Search Results for author:
Found 13 papers, 2 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.
Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay
Altogether, we found that LSTM with the proposed forget gate can learn long-term dependencies, outperforming other recurrent networks in multiple domains; such gating mechanism can be integrated into other architectures for improving the learning of long timescale information in recurrent neural networks.
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
To understand how the human brain represents this information, one approach is to build encoding models that predict fMRI responses to natural language using representations extracted from neural network language models (LMs).
Multi-timescale Representation Learning in LSTM Language Models
Earlier work has demonstrated that dependencies in natural language tend to decay with distance between words according to a power law.
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
In this work, we explore the delayed-RNN, which is a single-layer RNN that has a delay between the input and output.
Learning Fitness Functions for Machine Programming
The problem of automatic software generation is known as Machine Programming.
Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging
Our improvements to the pipeline on a single machine provide a 3x overall reconstruction speedup, which allowed us to add algorithmic changes improving image quality.
Efficient, sparse representation of manifold distance matrices for classical scaling
Thus for large point sets it is common to use a low-rank approximation to the distance matrix, which fits in memory and can be efficiently analyzed using methods such as multidimensional scaling (MDS).
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
In this work, we examine a searchlight based shared response model to identify shared information in small contiguous regions (searchlights) across the whole brain.
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality.
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.
A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression
A multilevel framework is presented for solving such l1 regularized sparse optimization problems efficiently.
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation
Numerical experiments on both synthetic and real gene expression data demonstrate that our approach outperforms the existing state of the art methods, especially for large-scale problems.
