Industry
Protein Interface Prediction using Graph Convolutional Networks
Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur
Neural Information Processing Systems http://nips.cc/
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Privacy preserving is an important issue in learning.
Preventing Gradient Explosions in Gated Recurrent Units
Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura
Neural Information Processing Systems http://nips.cc/
Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander J. Ratner, Henry Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels.
Interactive Submodular Bandit
Lin Chen, Andreas Krause, Amin Karbasi
Neural Information Processing Systems http://nips.cc/
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control
Fanny Yang, Aaditya Ramdas, Kevin G. Jamieson, Martin J. Wainwright
This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test their
Reconstructing perceived faces from brain activations with deep adversarial neural decoding
Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob van Lier, Marcel A. J. van Gerven
At the same time, the use of deep learning as the basis for neural decoding has received less widespread attention.
Estimating Mutual Information for Discrete-Continuous Mixtures
Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Estimation of mutual information from observed samples is a basic primitive in machine learning, useful in several learning tasks including correlation mining, information bottleneck, Chow-Liu tree, and conditional independence testing in (causal) graphical models.
Tensor Biclustering
Soheil Feizi, Hamid Javadi, David Tse
Consider a dataset where data is collected on multiple features of multiple individuals over multiple times.
QMDP-Net: Deep Learning for Planning under Partial Observability
Peter Karkus, David Hsu, Wee Sun Lee
This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning.