Multiple Kernel Learning (MKL) aims to learn the kernel in an SVM from training data. Many MKL formulations have been proposed and some have proved effective in certain applications. Nevertheless, as MKL is a nascent field, many more formulations need to be developed ...
Varma. Spg-gmkl: Generalized multiple kernel learning with a million kernels. In Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining (KDD).Jain, A., Vishwanathan, S. V. N., and Varma, M. Spg- gmkl: Generalized multiple kernel learning with a mil- ...
The multiple-instance learning (MIL) model has been successful in numerous application areas. Recently, a generalization of this model and an algorithm for it were introduced, showing significant advantages over the conventional MIL model on certain application areas. Unfortunately, that algorithm is no...
Learning Disentanglement with Decoupled Labels for Vision-Language Navigation 3316 16:00 Explicit Image Caption Editing 3318 19:00 Acknowledging the Unknown for Multi-label Learning with Single Positive Labels 3314 13:00 A Large-scale Multiple-objective Method for Black-box Attack against Object Detectio...
It has been shown that multi-view learning performance can be significantly enhanced if the fractional-order idea is considered in the SSS situations. Motivated by recent progress in multiple view learning, we in this paper propose a new multi-view feature extraction method via fractional spectral ...
Learning multiple [SEG] tokens. We continue to ab- late the multiple [SEG] tokens, which is another core de- sign of GSVA. After removing [REJ] token, we then re- duce the number of [SEG] tokens to 1, which is identical to LISA [32] with the referring...
Figure 1 provides an overview of the proposed deep learning-based segmentation and downstream analyses framework for WSI slide images corresponding to multiple different cancer sites. Datasets used for this study The proposed framework was validated on multiple open-source datasets which included CAMELYON...
Other works are focused onobliqueclassification trees, i.e., on the usage of hyperplanes to recursively divide the feature space. In this case, the tree model exploits amultivariatelinear function at each branch node so that multiple features are involved in the decision process (Murthy et al....
(2009). Learning multiple layers of features from tiny images. Master’s thesis, Department of Computer Science, University of Toronto. Kuhn, H. W. (1955). The hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1–2), 83–97. Article MathSciNet MATH Google ...
Likewise for learning (personalized) MNL model parameters, Kallus and Udell (2016) develop an exploration/exploitation algorithm that requires to offer assortments to be chosen uniformly at random for multiple rounds. Cheung and Simchi-Levi (2017) criticize that this requirement can be problematic in...