I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UCL Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction in knowledge graphs. I did my master in computer science at the University of Lugano, where I was advised by Fabio Crestani. During my master, I did two research internship at Microsoft under Chris Basoglu, where I worked speech recognition and memory efficient deep learning algorithms. I did a bachelor in applied mathematics with The Open University while working as a software engineer in the automation industry.
In 2013, I also took part in Kaggle competitions where I peaked at world rank 63 (top 0.22%).
Feel free to contact me at firstname.lastname@example.org; if you have questions regarding deep learning, I prefer that you post your questions as comments on one of my blog posts (or on this page if it does not fit to any blog post); this way all people can profit from your questions and my answers.
My main research thesis is that computational efficient methods will accelerate and enable progress in and understanding of deep learning. In particular, I am interested in:
- Sparse learning
- Representation learning
- Understanding deep learning
- Neuro-inspired deep learning
- Hardware optimized deep learning
Sparse Networks from Scratch: Faster Training without Losing Performance. Tim Dettmers, Luke Zettlemoyer. under review. [arXiv] [bib] [code] [blog post] [Q&A]
Jack the Reader – A Machine Reading Framework, Dirk Weissenborn, Pasquale Minervini, Tim Dettmers, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Pontus Stenetorp, Sebastian Riedel. ACL, 2018. [arXiv] [bib] [code]
8-Bit Approximations for Parallelism in Deep Learning, Tim Dettmers, 4th International Conference on Learning Representations (ICLR), 2016 (conference track, acceptance rate 25%). [arXiv] [bib] [code] [data]
Computational and Parallel Deep Learning Performance Benchmarks for the Xeon Phi, Tim Dettmers, Hanieh Soleimani, technical report, 2016.
2018/2019 Jeff Dean – Heidi Hopper Endowed Regental Fellowship
2016/2017 Google Scholarship for Students with Disabilities
2016 ICRL 2016 Travel Award
2011/2012 Best regional graduate: Mathematical-technical Software Developer
2017-09 – Now UCL Machine Reading Group, London
2017-06 – 2017-09 Microsoft Research, Redmond
2017-01 – 2017-06 UCL Machine Reading Group, London
2016-06 – 2016-09 Microsoft Research, Redmond
I am reviewing for Knowledge and Information Systems.