My year of reading in 2024: https://t.co/mYR8v8lsxE
I read 99 papers in 2024. Complete list: https://t.co/PLqCgGKK3o
Top 15 favorite papers that I found particularly interesting and/or well-written (in alphabetical order):
Go and work with Antônio! 🤗
He’s an incredible supervisor. Smart, full of ideas and fun to work with. Doing a PhD with him cannot go wrong!
If you consider applying and have questions, hit me up!
Our paper "Evaluating Regression and Probabilistic Methods for ECG-Based Electrolyte Prediction" has been published in Scientific Reports.
Paper: https://t.co/AYxA9kS8d9
Code: https://t.co/ajfNlYAPm2
Work done together with @danigedon, @ahortaribeiro and Uppsala collaborators.
One year ago, I wrote a blog post called "The How and Why of Reading 300 Papers in 5 Years", and now I recently reached 400 read papers on the Github repository I use to track and organize my reading: https://t.co/PLqCgGKcdQ
Curious about the latest developments within ML for computational pathology? I can definitely recommend reading these 3 papers from the @AI4Pathology lab:
1. Towards a General-Purpose Foundation Model for Computational Pathology (Nature Medicine, 2024):
https://t.co/zxGbesdJ4h
I recently presented our paper "How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?" in the @RISEsweden ML seminars.
Paper: https://t.co/oeU27wj8PX
Code: https://t.co/dettfQJUVY
Slides: https://t.co/vDIdugmHgh
Video: https://t.co/P4jdEJbsOU
Our follow-up paper "Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models" will also be presented at a CVPR workshop this summer.
Paper: https://t.co/XWpmyU6F8U
Code: https://t.co/40ugzMWbPU
Our paper "Controlling Vision-Language Models for Multi-Task Image Restoration" will be presented at #ICLR2024 next week.
Paper: https://t.co/0HMsmRKEF4
Code: https://t.co/40ugzMWbPU
Project page: https://t.co/nni7nu4Vrx
Work lead by @ZiweiRo, with collaborators in Uppsala.
Our paper "Controlling Vision-Language Models for Multi-Task Image Restoration" will be presented at #ICLR2024 next week.
Paper: https://t.co/0HMsmRKEF4
Code: https://t.co/40ugzMWbPU
Project page: https://t.co/nni7nu4Vrx
Work lead by @ZiweiRo, with collaborators in Uppsala.
@nikitadurasov I think it could work well as the textbook for a first deep learning course, which follows a course on more traditional ML (e.g. https://t.co/FrTLsXPThi). Then you could have specialized courses on CV, RL, NLP etc.
Just finished reading the "Understanding Deep Learning" book: https://t.co/hDs7tNqiRF
Chapters that I particularly liked / found useful: 1, 2, 8, 9, 12, 14, 15, 17, 18, 21.
It contains a lot of neat/illustrative figures that really help to explain various concepts and methods.
Quite enjoyed reviewing the batch of 6 papers I was assigned for #ICML2024, probably the highest overall quality/relevance for any conference I've done so far.
Two months ago, I defended my thesis "Towards Accurate and Reliable Deep Regression Models".
Pdf: https://t.co/YmPt6hMvZX
Slides: https://t.co/SI4HN1r7fP
Video: https://t.co/04dx6fMPsA
I also joined @karolinskainst as a postdoc, working on ML for computational pathology.