32 citations · 116 across the 12 of their papers we have counts for
15 papers
Large Language Models Based Automatic Synthesis of Software Specifications
Shantanu Mandal, Adhrik Chethan, Vahid Janfaza +5
Software configurations play a crucial role in determining the behavior of software systems. In order to ensure safe and error-free operation, it is necessary to identify the corre…
Synthesizing Programs with Continuous Optimization
Shantanu Mandal, Todd A. Anderson, Javier Turek +2
Automatic software generation based on some specification is known as program synthesis. Most existing approaches formulate program synthesis as a search problem with discrete para…
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
Richard Antonello, Javier Turek, Vy Vo +1
How related are the representations learned by neural language models, translation models, and language tagging tasks? We answer this question by adapting an encoder-decoder transf…
Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay
Hsiang-Yun Sherry Chien, Javier S. Turek, Nicole Beckage +3
Sequential information contains short- to long-range dependencies; however, learning long-timescale information has been a challenge for recurrent neural networks. Despite improvem…
Multi-timescale Representation Learning in LSTM Language Models
Shivangi Mahto, Vy A. Vo, Javier S. Turek +1
Language models must capture statistical dependencies between words at timescales ranging from very short to very long. Earlier work has demonstrated that dependencies in natural l…
Selecting Informative Contexts Improves Language Model Finetuning
Richard Antonello, Nicole Beckage, Javier Turek +1
Language model fine-tuning is essential for modern natural language processing, but is computationally expensive and time-consuming. Further, the effectiveness of fine-tuning is li…
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
Javier S. Turek, Shailee Jain, Vy Vo +3
Recent work has shown that topological enhancements to recurrent neural networks (RNNs) can increase their expressiveness and representational capacity. Two popular enhancements ar…
Learning Fitness Functions for Machine Programming
Shantanu Mandal, Todd A. Anderson, Javier S. Turek +3
The problem of automatic software generation is known as Machine Programming. In this work, we propose a framework based on genetic algorithms to solve this problem. Although genet…
Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging
Michael J. Anderson, Jonathan I. Tamir, Javier S. Turek +4
Magnetic resonance imaging is capable of producing volumetric images without ionizing radiation. Nonetheless, long acquisitions lead to prohibitively long exams. Compressed sensing…
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Mejbah Alam, Justin Gottschlich, Nesime Tatbul +3
The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of…
Efficient, sparse representation of manifold distance matrices for classical scaling
Javier S. Turek, Alexander Huth
Geodesic distance matrices can reveal shape properties that are largely invariant to non-rigid deformations, and thus are often used to analyze and represent 3-D shapes. However, t…
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
Hejia Zhang, Po-Hsuan Chen, Janice Chen +5
There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One appr…
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
Po-Hsuan Chen, Xia Zhu, Hejia Zhang +5
Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human c…
A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression
Eran Treister, Javier S. Turek, Irad Yavneh
Solving l1 regularized optimization problems is common in the fields of computational biology, signal processing and machine learning. Such l1 regularization is utilized to find sp…
On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary
Javier Turek, Irad Yavneh, Matan Protter +1
Among the many ways to model signals, a recent approach that draws considerable attention is sparse representation modeling. In this model, the signal is assumed to be generated as…