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ORCIDSmita Krishnaswamy
Person information
- affiliation: Yale University, New Haven, CT, USA
- affiliation (PhD 2008): University of Michigan, USA
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2020 – today
- 2024
[j12]Aarthi Venkat, Samuel Leone, Scott E. Youlten, Eric Fagerberg
, John Attanasio, Nikhil S. Joshi, Michael Perlmutter, Smita Krishnaswamy:
Mapping the gene space at single-cell resolution with gene signal pattern analysis. Nat. Comput. Sci. 4(12): 955-977 (2024)- [j11]Ho-Joon Lee, Lee H. Schwamm, Lauren H. Sansing, Hooman Kamel, Adam DeHavenon, Ashby C. Turner, Kevin N. Sheth, Smita Krishnaswamy, Cynthia Brandt, Hongyu Zhao, Harlan M. Krumholz, Richa Sharma:
StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. npj Digit. Medicine 7(1) (2024) - [j10]Alexander Tong, Frederik Wenkel, Dhananjay Bhaskar, Kincaid MacDonald, Jackson D. Grady, Michael Perlmutter, Smita Krishnaswamy, Guy Wolf:
Learnable Filters for Geometric Scattering Modules. IEEE Trans. Signal Process. 72: 2939-2952 (2024) - [c53]Charles Xu, Laney Goldman, Valentina Guo, Benjamin Hollander-Bodie, Maedee Trank-Greene, Ian Adelstein, Edward De Brouwer, Rex Ying, Smita Krishnaswamy, Michael Perlmutter:
BLIS-Net: Classifying and Analyzing Signals on Graphs. AISTATS 2024: 4537-4545 - [c52]Samuel Leone, Xingzhi Sun, Michael Perlmutter, Smita Krishnaswamy:
Bayesian Spectral Graph Denoising with Smoothness Prior. CISS 2024: 1-6 - [c51]Danqi Liao, Chen Liu, Benjamin W. Christensen, Alexander Tong, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy:
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy. CISS 2024: 1-6 - [c50]Aarthi Venkat, Joyce A. Chew, Ferran Cardoso Rodriguez, Christopher J. Tape, Michael Perlmutter, Smita Krishnaswamy:
Directed Scattering for Knowledge Graph-Based Cellular Signaling Analysis. ICASSP 2024: 9761-9765 - [c49]Chen Liu, Matthew Amodio, Liangbo L. Shen, Feng Gao, Arman Avesta, Sanjay Aneja, Jay C. Wang, Lucian V. Del Priore, Smita Krishnaswamy:
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation. MICCAI (8) 2024: 155-165 - [c48]Holly R. Steach, Siddharth Viswanath, Yixuan He, Xitong Zhang, Natalia Ivanova, Matthew J. Hirn, Michael Perlmutter, Smita Krishnaswamy:
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning. RECOMB 2024: 235-252 - [i55]Chen Liu, Ke Xu, Liangbo L. Shen, Guillaume Huguet, Zilong Wang, Alexander Tong, Danilo Bzdok, Jay Stewart, Jay C. Wang, Lucian V. Del Priore, Smita Krishnaswamy:
ImageFlowNet: Forecasting Multiscale Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images. CoRR abs/2406.14794 (2024) - [i54]Xingzhi Sun, Charles Xu, João F. Rocha, Chen Liu, Benjamin Hollander-Bodie, Laney Goldman, Marcello DiStasio, Michael Perlmutter, Smita Krishnaswamy:
Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics. CoRR abs/2409.09469 (2024) - [i53]Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch, Laurent Caplette, Rahul Singh, Guillaume Lajoie, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Latent Representation Learning for Multimodal Brain Activity Translation. CoRR abs/2409.18462 (2024) - [i52]Arman Afrasiyabi, Erica L. Busch, Rahul Singh, Dhananjay Bhaskar, Laurent Caplette, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Looking through the mind's eye via multimodal encoder-decoder networks. CoRR abs/2410.00047 (2024) - [i51]Chen Liu, Danqi Liao, Alejandro Parada-Mayorga, Alejandro Ribeiro, Marcello DiStasio, Smita Krishnaswamy:
DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images. CoRR abs/2410.03058 (2024) - [i50]Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Chen Liu, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita Krishnaswamy:
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. CoRR abs/2410.12779 (2024) - [i49]David R. Johnson, Joyce A. Chew, Siddharth Viswanath, Edward De Brouwer, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter:
Convergence of Manifold Filter-Combine Networks. CoRR abs/2410.14639 (2024) - [i48]Siddharth Viswanath, Dhananjay Bhaskar, David R. Johnson, João F. Rocha, Egbert Castro, Jackson D. Grady, Alex T. Grigas, Michael A. Perlmutter, Corey S. O'Hern, Smita Krishnaswamy:
ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics. CoRR abs/2410.20317 (2024) - [i47]Elliott Abel, Peyton Crevasse, Yvan Grinspan, Selma Mazioud, Folu Ogundipe, Kristof Reimann, Ellie Schueler, Andrew J. Steindl, Ellen Zhang, Dhananjay Bhaskar, Siddharth Viswanath, Yanlei Zhang, Tim G. J. Rudner, Ian Adelstein, Smita Krishnaswamy:
Exploring the Manifold of Neural Networks Using Diffusion Geometry. CoRR abs/2411.12626 (2024) - [i46]Dmitry Kobak, Fred A. Hamprecht, Smita Krishnaswamy, Gal Mishne, Sebastian Damrich:
Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122). Dagstuhl Reports 14(3): 92-115 (2024) - 2023
- [j9]Erica L. Busch, Jessie Huang, Andrew Benz, Tom Wallenstein, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy, Nicholas B. Turk-Browne:
Multi-view manifold learning of human brain-state trajectories. Nat. Comput. Sci. 3(3): 240-253 (2023) - [j8]Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew J. Hirn, Guy Wolf, Smita Krishnaswamy:
Time-Inhomogeneous Diffusion Geometry and Topology. SIAM J. Math. Data Sci. 5(2): 346-372 (2023) - [c47]Tesfa Asmara, Dhananjay Bhaskar, Ian Adelstein, Smita Krishnaswamy, Michael Perlmutter:
Wire Before You Walk. ACSSC 2023: 714-716 - [c46]Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy:
Neural FIM for learning Fisher information metrics from point cloud data. ICML 2023: 9814-9826 - [c45]Dhananjay Bhaskar, Daniel Sumner Magruder, Matheo Morales, Edward De Brouwer, Aarthi Venkat, Frederik Wenkel, Guy Wolf, Smita Krishnaswamy:
Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations. LoG 2023: 22 - [c44]Guillaume Huguet, Alexander Tong, María Ramos Zapatero, Christopher J. Tape, Guy Wolf, Smita Krishnaswamy:
Geodesic Sinkhorn For Fast and Accurate Optimal Transport on Manifolds. MLSP 2023: 1-6 - [c43]Kincaid MacDonald, Dhananjay Bhaskar, Guy Thampakkul, Nhi Nguyen, Joia Zhang, Michael Perlmutter, Ian Adelstein, Smita Krishnaswamy:
A Flow Artist for High-Dimensional Cellular Data. MLSP 2023: 1-6 - [c42]Guillaume Huguet, Alexander Tong, Edward De Brouwer, Yanlei Zhang, Guy Wolf, Ian Adelstein, Smita Krishnaswamy:
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction. NeurIPS 2023 - [i45]Guillaume Huguet, Alexander Tong, Edward De Brouwer, Yanlei Zhang, Guy Wolf, Ian Adelstein, Smita Krishnaswamy:
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction. CoRR abs/2305.19043 (2023) - [i44]Samuel Leone, Aarthi Venkat, Guillaume Huguet, Alexander Tong, Guy Wolf, Smita Krishnaswamy:
Graph Fourier MMD for Signals on Graphs. CoRR abs/2306.02508 (2023) - [i43]Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy:
Neural FIM for learning Fisher Information Metrics from point cloud data. CoRR abs/2306.06062 (2023) - [i42]Dhananjay Bhaskar, Daniel Sumner Magruder, Edward De Brouwer, Aarthi Venkat, Frederik Wenkel, Guy Wolf, Smita Krishnaswamy:
