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Jayaraman J. Thiagarajan
Arizona State University
107Publications
10H-index
416Citations
Publications 107
Newest
#1Shusen LiuH-Index: 9
#2Rushil AnirudhH-Index: 5
Last.Peer-Timo Bremer (LLNL: Lawrence Livermore National Laboratory)H-Index: 27
view all 4 authors...
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often more interested in determining how one or multiple user-selected response function(s) can be explained by the data. To intuitively connect the responses to the data, FPP constructs 2...
With rapid adoption of deep learning in high-regret applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties. While identifying all sources that account for the stochasticity of learned models is challenging, it is common to augment predictions with confidence intervals to convey the expected variations in a model's behavior. In general, we require confidence intervals to be well-calibrated, reflect the true ...
#1Suhas Ranganath (ASU: Arizona State University)H-Index: 7
#2Jayaraman J. Thiagarajan (ASU: Arizona State University)H-Index: 10
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 8 authors...
In this paper, we present a unique Android-DSP (AJDSP) application which was built from the ground up to provide mobile laboratory and computational experiences for educational use. AJDSP provides a mobile intuitive environment for developing and running signal processing simulations in a user-friendly. It is based on a block diagram system approach to support signal generation, analysis, and processing. AJDSP is designed for use by undergraduate and graduate students and DSP instructors. Its ex...
#1Bhavya Kailkhura (LLNL: Lawrence Livermore National Laboratory)
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 10
Last.Peer-Timo Bremer (LLNL: Lawrence Livermore National Laboratory)H-Index: 27
view all 4 authors...
This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, expressed in terms of the power spectra of the sampling pattern and the function involved. The framework is build in the Euclidean space using Fourier analysis and establishes a connection between some high dimensional geometric objects and optimal ...
2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Rushil Anirudh (LLNL: Lawrence Livermore National Laboratory)H-Index: 5
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 10
2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Vivek Sivaraman Narayanaswamy (ASU: Arizona State University)
#2Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 10
Last.Andreas SpaniasH-Index: 26
view all 4 authors...
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 10
#2Rushil Anirudh (LLNL: Lawrence Livermore National Laboratory)H-Index: 5
Last.Peer-Timo Bremer (LLNL: Lawrence Livermore National Laboratory)H-Index: 27
view all 4 authors...
2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 10
#2Irene KimH-Index: 1
Last.Peer-Timo Bremer (LLNL: Lawrence Livermore National Laboratory)H-Index: 27
view all 4 authors...
#1Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 10
Last.Alexandros Karagyris (IBM)H-Index: 1
view all 3 authors...
Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation scenarios in computer vision, the problems that we consider are characterized by a small number of training images and non-local patterns that lead to the diagnosis. In this paper, we explore the use of multiple instance learning (MIL) to design an instance lab...
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