Search Results for author: Yossi Matias

Found 23 papers, 4 papers with code

Dreamix: Video Diffusion Models are General Video Editors

no code implementations2 Feb 2023 Eyal Molad, Eliahu Horwitz, Dani Valevski, Alex Rav Acha, Yossi Matias, Yael Pritch, Yaniv Leviathan, Yedid Hoshen

Our approach uses a video diffusion model to combine, at inference time, the low-resolution spatio-temporal information from the original video with new, high resolution information that it synthesized to align with the guiding text prompt.

Image Animation Image to Video Generation +3

Fast Inference from Transformers via Speculative Decoding

no code implementations30 Nov 2022 Yaniv Leviathan, Matan Kalman, Yossi Matias

Inference from large autoregressive models like Transformers is slow - decoding K tokens takes K serial runs of the model.

Language Modelling

Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning

no code implementations25 Jul 2022 Deborah Cohen, MoonKyung Ryu, Yinlam Chow, Orgad Keller, Ido Greenberg, Avinatan Hassidim, Michael Fink, Yossi Matias, Idan Szpektor, Craig Boutilier, Gal Elidan

Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge.

Natural Language Understanding reinforcement-learning +1

Adversarial Robustness of Streaming Algorithms through Importance Sampling

no code implementations NeurIPS 2021 Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou

In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more general counterparts, subspace embedding, low-rank approximation, and coreset construction.

Adversarial Robustness regression

Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling

1 code implementation NeurIPS 2021 Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry

Background: Floods are the most common natural disaster in the world, affecting the lives of hundreds of millions.

ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach

no code implementations29 Nov 2020 Sella Nevo, Gal Elidan, Avinatan Hassidim, Guy Shalev, Oren Gilon, Grey Nearing, Yossi Matias

Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm.

Adversarially Robust Streaming Algorithms via Differential Privacy

no code implementations NeurIPS 2020 Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary.

Adversarial Robustness

Detecting Deficient Coverage in Colonoscopies

no code implementations23 Jan 2020 Daniel Freedman, Yochai Blau, Liran Katzir, Amit Aides, Ilan Shimshoni, Danny Veikherman, Tomer Golany, Ariel Gordon, Greg Corrado, Yossi Matias, Ehud Rivlin

Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.

Depth Estimation

Spectral Algorithm for Low-rank Multitask Regression

no code implementations27 Oct 2019 Yotam Gigi, Ami Wiesel, Sella Nevo, Gal Elidan, Avinatan Hassidim, Yossi Matias

In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model.

Image Classification regression

LSH Microbatches for Stochastic Gradients: Value in Rearrangement

no code implementations ICLR 2019 Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias

We make a principled argument for the properties of our arrangements that accelerate the training and present efficient algorithms to generate microbatches that respect the marginal distribution of training examples.

Differentially Private Learning of Geometric Concepts

no code implementations13 Feb 2019 Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex).

PAC learning

ML for Flood Forecasting at Scale

no code implementations28 Jan 2019 Sella Nevo, Vova Anisimov, Gal Elidan, Ran El-Yaniv, Pete Giencke, Yotam Gigi, Avinatan Hassidim, Zach Moshe, Mor Schlesinger, Guy Shalev, Ajai Tirumali, Ami Wiesel, Oleg Zlydenko, Yossi Matias

We propose to build on these strengths and develop ML systems for timely and accurate riverine flood prediction.

Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many

no code implementations3 Jan 2019 Yotam Gigi, Gal Elidan, Avinatan Hassidim, Yossi Matias, Zach Moshe, Sella Nevo, Guy Shalev, Ami Wiesel

We demonstrate the efficacy of our approach for the problem of discharge estimation using simulations.

Self-Similar Epochs: Value in Arrangement

no code implementations ICLR 2019 Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias

Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples.

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