数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Analyzing Memory Effects in Large Language Models through the lens of Cognitive PsychologyMemory, a fundamental component of human cognition, exhibits adaptive yet fallible characteristics as illustrated by Schacter’s memory “sins”.These cognitive phenomena have been studied extensively in psychology and neuroscience, but the extent to which artificial systems, specifically Large Language Models (LLMs), emulate these cognitive phenomena remains underexplor ...

数据来源：ArXiv Domain LLM Domain Papers1. On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responsesThis paper gives an overview of a theory for modelling the interaction between geometric image transformations and receptive field responses for a visual observer that views objects and spatio-temporal events in the environment. This treatment is developed over combinations of ...

数据来源：ArXiv Domain LLM Domain Papers1. On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responsesThis paper gives an overview of a theory for modelling the interaction between geometric image transformations and receptive field responses for a visual observer that views objects and spatio-temporal events in the environment. This treatment is developed over combinations of ...

数据来源：ArXiv Domain LLM Domain Papers1. On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responsesThis paper gives an overview of a theory for modelling the interaction between geometric image transformations and receptive field responses for a visual observer that views objects and spatio-temporal events in the environment. This treatment is developed over combinations of ...

数据来源：ArXiv Domain LLM Domain Papers1. REVE: A Foundation Model for EEG — Adapting to Any Setup with Large-Scale Pretraining on 25,000 SubjectsFoundation models have transformed AI by reducing reliance on task-specific data through large-scale pretraining. While successful in language and vision, their adoption in EEG has lagged due to the heterogeneity of public datasets, which are collected under varying protocols, devices, and electrode configurations. Existing EEG foundation models struggle ...

数据来源：ArXiv Domain LLM Domain Papers1. Transformer brain encoders explain human high-level visual responsesA major goal of neuroscience is to understand brain computations during visual processing in naturalistic settings. A dominant approach is to use image-computable deep neural networks trained with different task objectives as a basis for linear encoding models. However, in addition to requiring estimation of a large number of linear encoding parameters, this approach ignores the structure ...

数据来源：ArXiv Domain LLM Domain Papers1. Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?Object binding, the brain’s ability to bind the many features that collectively represent an object into a coherent whole, is central to human cognition. It groups low-level perceptual features into high-level object representations, stores those objects efficiently and compositionally in memory, and supports human reasoning about individual object instances. While prior work ofte ...

数据来源：ArXiv Domain LLM Domain Papers1. Revisiting Generalization Across Difficulty Levels: It’s Not So EasyWe investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is mixed regarding whether training on easier or harder data leads to better results, and whether those gains come on easier or harder test data. We address this question by conducting a systematic evaluation of LLM ...

数据来源：ArXiv Domain LLM Domain Papers1. Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?Object binding, the brain’s ability to bind the many features that collectively represent an object into a coherent whole, is central to human cognition. It groups low-level perceptual features into high-level object representations, stores those objects efficiently and compositionally in memory, and supports human reasoning about individual object instances. While prior work ofte ...

数据来源：ArXiv Domain LLM Domain Papers1. Thinking by Doing: Building Efficient World Model Reasoning in LLMs via Multi-turn InteractionDeveloping robust world model reasoning is crucial for large language model (LLM) agents to plan and interact in complex environments. While multi-turn interaction offers a superior understanding of environmental dynamics via authentic feedback, current approaches often impose a rigid reasoning process, which constrains the model’s active learning, ultimately hind ...

数据来源：ArXiv Domain LLM Domain Papers1. EfficientFlow: Efficient Equivariant Flow Policy Learning for Embodied AIGenerative modeling has recently shown remarkable promise for visuomotor policy learning, enabling flexible and expressive control across diverse embodied AI tasks. However, existing generative policies often struggle with data inefficiency, requiring large-scale demonstrations, and sampling inefficiency, incurring slow action generation during inference. We introduce EfficientFlow, a ...