Tamar Rott Shaham

I am a postdoctoral fellow with Antonio Torralba at CSAIL, MIT. I did my PhD at the Electrical & Computer Engineering faculty of the Technion where I worked with Tomer Michaeli.

My research interests focus on understanding, controlling, and enhancing AI models. I develop interpretability tools that automatically discover and explain the internal operations of machine-learning models and use gained insights to control model behavior, enhance performance, and prevent undesired outcomes.

Office: 45-733D

Office hours: I'm hosting pro bono office hours, here are more details.

Email / Google Scholar / Twitter / Github

News

[April 2024] We introduce MAIA, a Multimodal Automated Interpretability Agent that solves interpretability tasks by iteratively designing experiments on other AI systems.

[Feb 2024] Why does fine-tuning LLMs work so well? Our ICLR'24 paper reveals it's not about introducing new mechanisms but enhancing the existing ones!

[Jan 2024] Check out our new paper: A Vision Check-up for Language Models, to be presented at CVPR'24!

[Dec 2023] MIT News covers our recent work on Automated Interpretability Agents (AIAs)

[Sep 2023] FIND a new benchmark for evaluating automated interpretability methods, was accepted to NeurIPS!

[June 2023] Internal Diverse Image Completion is presented by Noa at CVPR

[Dec 2022] SinGAN follow up paper BlendGAN for smooth image blending across time and space

[Sep 2022] I joined Antonio Torralba's lab as a postdoc

[Oct 2021] Our paper GANs Spatial Control via Inference-Time Adaptive Normalization was accepted to WACV

[Sep 2021] Our works on single audio generative model and deep self-dissimilarities were accepted to NeurIPS

[Jun 2021] Check out our new paper about learning a generative model from a single short audio source

[Mar 2021] Our ASAPNet paper was accepted to CVPR

[Oct 2020] I participated in the IMVC2020's GANs panel

[Aug 2020] We are organizing the Deep Internal Learning (DIL) workshop in conjunction with ECCV 2020 (check out my joint keynote with Tomer)

[Jan 2020] I received the Adobe Research Fellowship

[Jan 2020] I gave a talk about SinGAN at the Israeli Computer Vision day

[Nov 2019] SinGAN won ICCV’19 Best Paper Award (Marr Prize)!

[Aug 2019] I participated in the Google Student Retreat at London, for Women Techmakers Scholars (now called Generation Google Scholarship), and met an amazing group of women from all over Europe, the Middle East and Africa

[July 2019] I am interning at Adobe Research Seattle for summer 2019, working with Eli Shechtman, Michaël Gharbi, and Richard Zhang

Publications

	A Multimodal Automated Interpretability Agent Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang, Franklin Wang, Achyuta Rajaram, Evan Hernandez, Jacob Andreas, Antonio Torralba, paper / web / code / experiment browser
	Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov, David Bau ICLR, 2024 paper / web / code
	A Vision Check-up for Language Models Pratyusha Sharma, Tamar Rott Shaham*, Manel Baradad, Stephanie Fu, Adrián Rodríguez-Muñoz, Shivam Duggal, Phillip Isola, Antonio Torralba CVPR, 2024 Highlight paper* paper / web
	FIND: A Function Interpretation Benchmark for Evaluating Interpretability Methods Sarah Schwettmann, Tamar Rott Shaham*, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba, NeurIPS*, 2023 paper / web / code
	Discovering Variable Binding Circuitry with Desiderata Xander Davies, Max Nadeau, Nikhil Prakash, Tamar Rott Shaham, David Bau, ICML Workshop Deployable Generative AI*, 2023 paper / web / code
	Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts Hanan Tanasra, Tamar Rott Shaham, Tomer Michaeli Guy Austern, Shany Barath, Buildings, 2023 paper
	Internal Diverse Image Completion Noa Alkobi, Tamar Rott Shaham, Tomer Michaeli CVPR, Generative Models for Computer Vision Workshop, 2023 paper
	BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning Idan Kligvasser, Tamar Rott Shaham, Noa Alkobi, Tomer Michaeli Arxiv, 2022 paper
	GANs Spatial Control via Inference-Time Adaptive Normalization Karin Jakoel, Liron Efraim, Tamar Rott Shaham WACV, 2022 paper / video / supplementals
	Catch-A-Waveform: Learning to Generate Audio from a Single Short Example Gal Greshler, Tamar Rott Shaham, Tomer Michaeli NeurIPS, 2021 paper / web / code / supplementals
	Deep Self-Dissimilarities as Powerful Visual Fingerprints Idan Kligvasser, Tamar Rott Shaham, Yuval Bahat, Tomer Michaeli NeurIPS, 2021 Spotlight presentation paper / supplementals
	Spatially-Adaptive Pixelwise Networks for Fast Image Translation Tamar Rott Shaham, Michaël Gharbi, Richard Zhang, Eli Shechtman, Tomer Michaeli CVPR, 2021 project page / arXiv
	SinGAN: Learning a generative model from a single natural image Tamar Rott Shaham, Tali Dekel, Tomer Michaeli ICCV, 2019 Best Paper Award (Marr Prize) project page / arXiv / CVF / supp / code / ICCV talk / Israel Vision Day talk (recommended)
	Deformation Aware image Compression Tamar Rott Shaham, Tomer Michaeli CVPR, 2018 Spotlight presentation project page / paper / code / spotlight
	xUnit: Learning a Spatial Activation Function for Efficient Image Restoration Idan Kligvasser Tamar Rott Shaham, Tomer Michaeli CVPR, 2018 Spotlight presentation paper / code / spotlight (by Idan)
	Visualizing Image Priors Tamar Rott Shaham, Tomer Michaeli ECCV, 2016 project page / paper / poster
	Edge Preserving Multi-Modal Registration Based On Gradient Intensity Self-Similarity Tamar Rott Shaham, Dorin Shriki, Tamir Bendory IEEEI, 2014 paper

Pro bono office hour

Inspired by Krishna Murthy and Wei-Chiu Ma, I dedicate 1-2 hours each week to providing guidance and mentorship to students from underrepresented groups, or to anyone who needs it. Specifically, if you're searching for a postdoc position, I'd be happy to share insights from my experience. Please fill out this form to contact me.

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