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02456-deep-learning

Exercises and supplementary material for the deep learning course 02456.

adbench

Benchmarking various AD tools.

ah16_corenlp

CoreNLP pipeline for deriving dependencies and lemmatize words.

aoc22

https://adventofcode.com

berkeleydeeprl_hw

Assignments for CS294-112.

docker-stan

Dockerized cmdstan

dssp

The DSSP building software

hmcecs

Sample code for HMC-ECS

hyperopt

Distributed Asynchronous Hyperparameter Optimization in Python

idpconformergenerator

Build conformational representations of Intrinsically Disordered Proteins by a guided sampling of the protein torsion space. https://idpconformergenerator.readthedocs.io/

jax-md

Differentiable, Hardware Accelerated, Molecular Dynamics

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

modern-cpp-programming

Modern C++ Programming Course (C++11/14/17/20)

paraphrasegen

pnerf-jax

probai-2021-pyro

Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

probai21_exam

Project assignment for the Probabilistic AI course (DT8122) at NTNU.

project_euler

proteinnet

Standardized data set for machine learning of protein structure

rl4nlp

Reinforcement learning for natural language processing reading group

sidechainnet

An all-atom protein structure dataset for machine learning.

simppl

A simple ppl

sluice-networks

Code for Sluice networks: Learning what to share between loosely related tasks

sluice_antecedent_selection

Recurrent MTL architecture for antecedent selection in sluicing

sppl

Probabilistic programming system for fast and exact symbolic probabilistic inference

stein-variational-gradient-descent

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

stein_vi

tensorflow

Computation using data flow graphs for scalable machine learning

zoneout

Code for experiments with our RNN regularizer, which stochastically forces units to maintain previous values.