Brando Miranda

According to our database, Brando Miranda authored at least 16 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

When Do Universal Image Jailbreaks Transfer Between Vision-Language Models?

Rylan Schaeffer, Dan Valentine, Luke Bailey, James Chua, Cristóbal Eyzaguirre, Zane Durante, Joe Benton, Brando Miranda, Henry Sleight, John Hughes, Rajashree Agrawal, Mrinank Sharma, Scott Emmons, Sanmi Koyejo, Ethan Perez

CoRR, 2024

Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Rylan Schaeffer, Hailey Schoelkopf, Brando Miranda, Gabriel Mukobi, Varun Madan, Adam Ibrahim, Herbie Bradley, Stella Biderman, Sanmi Koyejo

CoRR, 2024

An Evaluation Benchmark for Autoformalization in Lean4.

Jasdeep Sidhu, Shubhra Mishra, Aryan Gulati, Devanshu Ladsaria, Brando Miranda

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

A Systematic Study of the Role of Data Quality and Alignment for Fine-tuning LLMs for Enhanced Autoformalization.

Krrish Chawla, Mario DePavia, Aryan Sahai, Brando Miranda

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

2023

Is Pre-training Truly Better Than Meta-Learning?

Brando Miranda, Patrick Yu, Saumya Goyal, Yu-Xiong Wang, Sanmi Koyejo

CoRR, 2023

Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data.

Alycia Lee, Brando Miranda, Sanmi Koyejo

CoRR, 2023

Transformer Models for Type Inference in the Simply Typed Lambda Calculus: A Case Study in Deep Learning for Code.

Brando Miranda, Avi Shinnar, Vasily Pestun, Barry M. Trager

CoRR, 2023

Are Emergent Abilities of Large Language Models a Mirage?

Rylan Schaeffer, Brando Miranda, Sanmi Koyejo

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence.

Brando Miranda, Patrick Yu, Yu-Xiong Wang, Sanmi Koyejo

CoRR, 2022

2021

Does MAML Only Work via Feature Re-use? A Data Centric Perspective.

Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo

CoRR, 2021

2019

Theory III: Dynamics and Generalization in Deep Networks.

Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso A. Poggio

CoRR, 2019

2018

A Surprising Linear Relationship Predicts Test Performance in Deep Networks.

Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso A. Poggio

CoRR, 2018

Theory IIIb: Generalization in Deep Networks.

Tomaso A. Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary

CoRR, 2018

Theory of Deep Learning IIb: Optimization Properties of SGD.

Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio

CoRR, 2018

Theory of Deep Learning III: explaining the non-overfitting puzzle.

Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar

CoRR, 2018

2017

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review.

Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao

Int. J. Autom. Comput., 2017