rishabhk108 / OptimizationDemos
Some simple demos I use in my optimization in ML course. Includes implementations of ML loss functions (Logistic Loss, SVM Loss, ..) and optimization algorithms (gradient descent, accelerated variants, conjugate GD, etc.)
☆13Updated 3 years ago
Alternatives and similar repositories for OptimizationDemos:
Users that are interested in OptimizationDemos are comparing it to the libraries listed below
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective (https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi …☆62Updated 4 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 3 years ago
- ☆53Updated 5 months ago
- Mathematical consequences of orthogonal weights initialization and regularization in deep learning. Experiments with gain-adjusted orthog…☆17Updated 5 years ago
- Dissecting the weight space of neural networks☆17Updated 3 years ago
- [ICLR 2022] "Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How" by Yuning You, Yue Cao, Tianl…☆13Updated 2 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 2 years ago
- Companion code for the paper "Learnable Uncertainty under Laplace Approximations" (UAI 2021).☆19Updated 3 years ago
- ☆36Updated 2 years ago
- ☆35Updated last year
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')☆16Updated 2 years ago
- Code to implement the AND-mask and geometric mean to do gradient based optimization, from the paper "Learning explanations that are hard …☆39Updated 4 years ago
- Implementation of Normalizing flows on MNIST https://arxiv.org/abs/1505.05770☆14Updated 6 years ago
- Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022☆44Updated 2 years ago
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 6 years ago
- ☆13Updated 2 years ago
- Collection of snippets for PyTorch users☆26Updated 2 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Notebooks for managing NeurIPS 2014 and analysing the NeurIPS experiment.☆11Updated 7 months ago
- The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) p…☆40Updated 4 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 3 years ago
- Energy Based Models are a quite novel technique for density estimation. In this university project I explore this new research topic and …☆15Updated 3 years ago
- Anytime Learning At Macroscale☆9Updated 3 years ago
- Code for "Training Deep Energy-Based Models with f-Divergence Minimization" ICML 2020☆36Updated last year
- Distributional and Outlier Robust Optimization (ICML 2021)☆26Updated 3 years ago
- Code for "On the Expressiveness of Approximate Inference in Bayesian Neural Networks"☆13Updated 3 years ago
- This repository holds code and other relevant files for the NeurIPS 2022 tutorial: Foundational Robustness of Foundation Models.☆70Updated 2 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆72Updated last year