Industry
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi
Multiple-domain learning contains in turn two sub-challenges.
Scalable Variational Inference for Dynamical Systems
Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
It is a grid free inference approach, which, for fully observable systems is at times competitive with numerical integration.
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication
Qian Yu, Mohammad Maddah-Ali, Salman Avestimehr
For example, replicating the straggling task on another available node is a common approach to deal with stragglers (e.g., [
Beyond Parity: Fairness Objectives for Collaborative Filtering
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data.
Robust Estimation of Neural Signals in Calcium Imaging
Hakan Inan, Murat A. Erdogdu, Mark Schnitzer
Calcium imaging has become an indispensable tool in systems neuroscience research.
Learning Efficient Object Detection Models with Knowledge Distillation
Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker
In this work, we propose a new framework to learn compact and fast object detection networks with improved accuracy using knowledge distillation [20]
Noise-Tolerant Interactive Learning Using Pairwise Comparisons
Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski
In this paper, we attempt to characterize how the access to an easier comparison oracle helps in improving the label and total query complexity.
Collapsed variational Bayes for Markov jump processes
Boqian Zhang, Jiangwei Pan, Vinayak A. Rao
Neural Information Processing Systems http://nips.cc/
Learning Graph Representations with Embedding Propagation
Graph-structured data occurs in numerous application domains such as social networks, bioinfor-matics, natural language processing, and relational knowledge bases.