Inferring dynamic regulatory interaction graphs from time series data with perturbations. CoRR abs/2306.07803 (2023) - [i41]Joyce A. Chew, Edward De Brouwer, Smita Krishnaswamy, Deanna Needell, Michael Perlmutter:
Manifold Filter-Combine Networks. CoRR abs/2307.04056 (2023) - [i40]Kincaid MacDonald, Dhananjay Bhaskar, Guy Thampakkul, Nhi Nguyen, Joia Zhang, Michael Perlmutter, Ian Adelstein, Smita Krishnaswamy:
A Flow Artist for High-Dimensional Cellular Data. CoRR abs/2308.00176 (2023) - [i39]Aarthi Venkat, Joyce A. Chew, Ferran Cardoso Rodriguez, Christopher J. Tape, Michael Perlmutter, Smita Krishnaswamy:
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis. CoRR abs/2309.07813 (2023) - [i38]Dhananjay Bhaskar, Yanlei Zhang, Charles Xu, Xingzhi Sun, Oluwadamilola Fasina, Guy Wolf, Maximilian Nickel, Michael Perlmutter, Smita Krishnaswamy:
Graph topological property recovery with heat and wave dynamics-based features on graphs. CoRR abs/2309.09924 (2023) - [i37]Charles Xu, Laney Goldman, Valentina Guo, Benjamin Hollander-Bodie, Maedee Trank-Greene, Ian Adelstein, Edward De Brouwer, Rex Ying, Smita Krishnaswamy, Michael Perlmutter:
BLIS-Net: Classifying and Analyzing Signals on Graphs. CoRR abs/2310.17579 (2023) - [i36]Samuel Leone, Xingzhi Sun, Michael Perlmutter, Smita Krishnaswamy:
Bayesian Formulations for Graph Spectral Denoising. CoRR abs/2311.16378 (2023) - [i35]Danqi Liao, Chen Liu, Benjamin W. Christensen, Alexander Tong, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy:
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy. CoRR abs/2312.04823 (2023) - 2022
- [j7]Egbert Castro, Abhinav Godavarthi, Julian A. Rubinfien, Kevin B. Givechian, Dhananjay Bhaskar, Smita Krishnaswamy:
Transformer-based protein generation with regularized latent space optimization. Nat. Mac. Intell. 4(10): 840-851 (2022) - [j6]Matthew Amodio, Scott E. Youlten, Aarthi Venkat, Beatriz P. San Juan, Christine L. Chaffer, Smita Krishnaswamy:
Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer. Patterns 3(9): 100577 (2022) - [j5]Alexander Tong, Guy Wolf, Smita Krishnaswamy:
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators. J. Signal Process. Syst. 94(2): 229-243 (2022) - [c41]Alexander Tong, Guillaume Huguet, Dennis L. Shung, Amine Natik, Manik Kuchroo, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy:
Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance. ICASSP 2022: 5647-5651 - [c40]Stefan Horoi, Jessie Huang, Bastian Rieck, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy:
Exploring the Geometry and Topology of Neural Network Loss Landscapes. IDA 2022: 171-184 - [c39]Dhananjay Bhaskar, Jackson D. Grady, Egbert Castro, Michael Perlmutter, Smita Krishnaswamy:
Molecular Graph Generation via Geometric Scattering. MLSP 2022: 1-6 - [c38]Jessie Huang, Erica L. Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Learning Shared Neural Manifolds from Multi-Subject FMRI Data. MLSP 2022: 1-6 - [c37]Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina, Dawson Thomas, Bastian Rieck, Ian Adelstein, Smita Krishnaswamy:
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data. NeurIPS 2022 - [c36]Guillaume Huguet, Daniel Sumner Magruder, Alexander Tong, Oluwadamilola Fasina, Manik Kuchroo, Guy Wolf, Smita Krishnaswamy:
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference. NeurIPS 2022 - [c35]Joyce A. Chew, Holly R. Steach, Siddharth Viswanath, Hau-Tieng Wu, Matthew J. Hirn, Deanna Needell, Matthew D. Vesely, Smita Krishnaswamy, Michael Perlmutter:
