Neural Latent Aligner: Cross-trial Alignment for Learning
Representations of Complex, Naturalistic Neural Data

Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data

12 August 2023

Cheol Jun Cho

Edward F. Chang

Gopala K. Anumanchipalli

Papers citing "Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data"

6 / 6 papers shown

Title
Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals Hui Zheng Haiteng Wang Wei-Bang Jiang Zhongtao Chen Li He Pei-Yang Lin Peng-Hu Wei Guo-Guang Zhao Yun-Zhe Liu 50 1 0 19 May 2024
Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition Bulat Khaertdinov S. Asteriadis 37 8 0 07 Oct 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 305 7,434 0 11 Nov 2021
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity Felix Pei Joel Ye D. Zoltowski Anqi Wu Raeed H. Chowdhury ... L. Miller Jonathan W. Pillow Il Memming Park Eva L. Dyer C. Pandarinath 47 86 0 09 Sep 2021
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data Demetres Kostas Stephane Aroca-Ouellette Frank Rudzicz SSL 41 202 0 28 Jan 2021
Soft-DTW: a Differentiable Loss Function for Time-Series Marco Cuturi Mathieu Blondel AI4TS 141 611 0 05 Mar 2017