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Title
Cited by
Cited by
Year
Emonets: Multimodal deep learning approaches for emotion recognition in video
SE Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, K Konda, ...
Journal on Multimodal User Interfaces 10, 99-111, 2016
5192016
Recurrent neural networks for emotion recognition in video
S Ebrahimi Kahou, V Michalski, K Konda, R Memisevic, C Pal
Proceedings of the 2015 ACM on international conference on multimodal …, 2015
4832015
Combining modality specific deep neural networks for emotion recognition in video
SE Kahou, C Pal, X Bouthillier, P Froumenty, Ç Gülçehre, R Memisevic, ...
Proceedings of the 15th ACM on International conference on multimodal …, 2013
4442013
Learning visual odometry with a convolutional network
K Konda, R Memisevic
International Conference on Computer Vision Theory and Applications 2, 486-490, 2015
2152015
Dropout as data augmentation
K Konda, X Bouthillier, R Memisevic, P Vincent
stat 1050, 29, 2015
167*2015
Modeling deep temporal dependencies with recurrent grammar cells""
V Michalski, R Memisevic, K Konda
Advances in neural information processing systems 27, 2014
1342014
Zero-bias autoencoders and the benefits of co-adapting features
K Konda, R Memisevic, D Krueger
International conference on learning representations, 2015
692015
How far can we go without convolution: Improving fully-connected networks
Z Lin, R Memisevic, K Konda
International conference in learning representations Workshop Track, 2016
622016
A unified approach to learning depth and motion features
K Konda, R Memisevic
Proceedings of the 2014 Indian Conference on Computer Vision Graphics and …, 2014
56*2014
Real time interaction with mobile robots using hand gestures
KR Konda, A Königs, H Schulz, D Schulz
Proceedings of the seventh annual ACM/IEEE international conference on Human …, 2012
442012
The role of spatio-temporal synchrony in the encoding of motion.
KR Konda, R Memisevic, V Michalski
ICLR (Poster), 2014
30*2014
Unsupervised relational feature learning for vision
KR Konda
Goethe University Frankfurt, 2016
92016
Building effective deep neural network architectures one feature at a time
M Mundt, T Weis, K Konda, V Ramesh
arXiv preprint arXiv:1705.06778, 2017
22017
EmoNets: Multimodal deep learning approaches for emotion recognition in video
S Ebrahimi Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, ...
arXiv e-prints, arXiv: 1503.01800, 2015
22015
Real-time activity recognition via deep learning of motion features.
K Konda, P Chandrashekhariah, R Memisevic, J Triesch
ESANN, 2015
12015
Only sparsity based loss function for learning representations
V Bakaraju, KR Konda
arXiv preprint arXiv:1903.02893, 2019
2019
Building effective deep neural networks one feature at a time
M Mundt, T Weis, K Konda, V Ramesh
2018
ARTICLE 2: RECURRENT NEURAL NETWORKS FOR EMOTION RECOGNITION IN VIDEO
SE Kahou, V Michalski, K Konda, R Memisevic, C Pal
Titre, 67, 2016
2016
ARTICLE 1: EMONETS: MULTIMODAL DEEP LEARNING APPROACHES FOR EMOTION RECOGNITION IN VIDEO
SE Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, K Konda, ...
Titre, 42, 2016
2016
Center for Cognition and Computation
C Becker, M Rammensee, K Konda, S Veeravasarapu, T Weis, ...
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