| A Gradient Flow Framework For Analyzing Network Pruning ES Lubana, RP Dick International Conference on Learning Representations (ICLR) (spotlight), 2021 | 49 | 2021 |
| Augmentations in graph contrastive learning: Current methodological flaws & towards better practices P Trivedi, ES Lubana, Y Yan, Y Yang, D Koutra Proceedings of the ACM Web Conference 2022, 1538-1549, 2022 | 48 | 2022 |
| Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering ES Lubana, CI Tang, F Kawsar, RP Dick, A Mathur International Conference on Machine Learning (ICML) (spotlight), 2022 | 45 | 2022 |
| Beyond BatchNorm: towards a unified understanding of normalization in deep learning ES Lubana, R Dick, H Tanaka Advances in Neural Information Processing Systems (NeurIPS), 2021 | 43 | 2021 |
| Mechanistic Mode Connectivity ES Lubana, EJ Bigelow, RP Dick, D Krueger, H Tanaka International Conference on Machine Learning (ICML), 2023 | 42* | 2023 |
| Foundational Challenges in Assuring Alignment and Safety of Large Language Models U Anwar, A Saparov*, J Rando*, D Paleka*, M Turpin*, P Hase*, ... arXiv preprint arXiv:2404.09932, 2024 | 35 | 2024 |
| Minimalistic image signal processing for deep learning applications ES Lubana, RP Dick, V Aggarwal, PM Pradhan 2019 IEEE International Conference on Image Processing (ICIP), 4165-4169, 2019 | 32* | 2019 |
| Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks S Jain*, R Kirk*, ES Lubana*, RP Dick, H Tanaka, E Grefenstette, ... International Conference on Learning Representations (ICLR), 2024 | 26 | 2024 |
| What shapes the loss landscape of self-supervised learning? L Ziyin, ES Lubana, M Ueda, H Tanaka International Conference on Learning Representations (ICLR), 2023 | 20 | 2023 |
| Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task M Okawa*, ES Lubana*, RP Dick, H Tanaka* Advances in Neural Information Processing Systems (NeurIPS), 2023 | 18 | 2023 |
| Analyzing data-centric properties for graph contrastive learning P Trivedi, ES Lubana, M Heimann, D Koutra, J Thiagarajan Advances in Neural Information Processing Systems (NeurIPS), 2022 | 17* | 2022 |
| How do quadratic regularizers prevent catastrophic forgetting: The role of interpolation ES Lubana, P Trivedi, D Koutra, R Dick Conference on Lifelong Learning Agents (CoLLAs), 2021 | 14* | 2021 |
| How capable can a transformer become? a study on synthetic, interpretable tasks R Ramesh, M Khona, RP Dick, H Tanaka, ES Lubana International Conference on Machine Learning (ICML), 2023 | 7* | 2023 |
| Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space CF Park, M Okawa, A Lee, ES Lubana, H Tanaka arXiv preprint arXiv:2406.19370, 2024 | 1 | 2024 |
| In-Context Learning Dynamics with Random Binary Sequences EJ Bigelow, ES Lubana, RP Dick, H Tanaka, TD Ullman International Conference on Learning Representations (ICLR), 2024 | 1 | 2024 |
| FoMo rewards: Can we cast foundation models as reward functions? ES Lubana, J Brehmer, P De Haan, T Cohen NeurIPS workshop on Foundation Models for Decision Making, 2023 | 1 | 2023 |
| What Makes and Breaks Safety Fine-tuning? A Mechanistic Study S Jain, ES Lubana, K Oksuz, T Joy, P Torr, A Sanyal, PK Dokania ICML 2024 Workshop on Mechanistic Interpretability (spotlight), 2024 | | 2024 |
| The Concept Percolation Hypothesis: Analyzing the Emergence of Capabilities in Neural Networks Trained on Formal Grammars ES Lubana, K Kawaguchi, RP Dick, H Tanaka ICML 2024 Workshop on Mechanistic Interpretability, 0 | | |
| How Do Transformers Fill in the Blanks? A Case Study on Matrix Completion P Gopalani, ES Lubana, W Hu ICML 2024 Workshop on Mechanistic Interpretability, 0 | | |
Robert DickUniversity of Michigan, Stryd在 rpdmail.dyndns.org 的电子邮件经过验证
