Artificial Intelligence Camp
24
May 2021, Monday

This summer, we are organizing an online AI Summer Camp for high school students! There will be introductory-level courses as well as practical sessions in various subjects of AI such as Computer Vision, Robotics, and Natural Language Processing.

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KUIS AI Center PhD and MSc Fellowships Information Webinar
06
May 2021, Thursday

Join our webinar to find out more about the Koç University Iş Bankası Artificial Intelligence Research Center Ph.D. and Master Fellowships for Fall 2021 and Spring 2022 Admission. We are going to hold an “Ask me Anything” event online on May 15th, between 15:00-17:00. We will briefly introduce faculty members, research areas, infrastructure and funding opportunities, and answer questions from the participants. Send your questions before the webinar and make sure you check the KUIS AI Center research areas, faculty members and projects on our website. Please fill out the form if interested: https://kocun.zoom.us/webinar/register/3316194390839/WN_jsgIGEl-SAGmWemPIKlseg

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Assoc. Prof. Dr. Aykut Erdem has received the Science Academy’s 2021 Young Scientists Award (BAGEP 2021)
06
April 2021, Tuesday

We congratulate  Assoc. Prof. Dr. Aykut Erdem, Committee Member of KUIS AI Center, who is awarded the 2021 Science Academy Young Scientists Award Program (BAGEP 2021).

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Deep Learning for Image/Video Restoration and Compression
15
January 2021, Friday

We are experiencing a paradigm shift to learned nonlinear models in signal-image-video processing. There are no theoretical bounds on the performance limits of learned models, it is not possible to tell how much the results can be further improved. In order to fill this gap in the state of the art, this project aims following.

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CRAFT: A Benchmark for Causal Reasoning About Forces and InTeractions
29
December 2020, Tuesday

Our preliminary results demonstrate that even though these tasks are very intuitive for humans, the implemented baselines could not cope with the underlying challenges.

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AffectON: Incorporating Affect Into Dialog Generation
21
December 2020, Monday

Recent paper by Zana Buçinca, Yücel Yemez, Engin Erzin, and Metin Sezgin: "AffectON: Incorporating Affect Into Dialog Generation" has been accepted at IEEE Transactions on Affective Computing.

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Cyberphysical Blockchain-Enabled Peer-to-Peer Energy Trading
18
December 2020, Friday

Recent paper "Cyberphysical Blockchain-Enabled Peer-to-Peer Energy Trading" is published as part of the Cover Feature of IEEE Computer. Three blockchain-based energy-trading models are proposed to overcome the technical challenges and market barriers as well as enhance the adoption of this disruptive technology.

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A new single input multiple output deep temporal regression network (DTRN)
17
December 2020, Thursday

A new single input multiple output deep temporal regression network (DTRN) to detect the vocal tract (VT) contour and the separation boundary between different articulators.

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Available Positions: Learning Rare Events in Autonomous Driving
16
December 2020, Wednesday

We're looking for highly motivated graduate students for our project "The Road Less Travelled: One-Shot Learning of Rare Events in Autonomous Driving", also in collaboration with Joao Henriques from the University of Oxford and Luca Bertinetto from the autonomous driving startup Five.

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Postdoctoral Researcher
13
December 2020, Sunday

We're looking for talented and motivated post-doctoral researchers in multiple areas of AI and ML. We provide a competitive benefits package, please check our page for details and spread the word!

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College of Engineering Outstanding Faculty Award
13
December 2020, Sunday

Assistant Professor Didem Unat, Department of Computer Engineering, is awarded with the 2019-2020 College of Engineering Outstanding Faculty Award.

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Reward Learning From Very Few Demonstrations
10
December 2020, Thursday

We introduce a reward learning framework that extracts dense rewards from learned perceptual goals in a robotic skill learning from demonstration setting. We show the efficacy of the learned rewards on various Policy Search methods both in simulation and real robot.

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