The Manifold Scattering Transform for High-Dimensional Point Cloud Data. TAG-ML 2022: 67-78 - [i34]Jessie Huang, Erica L. Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Learning shared neural manifolds from multi-subject FMRI data. CoRR abs/2201.00622 (2022) - [i33]Egbert Castro, Abhinav Godavarthi, Julian A. Rubinfien, Kevin B. Givechian, Dhananjay Bhaskar, Smita Krishnaswamy:
Guided Generative Protein Design using Regularized Transformers. CoRR abs/2201.09948 (2022) - [i32]Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew J. Hirn, Guy Wolf, Smita Krishnaswamy:
Time-inhomogeneous diffusion geometry and topology. CoRR abs/2203.14860 (2022) - [i31]Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina, Dawson Thomas, Bastian Rieck, Ian Adelstein, Smita Krishnaswamy:
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data. CoRR abs/2206.03977 (2022) - [i30]Joyce A. Chew, Holly R. Steach, Siddharth Viswanath, Hau-Tieng Wu, Matthew J. Hirn, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter:
The Manifold Scattering Transform for High-Dimensional Point Cloud Data. CoRR abs/2206.10078 (2022) - [i29]Guillaume Huguet, Daniel Sumner Magruder, Oluwadamilola Fasina, Alexander Tong, Manik Kuchroo, Guy Wolf, Smita Krishnaswamy:
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference. CoRR abs/2206.14928 (2022) - [i28]Alexander Tong, Frederik Wenkel, Dhananjay Bhaskar, Kincaid MacDonald, Jackson D. Grady, Michael Perlmutter, Smita Krishnaswamy, Guy Wolf:
Learnable Filters for Geometric Scattering Modules. CoRR abs/2208.07458 (2022) - [i27]Joyce A. Chew, Matthew J. Hirn, Smita Krishnaswamy, Deanna Needell, Michael Perlmutter, Holly R. Steach, Siddharth Viswanath, Hau-Tieng Wu:
Geometric Scattering on Measure Spaces. CoRR abs/2208.08561 (2022) - [i26]Matthew Amodio, Feng Gao, Arman Avesta, Sanjay Aneja, Lucian V. Del Priore, Jay Wang, Smita Krishnaswamy:
CUTS: A Fully Unsupervised Framework for Medical Image Segmentation. CoRR abs/2209.11359 (2022) - [i25]Guillaume Huguet, Alexander Tong, María Ramos Zapatero, Guy Wolf, Smita Krishnaswamy:
Geodesic Sinkhorn: optimal transport for high-dimensional datasets. CoRR abs/2211.00805 (2022) - 2021
- [j4]Matthew Amodio, Dennis L. Shung, Daniel B. Burkhardt, Patrick Wong, Michael Simonov, Yu Yamamoto, David van Dijk, Francis Perry Wilson, Akiko Iwasaki, Smita Krishnaswamy:
Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference. Patterns 2(7): 100288 (2021) - [c34]Michal Gerasimiuk, Dennis L. Shung, Alexander Tong, Adrian J. Stanley, Michael Schultz, Jeffrey Ngu, Loren Laine, Guy Wolf, Smita Krishnaswamy:
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data. IEEE BigData 2021: 4694-4704 - [c33]Alexander Tong, Guillaume Huguet, Amine Natik, Kincaid MacDonald, Manik Kuchroo, Ronald R. Coifman, Guy Wolf, Smita Krishnaswamy:
Diffusion Earth Mover's Distance and Distribution Embeddings. ICML 2021: 10336-10346 - [c32]Matthew Amodio, Smita Krishnaswamy:
Multiple-manifold Generation with an Ensemble GAN and Learned Noise Prior. IDA 2021: 24-36 - [c31]Matthew Amodio, Smita Krishnaswamy:
Noise Space Optimization for GANs. IJCNN 2021: 1-8 - [c30]Matthew Amodio, Smita Krishnaswamy:
Canvas GAN: Bootstrapped Image-Conditional Models. IJCNN 2021: 1-8 - [c29]Manik Kuchroo, Abhinav Godavarthi, Alexander Tong, Guy Wolf, Smita Krishnaswamy:
Multimodal Data Visualization and Denoising with Integrated Diffusion. MLSP 2021: 1-6 - [c28]Alexander Tong, Frederick Wenkel, Kincaid MacDonald, Smita Krishnaswamy, Guy Wolf:
Data-Driven Learning of Geometric Scattering Modules for GNNs. MLSP 2021: 1-6 - [c27]Christopher Lance, Malte D. Lücken, Daniel B. Burkhardt, Robrecht Cannoodt, Pia Rautenstrauch, Anna Laddach, Aidyn Ubingazhibov, Zhi-Jie Cao, Kaiwen Deng, Sumeer Khan, Qiao Liu, Nikolay Russkikh, Gleb Ryazantsev, Uwe Ohler, Angela Oliveira Pisco, Jonathan Bloom, Smita Krishnaswamy, Fabian J. Theis:
Multimodal single cell data integration challenge: Results and lessons learned. NeurIPS (Competition and Demos) 2021: 162-176 - [c26]Malte Lücken, Daniel Burkhardt, Robrecht Cannoodt, Christopher Lance, Aditi Agrawal, Hananeh Aliee, Ann Chen, Louise Deconinck, Angela Detweiler, Alejandro Granados, Shelly Huynh, Laura Isacco, Yang Kim, Dominik Klein, Bony de Kumar, Sunil Kuppasani, Heiko Lickert, Aaron McGeever, Joaquin Melgarejo, Honey Mekonen, Maurizio Morri, Michaela Müller, Norma Neff, Sheryl Paul, Bastian Rieck, Kaylie Schneider, Scott Steelman, Michael Sterr, Daniel Treacy, Alexander Tong, Alexandra-Chloé Villani, Guilin Wang, Jia Yan, Ce Zhang, Angela Pisco, Smita Krishnaswamy, Fabian J. Theis, Jonathan M. Bloom:
A sandbox for prediction and integration of DNA, RNA, and proteins in single cells. NeurIPS Datasets and Benchmarks 2021 - [i24]Stefan Horoi, Jessie Huang, Guy Wolf, Smita Krishnaswamy:
Visualizing High-Dimensional Trajectories on the Loss-Landscape of ANNs. CoRR abs/2102.00485 (2021) - [i23]Manik Kuchroo, Abhinav Godavarthi, Guy Wolf, Smita Krishnaswamy:
Multimodal data visualization, denoising and clustering with integrated diffusion. CoRR abs/2102.06757 (2021) - [i22]Alexander Tong, Guillaume Huguet, Amine Natik, Kincaid MacDonald, Manik Kuchroo, Ronald R. Coifman, Guy Wolf, Smita Krishnaswamy:
Diffusion Earth Mover's Distance and Distribution Embeddings. CoRR abs/2102.12833 (2021) - [i21]Alexander Tong, Guillaume Huguet, Dennis L. Shung, Amine Natik, Manik Kuchroo, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy:
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance. CoRR abs/2107.12334 (2021) - [i20]Dhananjay Bhaskar, Jackson D. Grady, Michael A. Perlmutter, Smita Krishnaswamy:
Molecular Graph Generation via Geometric Scattering. CoRR abs/2110.06241 (2021) - [i19]Michal Gerasimiuk, Dennis L. Shung, Alexander Tong, Adrian J. Stanley, Michael Schultz, Jeffrey Ngu, Loren Laine, Guy Wolf, Smita Krishnaswamy:
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data. CoRR abs/2111.10452 (2021) - 2020
- [c25]Egbert Castro, Andrew Benz, Alexander Tong, Guy Wolf, Smita Krishnaswamy:
Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings. IEEE BigData 2020: 4519-4528 - [c24]Alexander Tong, Jessie Huang, Guy Wolf, David van Dijk, Smita Krishnaswamy:
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. ICML 2020: 9526-9536 - [c23]Alexander Tong, David van Dijk, Jay S. Stanley III, Matthew Amodio, Kristina Yim, Rebecca Muhle, James Noonan, Guy Wolf, Smita Krishnaswamy:
Interpretable Neuron Structuring with Graph Spectral Regularization. IDA 2020: 509-521 - [c22]Alexander Tong, Guy Wolf, Smita Krishnaswamy:
Fixing Bias in Reconstruction-Based Anomaly Detection with Lipschitz Discriminators. MLSP 2020: 1-6 - [c21]Matthew Amodio, David van Dijk, Guy Wolf, Smita Krishnaswamy:
Learning General Transformations of Data for Out-of-Sample Extensions. MLSP 2020: 1-6 - [c20]Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. NeurIPS 2020 - [c19]Jay S. Stanley III, Scott Gigante, Guy Wolf, Smita Krishnaswamy:
Harmonic Alignment. SDM 2020: 316-324 - [i18]Tobias Brudermueller, Dennis L. Shung, Loren Laine, Adrian J. Stanley, Stig B. Laursen, Harry R. Dalton, Jeffrey Ngu, Michael Schultz, Johannes Stegmaier, Smita Krishnaswamy:
