Search Results for author:
Found 14 papers, 3 papers with code
LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records
We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data.
Discriminative Radial Domain Adaptation
Domain adaptation methods reduce domain shift typically by learning domain-invariant features.
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition
Finally, in the Information Fuser, we explore varied strategies to combine the Sequence Reconstructor and Contrastive Motion Learner, and propose to capture postures and motions simultaneously via a knowledge-distillation based fusion strategy that transfers the motion learning from the Contrastive Motion Learner to the Sequence Reconstructor.
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation
Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc.
Interventional Domain Adaptation
To address this issue, we intervene in the learning of feature discriminability using unlabeled target data to guide it to get rid of the domain-specific part and be safely transferable.
Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence
We reveal the incoherence between the widely-adopted empirical domain adversarial training and its generally-assumed theoretical counterpart based on $\mathcal{H}$-divergence.
Deep Robotic Prediction with hierarchical RGB-D Fusion
Robotic arm grasping is a fundamental operation in robotic control task goals.
Linear Context Transform Block
In this work, we first revisit the SE block, and then present a detailed empirical study of the relationship between global context and attention distribution, based on which we propose a simple yet effective module, called Linear Context Transform (LCT) block.
C^3 Framework: An Open-source PyTorch Code for Crowd Counting
This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F).
Policy Optimization with Stochastic Mirror Descent
Improving sample efficiency has been a longstanding goal in reinforcement learning.
Expected Sarsa($λ$) with Control Variate for Variance Reduction
In this paper, for reducing the variance, we introduce control variate technique to $\mathtt{Expected}$ $\mathtt{Sarsa}$($\lambda$) and propose a tabular $\mathtt{ES}$($\lambda$)-$\mathtt{CV}$ algorithm.
Bayesian Uncertainty Matching for Unsupervised Domain Adaptation
By imposing distribution matching on both features and labels (via uncertainty), label distribution mismatching in source and target data is effectively alleviated, encouraging the classifier to produce consistent predictions across domains.
Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
In this paper, we present a method for learning domain-invariant local feature patterns and jointly aligning holistic and local feature statistics.
Object-Level Representation Learning for Few-Shot Image Classification
Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task.
