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Found 9 papers, 5 papers with code
Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks
To address the gap in evaluating memory in LLMs, we introduce Sequence Order Recall Tasks (SORT), which we adapt from tasks used to study episodic memory in cognitive psychology.
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited.
Reconciling Shared versus Context-Specific Information in a Neural Network Model of Latent Causes
Through learning, it naturally stores structure that is shared across tasks in the network weights.
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
This allows us to accurately retrieve images even from large-scale databases like LAION-5B.
Large language models can segment narrative events similarly to humans
In the future, GPT-3 may thereby help to elucidate the principles underlying human event perception.
Learning to Perform Role-Filler Binding with Schematic Knowledge
We can perform these bindings for arbitrary fillers -- we understand this sentence even if we have never heard the names "Alice," "tea," or "Bob" before.
Shared Representational Geometry Across Neural Networks
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights.
Leabra7: a Python package for modeling recurrent, biologically-realistic neural networks
Emergent is a software package that uses the AdEx neural dynamics model and LEABRA learning algorithm to simulate and train arbitrary recurrent neural network architectures in a biologically-realistic manner.
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets
The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted.
