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Há 6 dias · Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, Alexis Chevalier, Sanjeev Arora, Danqi Chen. Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial ...
5 de jun. de 2024 · My research interests are broadly in machine learning such as deep learning, representation learning, reinforcement learning and data selection. Prior to starting as faculty, I was a postdoc at Institute for Advanced Study of Princeton, hosted by Sanjeev Arora.
10 de jun. de 2024 · Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang. Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks. In International Conference on Machine Learning , pages 322–332.
5 de jun. de 2024 · Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. Sanjeev Arora*, Simon S. Du*, Wei Hu*, Zhiyuan Li*, Ruosong Wang*. International Conference on Machine Learning (ICML) 2019.
10 de jun. de 2024 · Sanjeev Arora, Nadav Cohen, Noah Golowich, and Wei Hu. A convergence analysis of gradient descent for deep linear neural networks. International Conference on Learning Representations , 2019.
5 de jun. de 2024 · Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. Zhiyuan Li, Tianhao Wang, Jason D. Lee, and Sanjeev Arora. NeurIPS 2022. Neural Networks can Learn Representations with Gradient Descent.
12 de jun. de 2024 · Sanjeev Arora, Nadav Cohen, Wei Hu and Yuping Luo (alphabetical order). Jun’19 (v1), Oct’19 (v2). Conference on Neural Information Processing Systems (NeurIPS) 2019, Spotlight Track (top 3%). * A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks.
