This method is a generalization of thewell-known single-task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learnedfeatures common across the tasks. We prove that the method is equivalent to solving a convex optimization problem for whichthere ...
http://scholar.google.com/scholar?q=%222008%22+Convex+Multi-task+Feature+Learning http://dl.acm.org/citation.cfm?id=1455903.1455908&preflayout=flat#citedby Quotes Author Keywords Collaborative Filtering;Inductive Transfer;Kernels;Multi-Task Learning;Regularization;Transfer Learning;Vector-Valued Functions...
First, we extend the method from a 2-task space to an n-task space. This expands the dimensionality of the task interpolation, providing more tasks choice for subsequent convex combination interpolation. Second, in the multi-task space, we randomly select multiple tasks and combine them using ...
Oreshkin B, Rodríguez López P, Lacoste A (2018) Tadam: Task dependent adaptive metric for improved few-shot learning. Adv Neural Inf Process Syst 31:721–731 MATH Google Scholar Pelckmans K, De Brabanter J, Suykens JA, De Moor B (2005) Convex clustering shrinkage. In: PASCAL worksh...
3.4. Multi convex decomposition – Figure 7 Having a learnable pipeline for a single convex object, we can now expand the expressivity of our model by repre- senting generic non-convex objects as compositions of con- vexes [66]. To achieve this task an encoder E outputs a low- 4 34 ...
%convexAdam + Hyperparameter Optimisation TMI @article{siebert2024convexadam, title={ConvexAdam: Self-Configuring Dual-Optimisation-Based 3D Multitask Medical Image Registration}, author={Siebert, Hanna and Gro{\ss}br{\"o}hmer, Christoph and Hansen, Lasse and Heinrich, Mattias P}, journal={IEEE...
Qiao. A discriminative feature learning approach for deep face recognition. In European Conference on Computer Vision, pages 499–515. Springer, 2016. [27] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao. Joint face detection and alignment using multitask cascaded convolutional net- works. IEEE ...
The main components of Transformer are multi-head self-attention, position-wise feed-forward, and residual connection. The self-attention projects the input feature into three spaces: query, key, and value. By concatenating the multi-head attention, we can let the model learn more information. ...
In this work, we only consider methods not using anomaly labels in learning, in such cases, the problem becomes a binary classification task with much less challenge. In the past, trajectories were the feature of choice to model patterns in visual surveillance scenarios [4]. Trajectory based ...
As for the regression estimation problem, one is given the training samples of input vectors {xi}i=1n along with the corresponding targets {yi}i=1n, and the task is to find a regression function that best represents the relation between input vectors and their targets. A nonlinear regressor ...