A benchmark for comparing early-exiting and conditional computation methods and models
Positions: Student Researcher
Created: 2023-10-22 Deadline:
Location: Poland
The project aims to create a unified benchmark for multiple methods that reduce the inference time of deep learning models. We begin by focusing on early-exiting methods. You task will be to reimplement a conditional computation method from a selected published paper into our common codebase. Conditional computation methods are usually simple to implement and provide significant computational cost savings. We intend to publish the benchmark with the accompanying analysis as a paper in a rank A* conference. We have tasks appropriate for both beginners and people with experience.
General must-have requirements
The student needs to know how to program in PyTorch (In order to undestand the present code and implement new methods).
Contact: Bartosz Wójcik, Maciej Wołczyk (bartwojc [ at ] gmail.com)
Project's lab:
GMUM (Group of Machine Learning Research) is a group of researchers working on various aspects of machine learning, and in particular deep learning - in both fundamental and applied settings. The group is led by prof. Jacek Tabor. We are based in the Jagiellonian University in the beautiful city of Kraków, Poland.
Some of the research directions our group pursues include:
- Generative models: efficient training and sampling; inpainting; super-resolution,
- Theoretical understanding of deep learning and optimization,
- Natural language processing,
- Drug design and cheminformatics,
- Unsupervised learning and clustering,
- Computer vision and medical image analysis.
In 2023, we organized the second edition of Machine Learning Summer School (MLSS^S) with a focus on Applications in Science. We invite participants to collaborate with us on various ongoing research projects - learn more here.
See lab's page