16 citations · 55 across the 22 of their papers we have counts for
8 papers · 1 filter
RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning
Jinhan Liu, Mahsa Shoaran
Affective and cognitive disorders manifest as distributed, time-varying brain network dynamics across regions, channels, and time, challenging robust representation learning from E…
BrainDistill: Implantable Motor Decoding with Task-Specific Knowledge Distillation
Yuhan Xie, Jinhan Liu, Xiaoyong Ni +11
Transformer-based neural decoders with large parameter counts, pre-trained on large-scale datasets, have recently outperformed classical machine learning models and small neural ne…
rETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data
Yuhan Xie, William Cappelletti, Mahsa Shoaran +1
Deep neural networks for time series must capture complex temporal patterns, to effectively represent dynamic data. Self- and semi-supervised learning methods show promising result…
Linear Attention for Efficient Bidirectional Sequence Modeling
Arshia Afzal, Elias Abad Rocamora, Leyla Naz Candogan +5
Linear Transformers and State Space Models have emerged as efficient alternatives to softmax Transformers for causal sequence modeling, enabling parallel training via matrix multip…
XTab: Cross-table Pretraining for Tabular Transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson +3
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-su…
Tree in Tree: from Decision Trees to Decision Graphs
Bingzhao Zhu, Mahsa Shoaran
Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree…
Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection
Bingzhao Zhu, Mahsa Shoaran
Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject.…
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification
Bingzhao Zhu, Masoud Farivar, Mahsa Shoaran
Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT device…