Julius Berner's repositories
sde_sampler
Improved sampling via learned diffusions (ICLR2024) and an optimal control perspective on diffusion-based generative modeling (TMLR2024)
deep_kolmogorov
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)
oberwolfach_workshop
Material for 'Mathematics of Deep Learning Workshop' (Invited Talk)
emotion_transformer
Contextual Emotion Detection in Text (DoubleDistilBert Model)
Deep-Multilevel-Kolmogorov-PDE-Solver
Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation
homeserver
Ansible playbook for dockerized home-server
svm_tf_pytorch
Soft-margin SVM gradient-descent implementation in PyTorch and TensorFlow/Keras
neural_ode_julia
Using DiffEqFlux to learn underlying differential equations from data.
rpi_vpn_router
Ansible playbook to setup a VPN router using OpenWrt on a Raspberry Pi
pgm_tutorial
A short introduction to probabilistic graphical models using jupyter slides
robust_kolmogorov
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning (ICML 2022)
ddn_tutorial
Tutorial on Deep Declarative Networks
science-GHOSTS
GHOSTS: Mathematical Capabilities of ChatGPT
theory2practice
Learning ReLU networks to high uniform accuracy is intractable (ICLR 2023)
NeuralCompression
A collection of tools for neural compression enthusiasts. Featuring my work on Bits-Back coding with diffusion models, see projects/bits_back_diffusion.
DPOT
Code for "DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training"
neural_wos
Efficient neural PDE solvers for elliptic PDEs based on Walk-on-Spheres methods.
nf_tutorial
A short tutorial on normalizing flows using jupyter slides
nn_inverse_stability
Illustrating the failure of inverse stability of the neural network realization map.
painter_classification
Painter classification - Model deployment on Render
regularity_relu_network
Towards a regularity theory for ReLU networks (construction of approximating networks, ReLU derivative at zero, theory)