Daniel Jiwoong Im
Daniel Jiwoong Im
AIFounded
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Title
Cited by
Cited by
Year
Generating images with recurrent adversarial networks
DJ Im, CD Kim, H Jiang, R Memisevic
arXiv preprint arXiv:1602.05110, 2016
1662016
Denoising criterion for variational auto-encoding framework
DIJ Im, S Ahn, R Memisevic, Y Bengio
Thirty-First AAAI Conference on Artificial Intelligence, 2017
742017
Quantitatively evaluating GANs with divergences proposed for training
DJ Im, H Ma, G Taylor, K Branson
arXiv preprint arXiv:1803.01045, 2018
402018
An empirical analysis of deep network loss surfaces
DJ Im, M Tao, K Branson
272016
Neural machine translation with gumbel-greedy decoding
J Gu, DJ Im, VOK Li
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
222018
Generative adversarial parallelization
DJ Im, H Ma, CD Kim, G Taylor
arXiv preprint arXiv:1612.04021, 2016
172016
Semisupervised hyperspectral image classification via neighborhood graph learning
DJ Im, GW Taylor
IEEE Geoscience and Remote Sensing Letters 12 (9), 1913-1917, 2015
122015
An empirical analysis of the optimization of deep network loss surfaces
DJ Im, M Tao, K Branson
arXiv preprint arXiv:1612.04010, 2016
112016
Conservativeness of untied auto-encoders
DJ Im, MI Belghazi, R Memisevic
Thirtieth AAAI Conference on Artificial Intelligence, 2016
72016
Neural network regularization via robust weight factorization
J Rudy, W Ding, DJ Im, GW Taylor
arXiv preprint arXiv:1412.6630, 2014
62014
Generative adversarial metric
DJ Im, CD Kim, H Jiang, R Memisevic
52016
Learning a metric for class-conditional KNN
DJ Im, GW Taylor
2016 International Joint Conference on Neural Networks (IJCNN), 1932-1939, 2016
32016
Stochastic Neighbor Embedding under f-divergences
DJ Im, N Verma, K Branson
arXiv preprint arXiv:1811.01247, 2018
22018
Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data
DJ Im, S Prakhya, J Yan, S Turaga, K Branson
arXiv preprint arXiv:1906.03214, 2019
12019
Scoring and classifying with Gated auto-encoders
DJ Im, GW Taylor
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015
12015
Understanding minimum probability flow for RBMs under various kinds of dynamics
DJ Im, E Buchman, GW Taylor
arXiv preprint arXiv:1412.6617, 2014
12014
Are skip connections necessary for biologically plausible learning rules?
DJ Im, R Patil, K Branson
arXiv preprint arXiv:2001.01647, 2019
2019
Model-Agnostic Meta-Learning using Runge-Kutta Methods
DJ Im, Y Jiang, N Verma
arXiv preprint arXiv:1910.07368, 2019
2019
An empirical investigation of minimum probability flow learning under different connectivity patterns
DJ Im, E Buchman, GW Taylor
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015
2015
Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems
J Im
2015
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