Deep Learning
Glow: Generative Flow with Invertible 1x1 Convolutions
Durk P. Kingma, Prafulla Dhariwal
Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference,
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning
Tyler Scott, Karl Ridgeway, Michael C. Mozer
Lempitsky, 2016) is most robust.
Neural Edit Operations for Biological Sequences
Satoshi Koide, Keisuke Kawano, Takuro Kutsuna
The evolution of biological sequences, such as proteins or DNAs, is driven by the three basic edit operations: substitution, insertion, and deletion.
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin
Neural Information Processing Systems http://nips.cc/
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander Schwing, Murali Annavaram, Salman Avestimehr
Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation.
Reinforced Continual Learning
Neural Information Processing Systems http://nips.cc/
Re-evaluating evaluation
David Balduzzi, Karl Tuyls, Julien Perolat, Thore Graepel
Neural Information Processing Systems http://nips.cc/
A Retrieve-and-Edit Framework for Predicting Structured Outputs
Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy S. Liang
Neural Information Processing Systems http://nips.cc/
Domain-Invariant Projection Learning for Zero-Shot Recognition
An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen
This is made possible by learning a projection between a feature space and a semantic space (e.g.
Symbolic Graph Reasoning Meets Convolutions
Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing
Beyond local convolution networks, we explore how to harness various external human knowledge for endowing the networks with the capability of semantic global reasoning.