Spherical embedding of inlier silhouette dissimilarities. - Etai Littwin; Hadar Averbuch-Elor; Daniel Cohen-Or (CVPR, 2015)

The Multiverse Loss for Robust Transfer Learning. - Etai Littwin; Lior Wolf (CVPR, 2016)

Complexity of multiverse networks and their multilayer generalization. - Etai Littwin; Lior Wolf (ICPR, 2016)

The Loss Surface of Residual Networks: Ensembles and the Role of Batch Normalization. - Etai Littwin; Lior Wolf (CoRR, 2016)

Regularizing by the Variance of the Activations' Sample-Variances. - Etai Littwin; Lior Wolf (NeurIPS, 2018)

On Infinite-Width Hypernetworks. - Etai Littwin; Tomer Galanti; Lior Wolf; Greg Yang (NeurIPS, 2020)

Collegial Ensembles. - Etai Littwin; Ben Myara; Sima Sabah; Joshua M. Susskind; Shuangfei Zhai; Oren Golan (NeurIPS, 2020)

On the Convex Behavior of Deep Neural Networks in Relation to the Layers' Width. - Etai Littwin; Lior Wolf (CoRR, 2020)

Residual Tangent Kernels. - Etai Littwin; Lior Wolf (CoRR, 2020)

On the Optimization Dynamics of Wide Hypernetworks. - Etai Littwin; Tomer Galanti; Lior Wolf (CoRR, 2020)

Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics. - Greg Yang; Etai Littwin (ICML, 2021)

On random kernels of residual architectures. - Etai Littwin; Tomer Galanti; Lior Wolf (UAI, 2021)

Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks. - Etai Littwin; Omid Saremi; Shuangfei Zhai; Vimal Thilak; Hanlin Goh; Joshua M. Susskind; Greg Yang (CoRR, 2021)

Implicit Greedy Rank Learning in Autoencoders via Overparameterized Linear Networks. - Shih-Yu Sun; Vimal Thilak; Etai Littwin; Omid Saremi; Joshua M. Susskind (CoRR, 2021)

Learning Representation from Neural Fisher Kernel with Low-rank Approximation. - Ruixiang Zhang; Shuangfei Zhai; Etai Littwin; Joshua M. Susskind (ICLR, 2022)

The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon. - Vimal Thilak; Etai Littwin; Shuangfei Zhai; Omid Saremi; Roni Paiss; Joshua M. Susskind (CoRR, 2022)

Adaptive Optimization in the ∞-Width Limit. - Etai Littwin; Greg Yang (ICLR, 2023)

Stabilizing Transformer Training by Preventing Attention Entropy Collapse. - Shuangfei Zhai; Tatiana Likhomanenko; Etai Littwin; Dan Busbridge; Jason Ramapuram; Yizhe Zhang 002; Jiatao Gu; Joshua M. Susskind (ICML, 2023)

Transformers learn through gradual rank increase. - Emmanuel Abbe; Samy Bengio; Enric Boix-Adserà; Etai Littwin; Joshua M. Susskind (NeurIPS, 2023)

The NTK approximation is valid for longer than you think. - Enric Boix-Adserà; Etai Littwin (CoRR, 2023)

Tensor Programs IVb: Adaptive Optimization in the Infinite-Width Limit. - Greg Yang; Etai Littwin (CoRR, 2023)

Adaptivity and Modularity for Efficient Generalization Over Task Complexity. - Samira Abnar; Omid Saremi; Laurent Dinh; Shantel Wilson; Miguel Ángel Bautista 001; Chen Huang 001; Vimal Thilak; Etai Littwin; Jiatao Gu; Josh M. Susskind; Samy Bengio (CoRR, 2023)

Tight conditions for when the NTK approximation is valid. - Enric Boix-Adserà; Etai Littwin (Trans. Mach. Learn. Res., 2023)

When can transformers reason with abstract symbols? - Enric Boix-Adserà; Omid Saremi; Emmanuel Abbe; Samy Bengio; Etai Littwin; Joshua M. Susskind (ICLR, 2024)

Vanishing Gradients in Reinforcement Finetuning of Language Models. - Noam Razin; Hattie Zhou; Omid Saremi; Vimal Thilak; Arwen Bradley; Preetum Nakkiran; Joshua M. Susskind; Etai Littwin (ICLR, 2024)

LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures. - Vimal Thilak; Chen Huang 001; Omid Saremi; Laurent Dinh; Hanlin Goh; Preetum Nakkiran; Joshua M. Susskind; Etai Littwin (ICLR, 2024)

What Algorithms can Transformers Learn? A Study in Length Generalization. - Hattie Zhou; Arwen Bradley; Etai Littwin; Noam Razin; Omid Saremi; Joshua M. Susskind; Samy Bengio; Preetum Nakkiran (ICLR, 2024)

How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks. - Etai Littwin; Omid Saremi; Madhu Advani; Vimal Thilak; Preetum Nakkiran; Chen Huang 001; Joshua M. Susskind (NeurIPS, 2024)

Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning. - Etai Littwin; Vimal Thilak; Anand Gopalakrishnan (CoRR, 2024)

The Slingshot Effect: A Late-Stage Optimization Anomaly in Adaptive Gradient Methods. - Vimal Thilak; Etai Littwin; Shuangfei Zhai; Omid Saremi; Roni Paiss; Joshua M. Susskind (Trans. Mach. Learn. Res., 2024)

UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity. - Yicheng Fu; Raviteja Anantha; Prabal Vashisht; Jianpeng Cheng 003; Etai Littwin (UMAP, 2025)

Distillation Scaling Laws. - Dan Busbridge; Amitis Shidani; Floris Weers; Jason Ramapuram; Etai Littwin; Russ Webb (CoRR, 2025)