D-FaST: Cognitive Signal Decoding with Disentangled Frequency-Spatial-Temporal Attention
National University of Defense Technology（国防科技大学）；Academy of Medical Engineering and Translational Medicine, Tianjin University（天津大学医学工程与转化医学研究院）；School of Big Data and Artificial Intelligence, Xinyang College（信阳学院大数据与人工智能学院）
Cognitive Language Processing (CLP), situated at the intersection of Natural Language Processing (NLP) and cognitive science, plays a progressively pivotal role in the domains of artificial intelligence, cognitive intelligence, and brain science. Among the essential areas of investigation in CLP, Cognitive Signal Decoding (CSD) has made remarkable achievements, yet there still exist challenges related to insufficient global dynamic representation capability and deficiencies in multi-domain feature integration. In this paper, we introduce a novel paradigm for CLP referred to as Disentangled Frequency-Spatial-Temporal Attention(D-FaST). Specifically, we present an novel cognitive signal decoder that operates on disentangled frequency-space-time domain attention. This decoder encompasses three key components: frequency domain feature extraction employing multi-view attention, spatial domain feature extraction utilizing dynamic brain connection graph attention, and temporal feature extraction relying on local time sliding window attention. These components are integrated within a novel disentangled framework. Additionally, to encourage advancements in this field, we have created a new CLP dataset, MNRED. Subsequently, we conducted an extensive series of experiments, evaluating D-FaST's performance on MNRED, as well as on publicly available datasets including ZuCo, BCIC IV-2A, and BCIC IV-2B. Our experimental results demonstrate that D-FaST outperforms existing methods significantly on both our datasets and traditional CSD datasets including establishing a state-of-the-art accuracy score 78.72% on MNRED, pushing the accuracy score on ZuCo to 78.35%, accuracy score on BCIC IV-2A to 74.85% and accuracy score on BCIC IV-2B to 76.81%.
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Decoding the Enigma: Benchmarking Humans and AIs on the Many Facets of Working Memory
Institute for Infocomm Research (I2R), A*STAR（资讯通信研究院（I2R），新加坡科技研究局）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
Anhui University（安徽大学）；Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education（教育部智能计算与信号处理重点实验室）；Anhui Province Key Laboratory of Intelligent Computing and Applications（安徽省智能计算与应用重点实验室）；Department of Information Materials and Intelligent Sensing Laboratory of Anhui Province（安徽省信息材料与智能感知实验室）
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)
Inria Bordeaux（法国国家信息与自动化研究所波尔多分所）；LaBRI, Université de Bordeaux（波尔多大学拉布里实验室）；Université de Bordeaux（波尔多大学）；TU Berlin（柏林工业大学）；National University of Singapore（新加坡国立大学）；Microsoft（微软公司）；International Institute of Information Technology Hyderabad（海得拉巴国际信息技术研究所）；CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Microsoft Dynamics 365 AI（微软Dynamics 365人工智能）；Microsoft Research（微软研究院）
预训练神经语言模型的最新进展显着提高了许多自然语言处理（NLP）任务的性能。在本文中，我们提出了一个新的模型架构DeBERTa（解码增强BERT与解开注意），提高了BERT和RoBERTa模型使用两种新的技术。第一种是解纠缠的注意力机制，其中每个词使用两个向量来表示，这两个向量分别编码其内容和位置，并且词之间的注意力权重分别使用解纠缠矩阵来计算它们的内容和相对位置。第二，增强的掩码解码器用于在解码层中合并绝对位置，以在模型预训练中预测掩码令牌。此外，一种新的虚拟对抗训练方法用于微调，以提高模型的泛化能力。我们表明，这些技术显着提高模型预训练的效率和性能的自然语言理解（NLU）和自然语言生成（NLG）下游任务。与RoBERTa-Large相比，在一半训练数据上训练的DeBERTa模型在广泛的NLP任务上表现更好，MNLI提高了+0.9%（90.2% vs. 91.1%），SQuAD v2.0提高了+2.3%（88.4% vs. 90.7%），RACE提高了+3.6%（83.2% vs. 86.8%）。值得注意的是，我们通过训练一个由48个变换层和15亿个参数组成的更大版本来扩展DeBERTa。显著的性能提升使得单个DeBERTa模型在SuperGLUE基准上超过人类性能（Wang等人，2019年a）首次在宏观平均得分方面（89.9对89.8），并且截至2021年1月6日，整体DeBE…
M$^3$Eval: Multi-Modal Memory Evaluation through Cognitively-Grounded Video Tasks
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）；Yuanpei College, Peking University（北京大学元培学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；School of Psychological and Cognitive Sciences, Peking University（北京大学心理学与认知科学学院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
Evaluating deep transfer learning for whole-brain cognitive decoding
Technische Universität Berlin（柏林工业大学）；Max Planck Institute for Human Development（马克斯·普朗克人类发展研究所）；Stanford University（斯坦福大学）；Max Planck UCL Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克伦敦大学学院计算精神病学与老龄化研究中心）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Ensemble-based graph representation of fMRI data for cognitive brain state classification
Institute for Cognitive Neuroscience, University Higher School of Economics（认知神经科学研究所，高等经济大学）；Department of Mathematics and Institute for Women’s Health, University College London（数学系和妇女健康研究所，伦敦大学学院）
