134 citations · 262 across the 11 of their papers we have counts for
12 papers
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
Jesse A. Livezey, Joshua I. Glaser
Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research as well as implications for systems neuroscienc…
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations
Jesse A. Livezey, Ahyeon Hwang, Jacob Yeung +1
Hierarchy and compositionality are common latent properties in many natural and scientific datasets. Determining when a deep network's hidden activations represent hierarchy and co…
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
David G. Clark, Jesse A. Livezey, Kristofer E. Bouchard
Linear dimensionality reduction methods are commonly used to extract low-dimensional structure from high-dimensional data. However, popular methods disregard temporal structure, re…
Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces
David G. Clark, Jesse A. Livezey, Edward F. Chang +1
Neuromorphic architectures achieve low-power operation by using many simple spiking neurons in lieu of traditional hardware. Here, we develop methods for precise linear computation…
Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex
Jesse A. Livezey, Kristofer E. Bouchard, Edward F. Chang
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial…
Learning overcomplete, low coherence dictionaries with linear inference
Jesse A. Livezey, Alejandro F. Bujan, Friedrich T. Sommer
Finding overcomplete latent representations of data has applications in data analysis, signal processing, machine learning, theoretical neuroscience and many other fields. In an ov…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
Discovering Hidden Factors of Variation in Deep Networks
Brian Cheung, Jesse A. Livezey, Arjun K. Bansal +1
Deep learning has enjoyed a great deal of success because of its ability to learn useful features for tasks such as classification. But there has been less exploration in learning…
Measurements of Electron Cloud Growth and Mitigation in Dipole, Quadrupole, and Wiggler Magnets
J. R. Calvey, W. Hartung, Y. Li +4
Retarding field analyzers (RFAs), which provide a localized measurement of the electron cloud, have been installed throughout the Cornell Electron Storage Ring (CESR), in different…
Attending to experimental physics practices and lifelong learning skills in an introductory laboratory course
Punit R. Gandhi, Jesse A. Livezey, Anna M. Zaniewski +2
We have designed an introductory laboratory course that engaged first-year undergraduate students in two complementary types of iteration: (1) iterative improvement of experiments…
Measurement and Modeling of Electron Cloud in a Field Free Environment Using Retarding Field Analyzers
J. R. Calvey, G. Dugan, W. Hartung +3
As part of the CESR-TA program at Cornell, diagnostic devices to measure and quantify the electron cloud effect have been installed throughout the CESR ring. One such device is the…
Comparison of Electron Cloud Mitigating Coatings Using Retarding Field Analyzers
J. R. Calvey, W. Hartung, Y. Li +4
In 2008, the Cornell Electron Storage Ring (CESR) was reconfigured to serve as a test accelerator (CESR-TA) for next generation lepton colliders, in particular for the ILC damping…