Gal Mishne

Orcid: 0000-0002-5287-3626

Affiliations:
  • University of California, San Diego, Department of Computer Science and Engineering, CA, USA


According to our database1, Gal Mishne authored at least 49 papers between 2013 and 2024.

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Bibliography

2024
Random Walks, Conductance, and Resistance for the Connection Graph Laplacian.
SIAM J. Matrix Anal. Appl., 2024

Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122).
Dagstuhl Reports, 2024

Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy.
CoRR, 2024

Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training.
CoRR, 2024

Deep and shallow data science for multi-scale optical neuroscience.
CoRR, 2024

Contextual Feature Selection with Conditional Stochastic Gates.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Comparing Graph Transformers via Positional Encodings.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Learning Cartesian Product Graphs with Laplacian Constraints.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Contextual Feature Selection with Conditional Stochastic Gates.
CoRR, 2023

On a Generalization of Wasserstein Distance and the Beckmann Problem to Connection Graphs.
CoRR, 2023

Semi-Supervised Laplacian Learning on Stiefel Manifolds.
CoRR, 2023

Graph Laplacian Learning with Exponential Family Noise.
CoRR, 2023

Non-degenerate Rigid Alignment in a Patch Framework.
CoRR, 2023

Product Manifold Learning with Independent Coordinate Selection.
Proceedings of the Topological, 2023

The Numerical Stability of Hyperbolic Representation Learning.
Proceedings of the International Conference on Machine Learning, 2023

Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning.
Proceedings of the International Conference on Machine Learning, 2023

Implicit Graphon Neural Representation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging.
IEEE Trans. Image Process., 2022

Multi-scale affinities with missing data: Estimation and applications.
Stat. Anal. Data Min., 2022

Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data.
Frontiers Bioinform., 2022

Learning Sample Reweighting for Accuracy and Adversarial Robustness.
CoRR, 2022

Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors.
Proceedings of the Topological, 2022

DiSC: Differential Spectral Clustering of Features.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Smooth graph learning for functional connectivity estimation.
NeuroImage, 2021

LDLE: Low Distortion Local Eigenmaps.
J. Mach. Learn. Res., 2021

Online Adversarial Purification based on Self-Supervision.
CoRR, 2021

COBRAC: a fast implementation of convex biclustering with compression.
Bioinform., 2021

Learning Disentangled Behavior Embeddings.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Online Adversarial Purification based on Self-supervised Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Multiway Graph Signal Processing on Tensors: Integrative Analysis of Irregular Geometries.
IEEE Signal Process. Mag., 2020

Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian.
SIAM J. Imaging Sci., 2020

Randomized near-neighbor graphs, giant components and applications in data science.
J. Appl. Probab., 2020

Kernel-based parameter estimation of dynamical systems with unknown observation functions.
CoRR, 2020

Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries.
CoRR, 2020

Poincaré Embedding Reveals Edge-Based Functional Networks of the Brain.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019
Visualizing the PHATE of Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data.
Proceedings of the Information Processing in Medical Imaging, 2019

Co-manifold learning with missing data.
Proceedings of the 36th International Conference on Machine Learning, 2019

Learning Spatially-correlated Temporal Dictionaries for Calcium Imaging.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
Data-Driven Tree Transforms and Metrics.
IEEE Trans. Signal Inf. Process. over Networks, 2018

2017
Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science.
CoRR, 2017

Iterative diffusion-based anomaly detection.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016
Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery.
IEEE J. Sel. Top. Signal Process., 2016

Improving resolution in supervised patch-based target detection.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2015
Graph-Based Supervised Automatic Target Detection.
IEEE Trans. Geosci. Remote. Sens., 2015

Diffusion Nets.
CoRR, 2015

2014
Multiscale anomaly detection using diffusion maps and saliency score.
Proceedings of the IEEE International Conference on Acoustics, 2014

2013
Multiscale Anomaly Detection Using Diffusion Maps.
IEEE J. Sel. Top. Signal Process., 2013


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