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ORCIDGal Mishne
Person information
- affiliation: University of California, San Diego, Department of Computer Science and Engineering, CA, USA
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2020 – today
- 2024
[j14]Alexander Cloninger, Gal Mishne
, Andreas Oslandsbotn, Sawyer Jack Robertson, Zhengchao Wan, Yusu Wang:
Random Walks, Conductance, and Resistance for the Connection Graph Laplacian. SIAM J. Matrix Anal. Appl. 45(3): 1541-1572 (2024)- [c20]Changhao Shi, Gal Mishne:
Learning Cartesian Product Graphs with Laplacian Constraints. AISTATS 2024: 2521-2529 - [c19]Mitchell Black, Zhengchao Wan, Gal Mishne, Amir Nayyeri, Yusu Wang:
Comparing Graph Transformers via Positional Encodings. ICML 2024 - [c18]Noga Mudrik, Gal Mishne, Adam S. Charles:
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States. ICML 2024 - [c17]Ram Dyuthi Sristi, Ofir Lindenbaum, Shira Lifshitz, Maria Lavzin, Jackie Schiller, Gal Mishne, Hadas Benisty:
Contextual Feature Selection with Conditional Stochastic Gates. ICML 2024 - [i28]Changhao Shi, Gal Mishne:
Learning Cartesian Product Graphs with Laplacian Constraints. CoRR abs/2402.08105 (2024) - [i27]Gal Mishne, Adam S. Charles:
Deep and shallow data science for multi-scale optical neuroscience. CoRR abs/2402.08811 (2024) - [i26]Mitchell Black, Zhengchao Wan, Gal Mishne, Amir Nayyeri, Yusu Wang:
Comparing Graph Transformers via Positional Encodings. CoRR abs/2402.14202 (2024) - [i25]Jiancheng Xie, Lou C. Kohler Voinov, Noga Mudrik, Gal Mishne, Adam S. Charles:
Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training. CoRR abs/2406.01969 (2024) - [i24]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
- [c16]Xinyue Xia, Gal Mishne, Yusu Wang:
Implicit Graphon Neural Representation. AISTATS 2023: 10619-10634 - [c15]Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon:
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning. ICML 2023: 21003-21025 - [c14]Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang:
The Numerical Stability of Hyperbolic Representation Learning. ICML 2023: 24925-24949 - [c13]Jesse He, Tristan Brugère, Gal Mishne:
Product Manifold Learning with Independent Coordinate Selection. TAG-ML 2023: 267-277 - [i23]Dhruv Kohli, Gal Mishne, Alexander Cloninger:
Non-degenerate Rigid Alignment in a Patch Framework. CoRR abs/2303.11620 (2023) - [i22]Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon:
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning. CoRR abs/2305.18962 (2023) - [i21]Noga Mudrik, Gal Mishne, Adam S. Charles:
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States. CoRR abs/2306.04817 (2023) - [i20]Changhao Shi, Gal Mishne:
Graph Laplacian Learning with Exponential Family Noise. CoRR abs/2306.08201 (2023) - [i19]Chester Holtz, Pengwen Chen, Alexander Cloninger, Chung-Kuan Cheng, Gal Mishne:
Semi-Supervised Laplacian Learning on Stiefel Manifolds. CoRR abs/2308.00142 (2023) - [i18]Alexander Cloninger, Gal Mishne, Andreas Oslandsbotn, Sawyer Jack Robertson, Zhengchao Wan, Yusu Wang:
Random Walks, Conductance, and Resistance for the Connection Graph Laplacian. CoRR abs/2308.09690 (2023) - [i17]Sawyer Jack Robertson, Dhruv Kohli, Gal Mishne, Alexander Cloninger:
On a Generalization of Wasserstein Distance and the Beckmann Problem to Connection Graphs. CoRR abs/2312.10295 (2023) - [i16]Ram Dyuthi Sristi, Ofir Lindenbaum, Maria Lavzin, Jackie Schiller, Gal Mishne, Hadas Benisty:
Contextual Feature Selection with Conditional Stochastic Gates. CoRR abs/2312.14254 (2023) - 2022
- [j13]George Armstrong, Gibraan Rahman, Cameron Martino, Daniel McDonald, Antonio González, Gal Mishne, Rob Knight:
Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data. Frontiers Bioinform. 2: 821861 (2022) - [j12]Min Zhang, Gal Mishne, Eric C. Chi:
Multi-scale affinities with missing data: Estimation and applications. Stat. Anal. Data Min. 15(3): 303-313 (2022) - [j11]Adam S. Charles, Nathan Cermak, Rifqi O. Affan, Benjamin B. Scott, Jackie Schiller, Gal Mishne:
GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging. IEEE Trans. Image Process. 31: 3509-3524 (2022) - [c12]Ram Dyuthi Sristi, Gal Mishne, Ariel Jaffe:
DiSC: Differential Spectral Clustering of Features. NeurIPS 2022 - [c11]Chester Holtz, Gal Mishne, Alexander Cloninger:
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors. TAG-ML 2022: 161-171 - [i15]Chester Holtz, Gal Mishne, Alexander Cloninger:
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors. CoRR abs/2210.01760 (2022) - [i14]Chester Holtz, Tsui-Wei Weng, Gal Mishne:
Learning Sample Reweighting for Accuracy and Adversarial Robustness. CoRR abs/2210.11513 (2022) - [i13]Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang:
The Numerical Stability of Hyperbolic Representation Learning. CoRR abs/2211.00181 (2022) - [i12]Xinyue Xia, Gal Mishne, Yusu Wang:
Implicit Graphon Neural Representation. CoRR abs/2211.03329 (2022) - [i11]Ram Dyuthi Sristi, Gal Mishne, Ariel Jaffe:
DiSC: Differential Spectral Clustering of Features. CoRR abs/2211.05314 (2022) - 2021
- [j10]Haidong Yi, Le Huang, Gal Mishne, Eric C. Chi:
COBRAC: a fast implementation of convex biclustering with compression. Bioinform. 37(20): 3667-3669 (2021) - [j9]Dhruv Kohli, Alexander Cloninger, Gal Mishne:
LDLE: Low Distortion Local Eigenmaps. J. Mach. Learn. Res. 22: 282:1-282:64 (2021) - [j8]Siyuan Gao, Xinyue Xia, Dustin Scheinost, Gal Mishne:
Smooth graph learning for functional connectivity estimation. NeuroImage 239: 118289 (2021) - [c10]Changhao Shi, Chester Holtz, Gal Mishne:
Online Adversarial Purification based on Self-supervised Learning. ICLR 2021 - [c9]Changhao Shi, Sivan Schwartz, Shahar Levy, Shay Achvat, Maisan Abboud, Amir Ghanayim, Jackie Schiller, Gal Mishne:
Learning Disentangled Behavior Embeddings. NeurIPS 2021: 22562-22573 - [i10]Changhao Shi, Chester Holtz, Gal Mishne:
Online Adversarial Purification based on Self-Supervision. CoRR abs/2101.09387 (2021) - [i9]Dhruv Kohli, Alexander Cloninger, Gal Mishne:
LDLE: Low Distortion Local Eigenmaps. CoRR abs/2101.11055 (2021) - 2020
- [j7]Ariel Jaffe, Yuval Kluger, George C. Linderman, Gal Mishne, Stefan Steinerberger:
Randomized near-neighbor graphs, giant components and applications in data science. J. Appl. Probab. 57(2): 458-476 (2020) - [j6]Xiuyuan Cheng, Gal Mishne:
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian. SIAM J. Imaging Sci. 13(2): 1015-1048 (2020) - [j5]Jay S. Stanley III, Eric C. Chi, Gal Mishne:
Multiway Graph Signal Processing on Tensors: Integrative Analysis of Irregular Geometries. IEEE Signal Process. Mag. 37(6): 160-173 (2020) - [c8]Siyuan Gao, Gal Mishne, Dustin Scheinost:
Poincaré Embedding Reveals Edge-Based Functional Networks of the Brain. MICCAI (7) 2020: 448-457 - [i8]Jay S. Stanley III, Eric C. Chi, Gal Mishne:
Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries. CoRR abs/2007.00041 (2020) - [i7]Ofir Lindenbaum, Amir Sagiv, Gal Mishne, Ronen Talmon:
Kernel-based parameter estimation of dynamical systems with unknown observation functions. CoRR abs/2009.04142 (2020)
2010 – 2019
- 2019
- [c7]Gal Mishne, Adam S. Charles:
Learning Spatially-correlated Temporal Dictionaries for Calcium Imaging. ICASSP 2019: 1065-1069 - [c6]Gal Mishne, Eric C. Chi, Ronald R. Coifman:
Co-manifold learning with missing data. ICML 2019: 4605-4614 - [c5]Siyuan Gao, Gal Mishne, Dustin Scheinost:
A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data. IPMI 2019: 631-643 - [c4]Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne:
Visualizing the PHATE of Neural Networks. NeurIPS 2019: 1840-1851 - [i6]Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne:
Visualizing the PHATE of Neural Networks. CoRR abs/1908.02831 (2019) - 2018
- [j4]Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger:
Data-Driven Tree Transforms and Metrics. IEEE Trans. Signal Inf. Process. over Networks 4(3): 451-466 (2018) - [i5]Gal Mishne, Eric C. Chi, Ronald R. Coifman:
Co-manifold learning with missing data. CoRR abs/1810.06803 (2018) - [i4]Xiuyuan Cheng, Gal Mishne:
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian. CoRR abs/1810.10695 (2018) - 2017
- [c3]Gal Mishne, Israel Cohen:
Iterative diffusion-based anomaly detection. ICASSP 2017: 1682-1686 - [i3]Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger:
Data-Driven Tree Transforms and Metrics. CoRR abs/1708.05768 (2017) - [i2]George C. Linderman, Gal Mishne, Yuval Kluger, Stefan Steinerberger:
Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science. CoRR abs/1711.04712 (2017) - 2016
- [j3]Gal Mishne, Ronen Talmon, Ron Meir, Jackie Schiller, Maria Lavzin, Uri Dubin, Ronald R. Coifman:
Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery. IEEE J. Sel. Top. Signal Process. 10(7): 1238-1253 (2016) - [c2]Ron Amit, Gal Mishne, Ronen Talmon:
Improving resolution in supervised patch-based target detection. ICASSP 2016: 1994-1998 - 2015
- [j2]Gal Mishne, Ronen Talmon, Israel Cohen:
Graph-Based Supervised Automatic Target Detection. IEEE Trans. Geosci. Remote. Sens. 53(5): 2738-2754 (2015) - [i1]Gal Mishne, Uri Shaham, Alexander Cloninger, Israel Cohen:
Diffusion Nets. CoRR abs/1506.07840 (2015) - 2014
- [c1]Gal Mishne, Israel Cohen:
Multiscale anomaly detection using diffusion maps and saliency score. ICASSP 2014: 2823-2827 - 2013
- [j1]Gal Mishne, Israel Cohen:
Multiscale Anomaly Detection Using Diffusion Maps. IEEE J. Sel. Top. Signal Process. 7(1): 111-123 (2013)
Coauthor Index
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last updated on 2024-10-07 21:25 CEST by the dblp team
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