Search Results for author:
Found 9 papers, 2 papers with code
Generative Modeling of Class Probability for Multi-Modal Representation Learning
In this paper, we propose a novel class anchor alignment approach that leverages class probability distributions for multi-modal representation learning.
Self-Corrective Task Planning by Inverse Prompting with Large Language Models
In robot task planning, large language models (LLMs) have shown significant promise in generating complex and long-horizon action sequences.
Unsupervised Anomaly Detection Using Diffusion Trend Analysis
Conventional anomaly detection techniques based on reconstruction via denoising diffusion model are widely used due to their ability to identify anomaly locations and shapes with high performance.
Growing a Brain with Sparsity-Inducing Generation for Continual Learning
The base network learns knowledge of sequential tasks, and the sparsity-inducing hypernetwork generates parameters for each time step for evolving old knowledge.
Helpful or Harmful: Inter-Task Association in Continual Learning
In this work, we propose a novel approach to differentiate helpful and harmful information for old tasks using a model search to learn a current task effectively.
Ranked #1 on
Continual Learning
on Split MNIST (5 tasks)
Deep Elastic Networks with Model Selection for Multi-Task Learning
To this end, we propose an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks.
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
To this end, we propose a novel network architecture producing multiple networks of different configurations, termed deep virtual networks (DVNs), for different tasks.
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning.
Elastic-Net Regularization of Singular Values for Robust Subspace Learning
The proposed method is applied to a number of low-rank matrix approximation problems to demonstrate its efficiency in the presence of heavy corruptions and to show its effectiveness and robustness compared to the existing methods.
