Search Results for author:
Found 13 papers, 6 papers with code
MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling
Specifically, MixEHR-Nest detects multiple subtopics from each phenotype topic, whose prior is guided by the expert-curated phenotype concepts such as Phenotype Codes (PheCodes) or Clinical Classification Software (CCS) codes.
Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents
The key is visual grounding models that can accurately map diverse referring expressions of GUI elements to their coordinates on the GUI across different platforms.
Ranked #2 on
Natural Language Visual Grounding
on ScreenSpot
Specification-guided temporal logic control for stochastic systems: a multi-layered approach
First, we develop a multi-layered discretization-based approach with variable precision by combining abstraction layers with different precision parameters.
Modelling and Kron reduction of power flow networks in directed graphs
Electrical grids are large-sized complex systems that require strong computing power for monitoring and analysis.
Robust Meta-Representation Learning via Global Label Inference and Classification
Empirically, MeLa outperforms existing methods across a diverse range of benchmarks, in particular under a more challenging setting where the number of training tasks is limited and labels are task-specific.
Schedule-Robust Online Continual Learning
A continual learning (CL) algorithm learns from a non-stationary data stream.
The Role of Global Labels in Few-Shot Classification and How to Infer Them
Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data.
Structured Prediction for Conditional Meta-Learning
We derive task-adaptive structured meta-learning (TASML), a principled framework that yields task-specific objective functions by weighing meta-training data on target tasks.
Support-weighted Adversarial Imitation Learning
To address the challenges, we propose Support-weighted Adversarial Imitation Learning (SAIL), a general framework that extends a given AIL algorithm with information derived from support estimation of the expert policies.
Support-guided Adversarial Imitation Learning
We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms.
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals.
Real-Time Workload Classification during Driving using HyperNetworks
Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics.
MAGAN: Margin Adaptation for Generative Adversarial Networks
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adaptive hinge loss function.
