Melih Kandemir

According to our database, Melih Kandemir authored at least 55 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting.

Busra Asan, Abdullah Akgül, Alper Unal, Melih Kandemir, Gozde Unal

CoRR, 2024

Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty.

Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir

CoRR, 2024

2023

PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison.

Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters

IEEE Trans. Pattern Anal. Mach. Intell., December, 2023

A Deterministic Approximation to Neural SDEs.

Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters

IEEE Trans. Pattern Anal. Mach. Intell., April, 2023

Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems.

Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters

Trans. Mach. Learn. Res., 2023

Demystifying the Myths and Legends of Nonconvex Convergence of SGD.

Aritra Dutta, El Houcine Bergou, Soumia Boucherouite, Nicklas Werge, Melih Kandemir, Xin Li

CoRR, 2023

EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders.

Gulcin Baykal, Melih Kandemir, Gozde Unal

CoRR, 2023

Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures.

Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters

CoRR, 2023

Estimation of Counterfactual Interventions under Uncertainties.

Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir, Martin Fränzle

CoRR, 2023

Sampling-Free Probabilistic Deep State-Space Models.

Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters

CoRR, 2023

BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits.

Nicklas Werge, Abdullah Akgül, Melih Kandemir

CoRR, 2023

PAC-Bayesian Soft Actor-Critic Learning.

Bahareh Tasdighi, Abdullah Akgül, Kenny Kazimirzak Brink, Melih Kandemir

CoRR, 2023

Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures.

Hamish Flynn, David Reeb, Melih Kandemir, Jan R. Peters

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

PAC-Bayesian lifelong learning for multi-armed bandits.

Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters

Data Min. Knowl. Discov., 2022

Continual Learning of Multi-modal Dynamics with External Memory.

Abdullah Akgül, Gozde Unal, Melih Kandemir

CoRR, 2022

Learning interacting dynamical systems with latent Gaussian process ODEs.

Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Traversing Time with Multi-Resolution Gaussian Process State-Space Models.

Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch

Proceedings of the Learning for Dynamics and Control Conference, 2022

Evidential Turing Processes.

Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Inferring the Structure of Ordinary Differential Equations.

Juliane Weilbach, Sebastian Gerwinn, Christian Weilbach, Melih Kandemir

CoRR, 2021

Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes.

Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Differentiable Implicit Layers.

Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters

CoRR, 2020

Deterministic Inference of Neural Stochastic Differential Equations.

Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir

CoRR, 2020

2019

Differential Bayesian Neural Nets.

Andreas Look, Melih Kandemir

CoRR, 2019

Bayesian Prior Networks with PAC Training.

Manuel Haußmann, Sebastian Gerwinn, Melih Kandemir

CoRR, 2019

Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation.

Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Deep Active Learning with Adaptive Acquisition.

Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Variational closed-Form deep neural net inference.

Melih Kandemir

Pattern Recognit. Lett., 2018

Supervising topic models with Gaussian processes.

Melih Kandemir, Taygun Kekeç, Reyyan Yeniterzi

Pattern Recognit., 2018

Sampling-Free Variational Inference of Bayesian Neural Nets.

Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht

CoRR, 2018

Evidential Deep Learning to Quantify Classification Uncertainty.

Murat Sensoy, Lance M. Kaplan, Melih Kandemir

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On Context-Aware DDoS Attacks Using Deep Generative Networks.

Gonca Gürsun, Murat Sensoy, Melih Kandemir

Proceedings of the 27th International Conference on Computer Communication and Networks, 2018

2017

Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks.

Omer Narmanlioglu, Engin Zeydan, Melih Kandemir, Yakup Tarik Kranda

Proceedings of the 8th International Conference on the Network of the Future, 2017

Variational Bayesian Multiple Instance Learning with Gaussian Processes.

Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

Gaussian Process Density Counting from Weak Supervision.

Matthias von Borstel, Melih Kandemir, Philip Schmidt, Madhavi K. Rao, Kumar T. Rajamani, Fred A. Hamprecht

Proceedings of the Computer Vision - ECCV 2016, 2016

Variational Weakly Supervised Gaussian Processes.

Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht

Proceedings of the British Machine Vision Conference 2016, 2016

2015

Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals.

Jukka-Pekka Kauppi, Melih Kandemir, Veli-Matti Saarinen, Lotta Hirvenkari, Lauri Parkkonen, Arto Klami, Riitta Hari, Samuel Kaski

NeuroImage, 2015

Computer-aided diagnosis from weak supervision: A benchmarking study.

Melih Kandemir, Fred A. Hamprecht

Comput. Medical Imaging Graph., 2015

The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors.

Melih Kandemir, Fred A. Hamprecht

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations.

Melih Kandemir, Christian Wojek, Fred A. Hamprecht

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference Munich, Germany, October 5, 2015

Asymmetric Transfer Learning with Deep Gaussian Processes.

Melih Kandemir

Proceedings of the 32nd International Conference on Machine Learning, 2015

Detection of Retinopathy of Prematurity using multiple instance learning.

Priya Rani, Elagiri Ramalingam Rajkumar, Kumar T. Rajamani, Melih Kandemir, Digvijay Singh

Proceedings of the 2015 International Conference on Advances in Computing, 2015

2014

Multi-task and multi-view learning of user state.

Melih Kandemir, Akos Vetek, Mehmet Gönen, Arto Klami, Samuel Kaski

Neurocomputing, 2014

Instance Label Prediction by Dirichlet Process Multiple Instance Learning.

Melih Kandemir, Fred A. Hamprecht

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs.

Melih Kandemir, Chong Zhang, Fred A. Hamprecht

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 2014

Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures.

Melih Kandemir, José C. Rubio, Ute Schmidt, Christian Wojek, Johannes Welbl, Björn Ommer, Fred A. Hamprecht

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 2014

Digital pathology: Multiple instance learning can detect Barrett's cancer.

Melih Kandemir, Annette Feuchtinger, Axel Walch, Fred A. Hamprecht

Proceedings of the IEEE 11th International Symposium on Biomedical Imaging, 2014

Multiple Instance Learning with Response-Optimized Random Forests.

Christoph N. Straehle, Melih Kandemir, Ullrich Köthe, Fred A. Hamprecht

Proceedings of the 22nd International Conference on Pattern Recognition, 2014

2013

Learning Mental States from Biosignals.

Melih Kandemir

PhD thesis, 2013

2012

Unsupervised Inference of Auditory Attention from Biosensors.

Melih Kandemir, Arto Klami, Akos Vetek, Samuel Kaski

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Learning relevance from natural eye movements in pervasive interfaces.

Melih Kandemir, Samuel Kaski

Proceedings of the International Conference on Multimodal Interaction, 2012

2011

An augmented reality interface to contextual information.

Antti Ajanki, Mark Billinghurst, Hannes Gamper, Toni Järvenpää, Melih Kandemir, Samuel Kaski, Markus Koskela, Mikko Kurimo, Jorma Laaksonen, Kai Puolamäki, Teemu Ruokolainen, Timo Tossavainen

Virtual Real., 2011

Multitask Learning Using Regularized Multiple Kernel Learning.

Mehmet Gönen, Melih Kandemir, Samuel Kaski

Proceedings of the Neural Information Processing - 18th International Conference, 2011

2010

Automatic segmentation of colon glands using object-graphs.

Cigdem Gunduz Demir, Melih Kandemir, Akif Burak Tosun, Cenk Sokmensuer

Medical Image Anal., 2010

Inferring object relevance from gaze in dynamic scenes.

Melih Kandemir, Veli-Matti Saarinen, Samuel Kaski

Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, 2010

2009

Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection.

Akif Burak Tosun, Melih Kandemir, Cenk Sokmensuer, Cigdem Gunduz Demir

Pattern Recognit., 2009