b'Gal Chechik'

research

∙ 07/13/2023

Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models

Text-to-image (T2I) personalization allows users to guide the creative i...

0 Moab Arar, et al. ∙

research

∙ 06/18/2023

Point-Cloud Completion with Pretrained Text-to-image Diffusion Models

Point-cloud data collected in real-world applications are often incomple...

0 Yoni Kasten, et al. ∙

research

∙ 06/15/2023

Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment

Text-conditioned image generation models often generate incorrect associ...

7 Royi Rassin, et al. ∙

research

∙ 05/30/2023

DisCLIP: Open-Vocabulary Referring Expression Generation

Referring Expressions Generation (REG) aims to produce textual descripti...

0 Lior Bracha, et al. ∙

research

∙ 05/02/2023

Key-Locked Rank One Editing for Text-to-Image Personalization

Text-to-image models (T2I) offer a new level of flexibility by allowing ...

0 Yoad Tewel, et al. ∙

research

∙ 05/02/2023

CALM: Conditional Adversarial Latent Models for Directable Virtual Characters

In this work, we present Conditional Adversarial Latent Models (CALM), a...

0 Chen Tessler, et al. ∙

research

∙ 03/06/2023

Graph Positional Encoding via Random Feature Propagation

Two main families of node feature augmentation schemes have been explore...

0 Moshe Eliasof, et al. ∙

research

∙ 02/23/2023

Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models

Text-to-image personalization aims to teach a pre-trained diffusion mode...

0 Rinon Gal, et al. ∙

research

∙ 02/19/2023

Guided Deep Kernel Learning

Combining Gaussian processes with the expressive power of deep neural ne...

0 Idan Achituve, et al. ∙

research

∙ 01/31/2023

Auxiliary Learning as an Asymmetric Bargaining Game

Auxiliary learning is an effective method for enhancing the generalizati...

0 Aviv Shamsian, et al. ∙

research

∙ 01/30/2023

SoftTreeMax: Exponential Variance Reduction in Policy Gradient via Tree Search

Despite the popularity of policy gradient methods, they are known to suf...

0 Gal Dalal, et al. ∙

research

∙ 01/30/2023

Equivariant Architectures for Learning in Deep Weight Spaces

Designing machine learning architectures for processing neural networks ...

0 Aviv Navon, et al. ∙

research

∙ 01/26/2023

Train Hard, Fight Easy: Robust Meta Reinforcement Learning

A major challenge of reinforcement learning (RL) in real-world applicati...

0 Ido Greenberg, et al. ∙

research

∙ 10/27/2022

Text2Model: Model Induction for Zero-shot Generalization Using Task Descriptions

We study the problem of generating a training-free task-dependent visual...

0 Ohad Amosy, et al. ∙

research

∙ 09/28/2022

SoftTreeMax: Policy Gradient with Tree Search

Policy-gradient methods are widely used for learning control policies. T...

0 Gal Dalal, et al. ∙

research

∙ 08/02/2022

An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Text-to-image models offer unprecedented freedom to guide creation throu...

2 Rinon Gal, et al. ∙

research

∙ 07/05/2022

Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs

Cloud datacenters are exponentially growing both in numbers and size. Th...

0 Benjamin Fuhrer, et al. ∙

research

∙ 05/30/2022

Reinforcement Learning with a Terminator

We present the problem of reinforcement learning with exogenous terminat...

0 Guy Tennenholtz, et al. ∙

research

∙ 04/18/2022

Optimizing Tensor Network Contraction Using Reinforcement Learning

Quantum Computing (QC) stands to revolutionize computing, but is current...

0 Eli A. Meirom, et al. ∙

research

∙ 04/04/2022

"This is my unicorn, Fluffy": Personalizing frozen vision-language representations

Large Vision Language models pretrained on web-scale data provide re...

0 Niv Cohen, et al. ∙

research

∙ 03/27/2022

Example-based Hypernetworks for Out-of-Distribution Generalization

While Natural Language Processing (NLP) algorithms keep reaching unprece...

0 Tomer Volk, et al. ∙

research

∙ 02/02/2022

Learning to reason about and to act on physical cascading events

Reasoning and interacting with dynamic environments is a fundamental pro...

0 Yuval Atzmon, et al. ∙

research

∙ 02/02/2022

Multi-Task Learning as a Bargaining Game

In Multi-task learning (MTL), a joint model is trained to simultaneously...

0 Aviv Navon, et al. ∙

research

∙ 01/28/2022

Planning and Learning with Adaptive Lookahead

The classical Policy Iteration (PI) algorithm alternates between greedy ...

