS-BOWL)
  ☆24Updated 3 years ago
• microsoft / bayesianize
  Bayesianize: A Bayesian neural network wrapper in pytorch
  ☆89Updated last year
• cure-lab / deep-active-learning
  An implementation of the state-of-the-art Deep Active Learning algorithms
  ☆104Updated last year
• logancyang / loss-landscape-anim
  Create animations for the optimization trajectory of neural nets
  ☆158Updated last year