I joined Bosch Center for Artificial Intelligence. This website will no longer be maintained.
I used to be an assistant professor at Özyeğin University, Computer Science Department. Back then, my research activity focused primarily on Bayesian inference of deep neural nets, transfer learning, meta-learning, few-shot learning, reinforcement learning, and application of these approaches to computer vision and medical image analysis problems. Currently I still do similar things.
- 02.01.2018– Gave an interview to Para, a popular economics magazine in Turkey, about applications of ML to histopathology cancer diagnostics. The link is here.
- 24.11.2017– Our paper at NoF 2017 has received the Best Paper Award.
- 19.10.2017- Received TÜBİTAK 2232 Career Reintegration Grant
- 21.08.2017- Gave an invited talk at the Turkish Machine Learning Summer School 2017 on probabilistic deep learning.
- 09.08.2017- Gave an interview to the pioneer AI company of the Turkish market, Etiya. The link is here.
- Faculty: Özyeğin University, Computer Science, 2017-2018
- Postdoc: Universität Heidelberg, HCI, 2013-16
- PhD: Aalto University, Computer Science, 2008-13
- MSc: Bilkent University, Computer Engineering, 2006-08
- BSc: Hacettepe University, Computer Engineering, 2001-05
- Sampling-free Variational Inference of Bayesian Neural Nets
M. Kandemir, M. Haußmann, F.A. Hamprecht [PDF]
(Full list available here)
- Evidential Deep Learning to Quantify Classification Uncertainty
M. Sensoy, L. Kaplan, M. Kandemir
NIPS, (2018) [PDF]
- Variational Closed-Form Deep Neural Net Inference
Pattern Recognition Letters, (2018), [PDF]
- On context aware DDoS attacks using deep generative networks
G. Gürsun, M. Şensoy, M. Kandemir
- Supervising topic models with Gaussian processes
M. Kandemir, T. Kekeç, R. Yeniterzi
Pattern Recognition, (2018) [PDF]
- Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks
Ö. Narmanlıoğlu, E. Zeydan, M. Kandemir, T. Kranda,
Network of the Future (NoF), Proceedings, (2017) [PDF]
Best Paper Award
- Variational Bayesian multiple instance learning with Gaussian processes
M. Haußmann, F.A. Hamprecht, M. Kandemir
CVPR, Proceedings, (2017) [PDF]
- Gaussian process density counting from weak supervision
M. von Borstel, M. Kandemir, P. Schmidt, M. Rao, K. Rajamani, F.A. Hamprecht
ECCV, Proceedings, (2016) [PDF]
- Asymmetric transfer learning with deep Gaussian processes
ICML, Proceedings, (2015) [PDF] [Code][Talk]
- Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals
J.P. Kauppi *, M. Kandemir *, V.M. Saarinen, L. Hirvenkari, L. Parkkonen, A.Klami, R. Hari, S. Kaski
NeuroImage, (2015) * Equal Contributions [PDF]
- Instance label prediction by Dirichlet process multiple instance learning
M. Kandemir, F.A. Hamprecht
UAI, Proceedings, (2014) [PDF] [Code]