0 Aviv Rosenberg, et al. ∙

research

∙ 01/20/2022

Federated Learning with Heterogeneous Architectures using Graph HyperNetworks

Standard Federated Learning (FL) techniques are limited to clients with ...

14 Or Litany, et al. ∙

research

∙ 11/16/2021

Inference-Time Personalized Federated Learning

In Federated learning (FL), multiple clients collaborate to learn a mode...

0 Ohad Amosy, et al. ∙

research

∙ 10/13/2021

Object-Region Video Transformers

Evidence from cognitive psychology suggests that understanding spatio-te...

4 Roei Herzig, et al. ∙

research

∙ 10/13/2021

On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning

We consider the problem of using expert data with unobserved confounders...

0 Guy Tennenholtz, et al. ∙

research

∙ 08/02/2021

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

Can a generative model be trained to produce images from a specific doma...

13 Rinon Gal, et al. ∙

research

∙ 07/04/2021

Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction

Tree Search (TS) is crucial to some of the most influential successes in...

0 Assaf Hallak, et al. ∙

research

∙ 06/29/2021

Personalized Federated Learning with Gaussian Processes

Federated learning aims to learn a global model that performs well on cl...

0 Idan Achituve, et al. ∙

research

∙ 06/08/2021

DETReg: Unsupervised Pretraining with Region Priors for Object Detection

Unsupervised pretraining has recently proven beneficial for computer vis...

6 Amir Bar, et al. ∙

research

∙ 04/07/2021

Distributional Robustness Loss for Long-tail Learning

Real-world data is often unbalanced and long-tailed, but deep models str...

0 Dvir Samuel, et al. ∙

research

∙ 03/08/2021

Personalized Federated Learning using Hypernetworks

Personalized federated learning is tasked with training machine learning...

0 Aviv Shamsian, et al. ∙

research

∙ 02/18/2021

Reinforcement Learning for Datacenter Congestion Control

We approach the task of network congestion control in datacenters using ...

0 Chen Tessler, et al. ∙

research

∙ 02/15/2021

GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning

Gaussian processes (GPs) are non-parametric, flexible, models that work ...

0 Idan Achituve, et al. ∙

research

∙ 01/26/2021

Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning

Large-scale datasets are the cornerstone of self-supervised representati...

8 Sangho Lee, et al. ∙

research

∙ 10/20/2020

Teacher-Student Consistency For Multi-Source Domain Adaptation

In Multi-Source Domain Adaptation (MSDA), models are trained on samples ...

0 Ohad Amosy, et al. ∙

research

∙ 10/17/2020

On Size Generalization in Graph Neural Networks

Graph neural networks (GNNs) can process graphs of different sizes but t...

0 Gilad Yehudai, et al. ∙

research

∙ 10/11/2020

How to Stop Epidemics: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

We consider the problem of monitoring and controlling a partially-observ...

0 Eli A. Meirom, et al. ∙

research

∙ 10/08/2020

Learning the Pareto Front with Hypernetworks

Multi-objective optimization problems are prevalent in machine learning....

0 Aviv Navon, et al. ∙

research

∙ 10/07/2020

ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization

We study the problem of recognizing visual entities from the textual des...

0 Tzuf Paz-Argaman, et al. ∙

research

∙ 09/30/2020

Learning Object Detection from Captions via Textual Scene Attributes

Object detection is a fundamental task in computer vision, requiring lar...

9 Achiya Jerbi, et al. ∙

research

∙ 06/27/2020

Compositional Video Synthesis with Action Graphs

Videos of actions are complex spatio-temporal signals, containing rich c...

3 Amir Bar, et al. ∙

research

∙ 06/25/2020

A causal view of compositional zero-shot recognition

People easily recognize new visual categories that are new combinations ...

18 Yuval Atzmon, et al. ∙

research

∙ 06/17/2020

Contrastive Learning for Weakly Supervised Phrase Grounding

Phrase grounding, the problem of associating image regions to caption wo...

4 Tanmay Gupta, et al. ∙

research

∙ 04/05/2020

Long-tail learning with attributes

Learning to classify images with unbalanced class distributions is chall...

8 Dvir Samuel, et al. ∙

research

∙ 03/29/2020

Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-supervised learning (SSL) allows to learn useful representations fr...

13 Idan Achituve, et al. ∙

research

∙ 03/23/2020

Learning Object Permanence from Video

Object Permanence allows people to reason about the location of non-visi...

11 Aviv Shamsian, et al. ∙

research

∙ 02/20/2020

On Learning Sets of Symmetric Elements

Learning from unordered sets is a fundamental learning setup, which is a...

21 Haggai Maron, et al. ∙

Gal Chechik

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