Deep Learning
A Meta-Learning Perspective on Cold-Start Recommendations for Items
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle
Neural Information Processing Systems http://nips.cc/
Learning What and Where to Draw
Scott E. Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee
Neural Information Processing Systems http://nips.cc/
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks
Kyuhong Shim, Minjae Lee, Iksoo Choi, Yoonho Boo, Wonyong Sung
We propose a fast approximation method of a softmax function with a very large vocabulary using singular value decomposition (SVD).
Adversarial Symmetric Variational Autoencoder
Yuchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin
Neural Information Processing Systems http://nips.cc/
Learning to See Physics via Visual De-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum
Neural Information Processing Systems http://nips.cc/
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
Liwei Wang, Alexander Schwing, Svetlana Lazebnik
This paper explores image caption generation using conditional variational auto-encoders (CV AEs).
Selective Classification for Deep Neural Networks
Neural Information Processing Systems http://nips.cc/
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
Steven Cheng-Xian Li, Benjamin M. Marlin
Neural Information Processing Systems http://nips.cc/
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
Anirudh Goyal ALIAS PARTH GOYAL, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio
Moreover, the detailed balance condition is highly restrictive: energy based models corresponding to neural networks must have symmetric weights, unlike biological neural circuits.
Contrastive Learning for Image Captioning
Image captioning, a popular topic in computer vision, has achieved substantial progress in recent years.