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Found 10 papers, 2 papers with code
DMON: A Simple yet Effective Approach for Argument Structure Learning
Argument structure learning~(ASL) entails predicting relations between arguments.
MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities
In contrast, we propose a simple yet effective method that guides text reconstruction by directly comparing them with the predicted text embeddings mapped from brain activities.
Computational Models to Study Language Processing in the Human Brain: A Survey
Despite differing from the human language processing mechanism in implementation and algorithms, current language models demonstrate remarkable human-like or surpassing language capabilities.
NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties
In the pursuit to understand the intricacies of human brain's visual processing, reconstructing dynamic visual experiences from brain activities emerges as a challenging yet fascinating endeavor.
Tuning In to Neural Encoding: Linking Human Brain and Artificial Supervised Representations of Language
To understand the algorithm that supports the human brain's language representation, previous research has attempted to predict neural responses to linguistic stimuli using embeddings generated by artificial neural networks (ANNs), a process known as neural encoding.
Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?
If so, what kind of NLU task leads a pre-trained model to better decode the information represented in the human brain?
Decoding Realistic Images from Brain Activity with Contrastive Self-supervision and Latent Diffusion
Reconstructing visual stimuli from human brain activities provides a promising opportunity to advance our understanding of the brain's visual system and its connection with computer vision models.
Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities
The second phase tunes the feature learner to attend to neural activation patterns most informative for visual reconstruction with guidance from an image auto-encoder.
Ranked #1 on
Brain Visual Reconstruction from fMRI
on GOD
Distill and Replay for Continual Language Learning
The framework is based on language models and can be smoothly built with different language model architectures.
Memory, Show the Way: Memory Based Few Shot Word Representation Learning
Distributional semantic models (DSMs) generally require sufficient examples for a word to learn a high quality representation.
