Search Results for author: Aviv Navon

Found 17 papers, 11 papers with code

Multi Task Inverse Reinforcement Learning for Common Sense Reward

no code implementations17 Feb 2024 Neta Glazer, Aviv Navon, Aviv Shamsian, Ethan Fetaya

One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function.

Common Sense Reasoning reinforcement-learning

Improved Generalization of Weight Space Networks via Augmentations

no code implementations6 Feb 2024 Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron

Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural fields (INRs, NeRFs), as well as making inferences about other types of neural networks.

Contrastive Learning Data Augmentation

Data Augmentations in Deep Weight Spaces

no code implementations15 Nov 2023 Aviv Shamsian, David W. Zhang, Aviv Navon, Yan Zhang, Miltiadis Kofinas, Idan Achituve, Riccardo Valperga, Gertjan J. Burghouts, Efstratios Gavves, Cees G. M. Snoek, Ethan Fetaya, Gal Chechik, Haggai Maron

Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various fields, from analyzing and editing neural fields and implicit neural representations, to network pruning and quantization.

Data Augmentation Network Pruning +1

Equivariant Deep Weight Space Alignment

1 code implementation20 Oct 2023 Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron

To accelerate the alignment process and improve its quality, we propose a novel framework aimed at learning to solve the weight alignment problem, which we name Deep-Align.

Open-vocabulary Keyword-spotting with Adaptive Instance Normalization

no code implementations13 Sep 2023 Aviv Navon, Aviv Shamsian, Neta Glazer, Gill Hetz, Joseph Keshet

Open vocabulary keyword spotting is a crucial and challenging task in automatic speech recognition (ASR) that focuses on detecting user-defined keywords within a spoken utterance.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Learning Discrete Weights and Activations Using the Local Reparameterization Trick

no code implementations4 Jul 2023 Guy Berger, Aviv Navon, Ethan Fetaya

In computer vision and machine learning, a crucial challenge is to lower the computation and memory demands for neural network inference.

Binarization Efficient Neural Network

Auxiliary Learning as an Asymmetric Bargaining Game

1 code implementation31 Jan 2023 Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya

Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets.

Auxiliary Learning

Equivariant Architectures for Learning in Deep Weight Spaces

1 code implementation30 Jan 2023 Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron

Designing machine learning architectures for processing neural networks in their raw weight matrix form is a newly introduced research direction.

A Study on the Evaluation of Generative Models

1 code implementation22 Jun 2022 Eyal Betzalel, Coby Penso, Aviv Navon, Ethan Fetaya

In this work, we study the evaluation metrics of generative models by generating a high-quality synthetic dataset on which we can estimate classical metrics for comparison.

Multi-Task Learning as a Bargaining Game

2 code implementations2 Feb 2022 Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya

In this paper, we propose viewing the gradients combination step as a bargaining game, where tasks negotiate to reach an agreement on a joint direction of parameter update.

Multi-Task Learning

Personalized Federated Learning using Hypernetworks

2 code implementations8 Mar 2021 Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik

In this approach, a central hypernetwork model is trained to generate a set of models, one model for each client.

Personalized Federated Learning

Learning the Pareto Front with Hypernetworks

1 code implementation ICLR 2021 Aviv Navon, Aviv Shamsian, Gal Chechik, Ethan Fetaya

Here, we tackle the problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training.

Fairness Multiobjective Optimization +3

Auxiliary Learning by Implicit Differentiation

1 code implementation ICLR 2021 Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya

Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss.

Auxiliary Learning Image Segmentation +3

Capturing Between-Tasks Covariance and Similarities Using Multivariate Linear Mixed Models

1 code implementation10 Dec 2018 Aviv Navon, Saharon Rosset

This setting naturally induces a group structure over the coefficient matrix, in which every explanatory variable corresponds to a set of related coefficients.


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