Search Results for author: Gal Mishne

Found 17 papers, 6 papers with code

Data Processing of Functional Optical Microscopy for Neuroscience

no code implementations10 Jan 2022 Hadas Benisty, Alexander Song, Gal Mishne, Adam S. Charles

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators.

Learning Disentangled Behavior Embeddings

1 code implementation NeurIPS 2021 Changhao Shi, Sivan Schwartz, Shahar Levy, Shay Achvat, Maisan Abboud, Amir Ghanayim, Jackie Schiller, Gal Mishne

To understand the relationship between behavior and neural activity, experiments in neuroscience often include an animal performing a repeated behavior such as a motor task.

Learning Sample Reweighting for Adversarial Robustness

no code implementations29 Sep 2021 Chester Holtz, Tsui-Wei Weng, Gal Mishne

There has been great interest in enhancing the robustness of neural network classifiers to defend against adversarial perturbations through adversarial training, while balancing the trade-off between robust accuracy and standard accuracy.

Adversarial Robustness Bilevel Optimization

LDLE: Low Distortion Local Eigenmaps

1 code implementation ICLR Workshop GTRL 2021 Dhruv Kohli, Alexander Cloninger, Gal Mishne

We present Low Distortion Local Eigenmaps (LDLE), a manifold learning technique which constructs a set of low distortion local views of a dataset in lower dimension and registers them to obtain a global embedding.

Online Adversarial Purification based on Self-Supervision

no code implementations23 Jan 2021 Changhao Shi, Chester Holtz, Gal Mishne

To the best of our knowledge, our paper is the first that generalizes the idea of using self-supervised signals to perform online test-time purification.

Representation Learning

Provable Robustness by Geometric Regularization of ReLU Networks

no code implementations1 Jan 2021 Chester Holtz, Changhao Shi, Gal Mishne

Recent work has demonstrated that neural networks are vulnerable to small, adversarial perturbations of their input.

Online Adversarial Purification based on Self-supervised Learning

no code implementations ICLR 2021 Changhao Shi, Chester Holtz, Gal Mishne

Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation.

Representation Learning Self-Supervised Learning

Kernel-based parameter estimation of dynamical systems with unknown observation functions

no code implementations9 Sep 2020 Ofir Lindenbaum, Amir Sagiv, Gal Mishne, Ronen Talmon

A low-dimensional dynamical system is observed in an experiment as a high-dimensional signal; for example, a video of a chaotic pendulums system.

Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries

no code implementations30 Jun 2020 Jay S. Stanley III, Eric C. Chi, Gal Mishne

Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures.

Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian

1 code implementation25 Oct 2018 Xiuyuan Cheng, Gal Mishne

The extraction of clusters from a dataset which includes multiple clusters and a significant background component is a non-trivial task of practical importance.

Anomaly Detection Outlier Detection

Co-manifold learning with missing data

no code implementations16 Oct 2018 Gal Mishne, Eric C. Chi, Ronald R. Coifman

We propose utilizing this coupled structure to perform co-manifold learning: uncovering the underlying geometry of both the rows and the columns of a given matrix, where we focus on a missing data setting.

Data Visualization Representation Learning

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

3 code implementations13 Nov 2017 George C. Linderman, Gal Mishne, Yuval Kluger, Stefan Steinerberger

If we pick $n$ random points uniformly in $[0, 1]^d$ and connect each point to its $k-$nearest neighbors, then it is well known that there exists a giant connected component with high probability.

Data-Driven Tree Transforms and Metrics

1 code implementation18 Aug 2017 Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger

Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality.

The Geometry of Nodal Sets and Outlier Detection

no code implementations5 Jun 2017 Xiuyuan Cheng, Gal Mishne, Stefan Steinerberger

Let $(M, g)$ be a compact manifold and let $-\Delta \phi_k = \lambda_k \phi_k$ be the sequence of Laplacian eigenfunctions.

Outlier Detection

Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discovery

no code implementations6 Nov 2015 Gal Mishne, Ronen Talmon, Ron Meir, Jackie Schiller, Uri Dubin, Ronald R. Coifman

In the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible.

Diffusion Nets

no code implementations25 Jun 2015 Gal Mishne, Uri Shaham, Alexander Cloninger, Israel Cohen

In this paper, we propose a manifold learning algorithm based on deep learning to create an encoder, which maps a high-dimensional dataset and its low-dimensional embedding, and a decoder, which takes the embedded data back to the high-dimensional space.

Outlier Detection

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