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
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 2 years ago
- Active and Sample-Efficient Model Evaluation☆24Updated 4 years ago
- Code for the paper "Semi-Conditional Normalizing Flows for Semi-Supervised Learning"☆10Updated 5 years ago
- Companion code for the paper "Learnable Uncertainty under Laplace Approximations" (UAI 2021).☆20Updated 3 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated last year
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 3 years ago
- Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')☆16Updated 2 years ago
- Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective (https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi …☆63Updated 4 years ago
- Pytorch implementation of VAEs for heterogeneous likelihoods.☆42Updated 2 years ago
- Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding☆21Updated 2 years ago
- Mathematical consequences of orthogonal weights initialization and regularization in deep learning. Experiments with gain-adjusted orthog…☆17Updated 5 years ago
- CEVAE with VampPrior☆11Updated 6 years ago
- ☆13Updated 2 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆75Updated last year
- ☆53Updated 8 months 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
- This repository holds code and other relevant files for the NeurIPS 2022 tutorial: Foundational Robustness of Foundation Models.☆71Updated 2 years ago
- Random feature latent variable models in Python☆22Updated last year
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆31Updated 3 years ago
- ☆12Updated 5 years ago
- Weekly reading group on Graph Representation Learning at Mila☆17Updated 5 years ago
- Guarantees on the behavior of neural networks don't always have to come at the cost of performance.☆28Updated 2 years ago
- ☆36Updated 3 years ago
- Shows how to create basic image adversaries, and train adversarially robust image classifiers (to some extent).☆13Updated 4 years ago
- Notebook for comprehensive analysis of authors, organizations, and countries of ICML 2020 papers.☆55Updated 4 years ago
- Dissecting the weight space of neural networks☆18Updated 3 years ago
- ☆16Updated 2 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 4 years ago