Aviv Netanyahu

I am a PhD student in EECS at MIT CSAIL, advised by Prof. Pulkit Agrawal, as part of the Embodied Intelligence Community of Research.

I earned my MSc from the Faculty of Mathematics and Computer Science at the Weizmann Institute of Science, advised by Prof. Shimon Ullman, where I worked on Computer Vision. Before that, I received my BSc in Mathematics and Computer Science from the Hebrew University of Jerusalem.

Email / Twitter / LinkedIn

Research

I am interested in studying how to deploy agents in new environments with minimum to no supervision. My research focuses, in this context, on inferring how agents interact with the environment and with other agents in ways that are generalizable to new settings. Issues related to this research include combinatorial and out-of-distribution generalization, learning from demonstrations, human in the loop learning and object-centric learning.

	Learning to Extrapolate: A Transductive Approach Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal ICLR 2023 (In Submission) NeurIPS Workshop on Distribution Shifts, 2022 Out-of-support generalization via transductive reparameteraization to within-support combinatorial generalization and bilinear embeddings.
	Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning Aviv Netanyahu, Tianmin Shu, Joshua B. Tenenbaum, Pulkit Agrawal ICML 2022 RLDM 2022 ICLR Elements of Reasoning: Objects, Structure and Causality Workshop 2022 project page / arXiv / talk / code Graph-based one-shot reward learning via active learning for object rearrangement tasks.
	PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception Aviv Netanyahu, Tianmin Shu, Boris Katz, Andrei Barbu, Joshua B. Tenenbaum AAAI 2021 NeurIPS Shared Visual Representations in Human & Machine Intelligence Workshop 2020 (Oral Presentation, Best Paper Award) project page / arXiv / talk / code A dataset for inferring physically grounded social interactions between two agents.
	Image Interpretation by Iterative Bottom-Up Top-Down Processing Shimon Ullman, Liav Assif, Alona Strugatski, Ben-Zion Vatashsky, Hila Levi, Aviv Netanyahu, Adam Yaari under review arXiv / code Extraction and representation of scene components, such as objects and their parts, people, and places, individual properties, and relations and interactions between them by combining bottom-up and top-down networks, interacting through a symmetric bi-directional communication between them.
	Cyclical Bottom-Up Top-Down Neural Networks for Relational Reasoning MSc thesis 2018 A system for selective visual relationship detection that achieves out-of-distribution and combinatorial generalization.

Teaching

TA, Machine Learning (6.867), MIT Fall 2021
Co-instructor, Deep Learning for Control (6.S090), MIT Winter 2021

Service

Organizer Computational Sensorimotor Learning (CSL) Seminar, MIT 2021
Outreach Officer GW6 2020, Lecturer 'Rishonot BaMada' 2017-2019, Coding instructor 'Project Mehamemet' 2016-2019

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