danielhelle Goto Github PK

3dunet-pytorch

3DUNet implemented with pytorch

adversarialsearch

Implemented an AI in a fishing game together with Olof Jonnerby that maximizes the green boat's score (number of fish). The AI uses the search algorithm minimax and alphabeta pruning

ci-server

This program listens for changes to the repository, compiles the project, runs the corresponding tests, and notifies the contributor of the test results via an email.

css-exercises

dataset-distillation

Dataset Distillation Fork

datasetcondensation

Black box meta learning on condensed priors.

digitrecognizer

Kaggle Digit Recognizer competition. See https://www.kaggle.com/competitions/digit-recognizer

haskell-algorithms

Learning to solve algorithms with Haskell. Currently I have only written an algorithm that, given a list of edges and a starting vertex and an end vertex, uses BFS to check if there exists a path between them. It takes cycles into account.

jabref

We solved an issue on an open source Graphical Java application for managing BibTeX and biblatex (.bib) databases. A popover was created that visualizes information about journal publications. GSON was parsed and fetched from an API. See branch Journal-information-popup-6189.

java-algorithms-implementation

Refactored code in an open source Algorithms and Data Structures project implemented in Java. See branch addRefactoringDaniel.

label_predictor

This is a program I wrote for a machine learning competition at KTH. It creates a txt file called labels.txt with predicted labels for the y attribute. It trains on the TrainOnMe.csv file and evaluates on EvaluateOnMe.csv.

launch-interceptor-program

As a part of a hypothetical anti-ballistic system, the decide() function generates a boolean signal which determines whether an interceptor should be launched based upon input radar tracking information.

library

Odin project

meta-learning-lstm-condensation

Fork of pytorch implementation of Optimization as a Model for Few-shot Learning

sda_condensed_source

Forked PyTorch implementation of CCSA("Unified Deep Supervised Domain Adaptation and Generalization")

transfer-learning-library

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

x2ct

Added a feature extraction ResUNet autoencoder that infuses 3D topology in the X2CT model.