Pinned Repositories
comp_creativity
Following the example of the parlor game "Monopoly", a creative system is generating new dimensions for the Monopoly game every time a player passes "START". The new dimension represents a new thematic world in comparison to the classic Monopoly. A creative system is generating new dimensions, locations and action cards for each monopoly round.
datasciencecoursera
datasharing
The Leek group guide to data sharing
dc-custom-component-template
fishpal
Master Thesis on "Comparing Modular Approaches for Parameter-Efficient Fine-Tuning"
jina
An easier way to build neural search in the cloud
machine-learning-articles
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
sfst
Stuttgart Finite State Transducer system
unify-parameter-efficient-tuning-fish-mask
Implementation of paper "Towards a Unified View of Parameter-Efficient Transfer Learning" (ICLR 2022)
lucalila's Repositories
lucalila/fishpal
Master Thesis on "Comparing Modular Approaches for Parameter-Efficient Fine-Tuning"
lucalila/comp_creativity
Following the example of the parlor game "Monopoly", a creative system is generating new dimensions for the Monopoly game every time a player passes "START". The new dimension represents a new thematic world in comparison to the classic Monopoly. A creative system is generating new dimensions, locations and action cards for each monopoly round.
lucalila/datasciencecoursera
lucalila/datasharing
The Leek group guide to data sharing
lucalila/dc-custom-component-template
lucalila/jina
An easier way to build neural search in the cloud
lucalila/machine-learning-articles
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
lucalila/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
lucalila/sfst
Stuttgart Finite State Transducer system
lucalila/unify-parameter-efficient-tuning-fish-mask
Implementation of paper "Towards a Unified View of Parameter-Efficient Transfer Learning" (ICLR 2022)