Deep Learning
Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming
Fei Wang, James Decker, Xilun Wu, Gregory Essertel, Tiark Rompf
Neural Information Processing Systems http://nips.cc/
ATOMO: Communication-efficient Learning via Atomic Sparsification
Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, Stephen Wright
Neural Information Processing Systems http://nips.cc/
Training Deep Neural Networks with 8-bit Floating Point Numbers
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan
Neural Information Processing Systems http://nips.cc/
Contamination Attacks and Mitigation in Multi-Party Machine Learning
Multi-party machine learning allows several parties (e.g., hospitals, banks, government agencies) to
Chain of Reasoning for Visual Question Answering
Chenfei Wu, Jinlai Liu, Xiaojie Wang, Xuan Dong
Neural Information Processing Systems http://nips.cc/
Deepcode: Feedback Codes via Deep Learning
Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
The ubiquitous digital communication enabled via wireless (e.g.
Modular Networks: Learning to Decompose Neural Computation
Louis Kirsch, Julius Kunze, David Barber
Scaling model capacity has been vital in the success of deep learning.
Diverse Ensemble Evolution: Curriculum Data-Model Marriage
Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes
Neural Information Processing Systems http://nips.cc/
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output.
Extracting Relationships by Multi-Domain Matching
Yitong Li, michael Murias, geraldine Dawson, David E. Carlson
Neural Information Processing Systems http://nips.cc/