5 papers
NeuroView-RNN: It's About Time
CJ Barberan, Sina Alemohammad, Naiming Liu +2
Recurrent Neural Networks (RNNs) are important tools for processing sequential data such as time-series or video. Interpretability is defined as the ability to be understood by a p…
NFT-K: Non-Fungible Tangent Kernels
Sina Alemohammad, Hossein Babaei, CJ Barberan +4
Deep neural networks have become essential for numerous applications due to their strong empirical performance such as vision, RL, and classification. Unfortunately, these networks…
Enhanced Recurrent Neural Tangent Kernels for Non-Time-Series Data
Sina Alemohammad, Randall Balestriero, Zichao Wang +1
Kernels derived from deep neural networks (DNNs) in the infinite-width regime provide not only high performance in a range of machine learning tasks but also new theoretical insigh…
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels
Sina Alemohammad, Hossein Babaei, Randall Balestriero +8
High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation. Techniques abound to red…
Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method
Ashkan Esmaeili, Kayhan Behdin, Sina Al-E-Mohammad +1
In this paper, we propose a novel approach in order to recover a quantized matrix with missing information. We propose a regularized convex cost function composed of a log-likeliho…