W Ge, Y Yu - Proceedings of the IEEE conference on …, 2017 - openaccess.thecvf.com
Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases …
O Chapelle, Y Chang - Proceedings of the learning to rank …, 2011 - proceedings.mlr.press
Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly …
D Wu, B Wang, D Precup… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Electric load forecasting, especially short-term load forecasting (STLF), is becoming more and more important for power system operation. We propose to use multiple kernel learning …
As information retrieval researchers, we not only develop algorithmic solutions to hard problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …
T Trzcinski, M Christoudias, P Fua… - Proceedings of the …, 2013 - openaccess.thecvf.com
Binary keypoint descriptors provide an efficient alternative to their floating-point competitors as they enable faster processing while requiring less memory. In this paper, we propose a …
A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of …
J Chen, L Tang, J Liu, J Ye - … of the 26th annual international conference …, 2009 - dl.acm.org
Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. In this paper, we consider the problem of learning shared …
T Trzcinski, M Christoudias… - IEEE transactions on …, 2014 - ieeexplore.ieee.org
We propose a novel and general framework to learn compact but highly discriminative floating-point and binary local feature descriptors. By leveraging the boosting-trick we first …