Making Logic Learnable With Neural Networks. CoRR abs/2002.03847 (2020) - [i17]Alexander Tong, Jessie Huang, Guy Wolf, David van Dijk, Smita Krishnaswamy:
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. CoRR abs/2002.04461 (2020) - [i16]Egbert Castro, Andrew Benz, Alexander Tong, Guy Wolf, Smita Krishnaswamy:
Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings. CoRR abs/2006.06885 (2020) - [i15]Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. CoRR abs/2006.07882 (2020) - [i14]Matthew Amodio, Rim Assouel, Victor Schmidt, Tristan Sylvain, Smita Krishnaswamy, Yoshua Bengio:
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer. CoRR abs/2006.13291 (2020) - [i13]Alexander Tong, Frederik Wenkel, Kincaid MacDonald, Smita Krishnaswamy, Guy Wolf:
Data-Driven Learning of Geometric Scattering Networks. CoRR abs/2010.02415 (2020)
2010 – 2019
- 2019
- [c18]Nathan Brugnone, Smita Krishnaswamy, Alex Gonopolskiy, Mark W. Moyle, Manik Kuchroo, David van Dijk, Kevin R. Moon, Daniel Colón-Ramos, Guy Wolf, Matthew J. Hirn:
Coarse Graining of Data via Inhomogeneous Diffusion Condensation. IEEE BigData 2019: 2624-2633 - [c17]David van Dijk, Daniel B. Burkhardt, Matthew Amodio, Alexander Tong, Guy Wolf, Smita Krishnaswamy:
Finding Archetypal Spaces Using Neural Networks. IEEE BigData 2019: 2634-2643 - [c16]Matthew Amodio, Smita Krishnaswamy:
TraVeLGAN: Image-To-Image Translation by Transformation Vector Learning. CVPR 2019: 8983-8992 - [c15]Daniel B. Burkhardt, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy:
Vertex-Frequency Clustering. DSW 2019: 145-149 - [c14]Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne:
Visualizing the PHATE of Neural Networks. NeurIPS 2019: 1840-1851 - [i12]Matthew Amodio, Smita Krishnaswamy:
Generating and Aligning from Data Geometries with Generative Adversarial Networks. CoRR abs/1901.08177 (2019) - [i11]David van Dijk, Daniel B. Burkhardt, Matthew Amodio, Alexander Tong, Guy Wolf, Smita Krishnaswamy:
Finding Archetypal Spaces for Data Using Neural Networks. CoRR abs/1901.09078 (2019) - [i10]Scott Gigante, Jay S. Stanley III, Ngan Vu, David van Dijk, Kevin R. Moon, Guy Wolf, Smita Krishnaswamy:
Compressed Diffusion. CoRR abs/1902.00033 (2019) - [i9]Matthew Amodio, Smita Krishnaswamy:
TraVeLGAN: Image-to-image Translation by Transformation Vector Learning. CoRR abs/1902.09631 (2019) - [i8]Alexander Tong, Guy Wolf, Smita Krishnaswamy:
A Lipschitz-constrained anomaly discriminator framework. CoRR abs/1905.10710 (2019) - [i7]Nathan Brugnone, Alex Gonopolskiy, Mark W. Moyle, Manik Kuchroo, David van Dijk, Kevin R. Moon, Daniel Colón-Ramos, Guy Wolf, Matthew J. Hirn, Smita Krishnaswamy:
Coarse Graining of Data via Inhomogeneous Diffusion Condensation. CoRR abs/1907.04463 (2019) - [i6]Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne:
Visualizing the PHATE of Neural Networks. CoRR abs/1908.02831 (2019) - 2018
- [c13]Matthew Amodio, Smita Krishnaswamy:
MAGAN: Aligning Biological Manifolds. ICML 2018: 215-223 - [c12]Ofir Lindenbaum, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy:
Geometry Based Data Generation. NeurIPS 2018: 1407-1418 - [i5]David van Dijk, Scott Gigante, Alexander Strzalkowski, Guy Wolf, Smita Krishnaswamy:
Modeling Dynamics with Deep Transition-Learning Networks. CoRR abs/1802.03497 (2018) - [i4]Ofir Lindenbaum, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy:
Geometry-Based Data Generation. CoRR abs/1802.04927 (2018) - [i3]Matthew Amodio, Smita Krishnaswamy:
