49 citations · 105 across the 14 of their papers we have counts for
15 papers
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Noga Mudrik, Gal Mishne, Adam S. Charles
Time series data across scientific domains are often collected under distinct states (e.g., tasks), wherein latent processes (e.g., biological factors) create complex inter- and in…
Multi-Lingual DALL-E Storytime
Noga Mudrik, Adam S. Charles
While recent advancements in artificial intelligence (AI) language models demonstrate cutting-edge performance when working with English texts, equivalent models do not exist in ot…
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Noga Mudrik, Yenho Chen, Eva Yezerets +2
Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and beha…
Data Processing of Functional Optical Microscopy for Neuroscience
Hadas Benisty, Alexander Song, Gal Mishne +1
Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spa…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
Visualizing the PHATE of Neural Networks
Scott Gigante, Adam S. Charles, Smita Krishnaswamy +1
Understanding why and how certain neural networks outperform others is key to guiding future development of network architectures and optimization methods. To this end, we introduc…
Learning spatially-correlated temporal dictionaries for calcium imaging
Gal Mishne, Adam S. Charles
Calcium imaging has become a fundamental neural imaging technique, aiming to recover the individual activity of hundreds of neurons in a cortical region. Current methods (mostly ma…
Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer
Nicholas P. Bertrand, Adam S. Charles, John Lee +2
Tracking algorithms such as the Kalman filter aim to improve inference performance by leveraging the temporal dynamics in streaming observations. However, the tracking regularizers…
Interpreting Deep Learning: The Machine Learning Rorschach Test?
Adam S. Charles
Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities. While deep neural networks (DNNs) originated a…
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
Adam Charles, Dong Yin, Christopher Rozell
Recurrent neural networks (RNNs) have drawn interest from machine learning researchers because of their effectiveness at preserving past inputs for time-varying data processing tas…
Statistical structure of concave compositions
Avinash J. Dalal, Amanda Lohss, Daniel Parry
In this paper, we study concave compositions, an extension of partitions that were considered by Andrews, Rhoades, and Zwegers. They presented several open problems regarding the s…
Dynamic Filtering of Time-Varying Sparse Signals via l1 Minimization
Adam Charles, Aurele Balavoine, Christopher Rozell
Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time…
Short Term Memory Capacity in Networks via the Restricted Isometry Property
Adam S. Charles, Han Lun Yap, Christopher J. Rozell
Cortical networks are hypothesized to rely on transient network activity to support short term memory (STM). In this paper we study the capacity of randomly connected recurrent lin…
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Adam S. Charles, Christopher J. Rozell
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploit…
Analog Sparse Approximation with Applications to Compressed Sensing
Adam S. Charles, Pierre Garrigues, Christopher J. Rozell
Recent research has shown that performance in signal processing tasks can often be significantly improved by using signal models based on sparse representations, where a signal is…