Search Results for author:
Found 16 papers, 10 papers with code
DomainLab: A modular Python package for domain generalization in deep learning
DomainLab is a modular Python package for training user specified neural networks with composable regularization loss terms.
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
We address the online combinatorial choice of weight multipliers for multi-objective optimization of many loss terms parameterized by neural works via a probabilistic graphical model (PGM) for the joint model parameter and multiplier evolution process, with a hypervolume based likelihood promoting multi-objective descent.
Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms
We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules.
Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems
Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection.
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization
We address the task of domain generalization, where the goal is to train a predictive model such that it is able to generalize to a new, previously unseen domain.
More Industry-friendly: Federated Learning with High Efficient Design
Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency.
Grid-GCN for Fast and Scalable Point Cloud Learning
Compared with popular sampling methods such as Farthest Point Sampling (FPS) and Ball Query, CAGQ achieves up to 50X speed-up.
Benchmarking time series classification -- Functional data vs machine learning approaches
In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners.
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning
Aiming at a comprehensive and concise tutorial survey, recap of variational inference and reinforcement learning with Probabilistic Graphical Models are given with detailed derivations.
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution shift.
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals.
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning
Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training.
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper.
Polar Field Correction for HMI Line-of-Sight Synoptic Data
This document provides some technical notes on the polar field correction scheme for the HMI synoptic maps and daily updated synchronic frames.
Solar and Stellar Astrophysics
Face Detection using Deep Learning: An Improved Faster RCNN Approach
In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation.
On the Coordinate System of Space-Weather HMI Active Region Patches (SHARPs): A Technical Note
We describe the coordinate systems of two streams of HMI active region vector data.
Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics
