Search Results for author:
Found 9 papers, 5 papers with code
Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization
Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning.
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy
Our proposed framework involves three stages of learning, which are formulated as a three-level optimization problem: (i) learning to group problems into different subgroups; (ii) learning group-specific sub-models for problem-solving; and (iii) updating group assignments of training examples by minimizing the validation loss.
Saliency-Aware Neural Architecture Search
At the second stage, saliency maps are generated using the trained model.
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation
Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving.
Learning from Mistakes -- A Framework for Neural Architecture Search
We propose a novel machine learning method called Learning From Mistakes (LFM), wherein the learner improves its ability to learn by focusing more on the mistakes during revision.
A Framework for Multisensory Foresight for Embodied Agents
The framework was tested and validated with a dataset containing 4 sensory modalities (vision, haptic, audio, and tactile) on a humanoid robot performing 9 behaviors multiple times on a large set of objects.
Learning by Self-Explanation, with Application to Neural Architecture Search
In our approach, an explainer model improves its learning ability by trying to clearly explain to an audience model regarding how a prediction outcome is made.
DSRNA: Differentiable Search of Robust Neural Architectures
To address this problem, we propose methods to perform differentiable search of robust neural architectures.
Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling
Annotation results are in agreement to those obtained using other tools that utilize additional information in the form of spectral signatures.
