Search Results for author:
Found 7 papers, 1 papers with code
Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism
Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels, enhancing the performance of each individual task.
Multi-Objective Trajectory Planning with Dual-Encoder
In this paper, we propose a two-stage approach to accelerate time-jerk optimal trajectory planning.
Sparsity via Sparse Group $k$-max Regularization
For the linear inverse problem with sparsity constraints, the $l_0$ regularized problem is NP-hard, and existing approaches either utilize greedy algorithms to find almost-optimal solutions or to approximate the $l_0$ regularization with its convex counterparts.
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action Recognition
Human Action Recognition plays a driving engine of many human-computer interaction applications.
Ranked #19 on
Action Recognition
on NTU RGB+D
Fast Contextual Scene Graph Generation With Unbiased Context Augmentation
Therefore, at the context level, we can produce diverse context descriptions by using a context augmentation method based on the original dataset.
Piecewise Linear Neural Networks and Deep Learning
To apply PWLNN methods, both the representation and the learning have long been studied.
Efficient hinging hyperplanes neural network and its application in nonlinear system identification
It is proved that for every EHH neural network, there is an equivalent adaptive hinging hyperplanes (AHH) tree, which was also proposed based on the model of HH and find good applications in system identification.
