I am a Postdoctoral Associate at the Center for Brains, Mind and Machines at MIT. I previously worked as a Research Scientist intern at DeepMind's Foundations team and received my Ph.D. in Computer Science from Tel Aviv University.

I study the foundational and algorithmic aspects of Deep Learning with a focus on understanding how knowledge is acquired and represented in artificial neural networks. I investigate how geometric and statistical structures develop in neural networks; how the learning task, the optimization process, and architectural choices promote these structures; and their relationship to different aspects of learning.

Email: surname at mit.edu
Github: TomerGalanti
Google scholar: Tomer Galanti

Publications

Norm-Based Generalization Bounds for Sparse Neural Networks
T. Galanti, M. Xu, L. Galanti, T. Poggio
Neural Information Processing Systems, NeurIPS, 2023.

Reverse Engineering Self-Supervised Learning
I. Ben-Shaul, R. Shwartz-Ziv*, T. Galanti*, S. Dekel, Y. LeCun
Neural Information Processing Systems, NeurIPS, 2023.

Comparative Generalization Bounds for Deep Neural Networks
T. Galanti, L. Galanti, I. Ben-Shaul
Transactions in Machine Learning Research, TMLR, 2023.

Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions
I. Ben-Shaul, T. Galanti, S. Dekel
Transactions in Machine Learning Research, TMLR, 2023.

Feature Learning in Deep Classifiers Through Intermediate Neural Collapse
A. Rangamani, M. Lindegaard, T. Galanti, T. Poggio
International Conference on Machine Learning, ICML, 2023.

Dynamics of Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, Neural Collapse and Generalization Bounds
M. Xu, A. Rangamani, Q. Liao, T. Galanti, T. Poggio
Research (a Science partner journal), 2023.

Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
C. Xu, S. Yang, T. Galanti, B. Wu, X. Yue, B. Zhai, W. Zhan, P. Vajda, K. Keutzer, M. Tomizuka
IEEE European Conference on Computer Vision, ECCV, 2022.

Improved Generalization Bounds for Transfer Learning via Neural Collapse
T. Galanti, A. Gyorgy, M. Hutter
ICML Workshop on Pretraining: Perspectives, Pitfalls and Paths Forward, 2022.

On the Implicit Bias Towards Depth Minimization in Deep Neural Networks
T. Galanti, L. Galanti, I. Ben-Shaul
Conference on the Mathematical Theory of Deep Neural Networks, DEEPMATH, 2022.
Workshop on the Theory of Overparameterized Machine Learning, TOPML, 2022.

On the Role of Neural Collapse in Transfer Learning
T. Galanti, A. Gyorgy, M. Hutter
International Conference on Learning Representations, ICLR, 2022.

Weakly Supervised Discovery of Semantic Attributes
A. A. Ali, T. Galanti, E. Zheltonozhskii, C. Baskin, L. Wolf
Causal Learning and Reasoning, CLeaR, 2022.

Meta Internal Learning
R. Ben Sadoun, S. Gur, T. Galanti, L. Wolf
Neural Information Processing Systems, NeurIPS, 2021.

On Random Kernels of Residual Architectures
E. Littwin*, T. Galanti*, L. Wolf
Uncertainty in Artificial Intelligence, UAI, 2021.

Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs
T. Galanti, S. Benaim, L. Wolf
Journal of Machine Learning Research, JMLR, 2021.

Evaluation Metrics for Conditional Image Generation
Y. Benny, T. Galanti, S. Benaim, L. Wolf
International Journal of Computer Vision, IJCV, 2021.

On the Modularity of Hypernetworks 
T. Galanti, L. Wolf
Neural Information Processing Systems, NeurIPS, 2020 (oral presentation - 1% acceptance).

On Infinite-Width Hypernetworks
E. Littwin*, T. Galanti*, L. Wolf
Neural Information Processing Systems, NeurIPS, 2020.

Domain Intersection and Domain Difference
S. Benaim, M. Khaitov, T. Galanti, L. Wolf
IEEE International Conference on Computer Vision, ICCV, 2019.

Unsuperivsed Learning of the Set of Local Maxima
L. Wolf, S. Benaim, T. Galanti
International Conference on Learning Representations, ICLR, 2019.

Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
O. Press, T. Galanti, S. Benaim, L. Wolf
International Conference on Learning Representations, ICLR, 2019.

A Formal Approach to Explainability
L. Wolf, T. Galanti, T. Hazan
Artificial Intelligence, Ethics and Society, AIES, 2019.

Generalization Bounds for Cross-Domain Mapping with WGANs
T. Galanti, S. Benaim, L. Wolf
NeurIPS Workshop on Integration of Deep Learning Theories, 2018.

Estimating the Success of Unsupervised Image to Image Translation
S. Benaim*, T. Galanti*, L. Wolf
IEEE European Conference on Computer Vision, ECCV, 2018.

The Role of Minimal Complexity in Unsupervised Learning of Semantic Mappings
T. Galanti, L. Wolf, S. Benaim
International Conference on Learning Representations, ICLR, 2018.

A Theoretical Framework for Deep Transfer Learning
T. Galanti, T. Hazan, L. Wolf
Information and Inference: A Journal of the IMA, IMAIAI, 2016.

Preprints

The Probabilistic Stability of Stochastic Gradient Descent
Z. Liu, B. Li, T. Galanti, M. Ueda
Arxiv preprint, 2023.

Generalization Bounds for Transfer Learning with Pretrained Classifiers
T. Galanti, A. Gyorgy, M. Hutter
Arxiv preprint, 2023.

Characterizing the Rank Minimization Bias of Regularized Stochastic Gradient Descent
T. Galanti, Z. Siegel, A. Gupte, T. Poggio
Arxiv preprint, 2023.