MAGAN: Aligning Biological Manifolds. CoRR abs/1803.00385 (2018) - [i2]Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy:
Manifold Alignment with Feature Correspondence. CoRR abs/1810.00386 (2018) - [i1]Alexander Tong, David van Dijk, Jay S. Stanley III, Matthew Amodio, Guy Wolf, Smita Krishnaswamy:
Graph Spectral Regularization for Neural Network Interpretability. CoRR abs/1810.00424 (2018) - 2013
- [b2]Smita Krishnaswamy, Igor L. Markov, John P. Hayes:
Design, Analysis and Test of Logic Circuits Under Uncertainty. Lecture Notes in Electrical Engineering 115, Springer 2013, ISBN 978-90-481-9643-2, pp. 1-120 - [c11]Smita Krishnaswamy, Bernd Bodenmiller, Dana Pe'er:
Can CAD cure cancer? DAC 2013: 142:1-142:2 - [c10]Haoxing Ren, Ruchir Puri, Lakshmi N. Reddy, Smita Krishnaswamy, Cindy Washburn, Joel Earl, Joachim Keinert:
Intuitive ECO synthesis for high performance circuits. DATE 2013: 1002-1007 - 2012
- [c9]Tobias Welp, Smita Krishnaswamy, Andreas Kuehlmann:
Generalized SAT-sweeping for post-mapping optimization. DAC 2012: 814-819 - 2011
- [c8]Douglas Densmore, Mark Horowitz, Smita Krishnaswamy, Xiling Shen, Adam P. Arkin, Erik Winfree, Chris Voigt:
Joint DAC/IWBDA special session design and synthesis of biological circuits. DAC 2011: 114-115 - 2010
- [c7]David A. Papa, Smita Krishnaswamy, Igor L. Markov:
SPIRE: A retiming-based physical-synthesis transformation system. ICCAD 2010: 373-380
2000 – 2009
- 2009
- [j3]Smita Krishnaswamy, Stephen Plaza, Igor L. Markov, John P. Hayes:
Signature-Based SER Analysis and Design of Logic Circuits. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 28(1): 74-86 (2009) - [c6]Smita Krishnaswamy, Igor L. Markov, John P. Hayes:
Improving testability and soft-error resilience through retiming. DAC 2009: 508-513 - [c5]Smita Krishnaswamy, Haoxing Ren, Nilesh Modi, Ruchir Puri:
DeltaSyn: An efficient logic difference optimizer for ECO synthesis. ICCAD 2009: 789-796 - 2008
- [b1]Smita Krishnaswamy:
Design, Analysis and Test of Logic Circuits under Uncertainty. University of Michigan, USA, 2008 - [j2]Smita Krishnaswamy, George F. Viamontes, Igor L. Markov, John P. Hayes:
Probabilistic transfer matrices in symbolic reliability analysis of logic circuits. ACM Trans. Design Autom. Electr. Syst. 13(1): 8:1-8:35 (2008) - [c4]Smita Krishnaswamy, Igor L. Markov, John P. Hayes:
On the role of timing masking in reliable logic circuit design. DAC 2008: 924-929 - 2007
- [j1]Smita Krishnaswamy, Igor L. Markov, John P. Hayes:
Tracking Uncertainty with Probabilistic Logic Circuit Testing. IEEE Des. Test Comput. 24(4): 312-321 (2007) - [c3]Smita Krishnaswamy, Stephen Plaza, Igor L. Markov, John P. Hayes:
Enhancing design robustness with reliability-aware resynthesis and logic simulation. ICCAD 2007: 149-154 - 2005
- [c2]Smita Krishnaswamy, George F. Viamontes, Igor L. Markov, John P. Hayes:
Accurate Reliability Evaluation and Enhancement via Probabilistic Transfer Matrices. DATE 2005: 282-287 - [c1]Smita Krishnaswamy, Igor L. Markov, John P. Hayes:
Logic circuit testing for transient faults. ETS 2005: 102-107
Coauthor Index
Michael Perlmutter
aka: Michael A. Perlmutter
aka: Michael A. Perlmutter
[j10] [c51] [i50] [j9] [j8] [c46] [c45] [c44] [c42] [i45] [i44] [i43] [i42] [i38] [i35] [j5] [c41] [c40] [c38] [c36] [i34] [i32] [i29] [i28] [i25] [c34] [c33] [c29] [c28] [i24] [i23] [i22] [i21] [i19] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [i17] [i16] [i15] [i13] [c18] [c17] [c15] [i11] [i10] [i8] [i7] [c12] [i5] [i4] [i2] [i1]
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