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Found 12 papers, 8 papers with code
Distributionally Robust Cross Subject EEG Decoding
In this work, we propose a principled approach to perform dynamic evolution on the data for improvement of decoding robustness.
MetaMix: Towards Corruption-Robust Continual Learning With Temporally Self-Adaptive Data Transformation
To make them trustworthy and robust to corruptions deployed in safety-critical scenarios, we propose a meta-learning framework of self-adaptive data augmentation to tackle the corruption robustness in CL.
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Two key challenges arise in this more realistic setting: (i) how to use unlabeled data in the presence of a large amount of unlabeled out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting on previously learned task distributions due to the task distribution shift.
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
To address these problems, for the first time, we propose a principled memory evolution framework to dynamically evolve the memory data distribution by making the memory buffer gradually harder to be memorized with distributionally robust optimization (DRO).
Learning To Learn and Remember Super Long Multi-Domain Task Sequence
We propose a domain shift detection technique to capture latent domain change and equip the meta optimizer with it to work in this setting.
Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process.
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
In this paper, we explore a more practical and challenging setting where task distribution changes over time with domain shift.
Towards Learning to Remember in Meta Learning of Sequential Domains
However, a natural generalization to the sequential domain setting to avoid catastrophe forgetting has not been well investigated.
Attention based Writer Independent Handwriting Verification
The task of writer verification is to provide a likelihood score for whether the queried and known handwritten image samples belong to the same writer or not.
Ranked #1 on
Handwriting Verification
on AND Dataset
Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification
The pattern of Electroencephalogram (EEG) signal differs significantly across different subjects, and poses challenge for EEG classifiers in terms of 1) effectively adapting a learned classifier onto a new subject, 2) retaining knowledge of known subjects after the adaptation.
Parallel Clustering of Single Cell Transcriptomic Data with Split-Merge Sampling on Dirichlet Process Mixtures
Motivation: With the development of droplet based systems, massive single cell transcriptome data has become available, which enables analysis of cellular and molecular processes at single cell resolution and is instrumental to understanding many biological processes.
Sequential Embedding Induced Text Clustering, a Non-parametric Bayesian Approach
In this paper, the documents are modeled as the joint of bags of words, sequential features and word embeddings.
