Brando Miranda

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 20

Top Topics

Document Length

Linear Relationship

Top Venues

Tiny Papers @ ICLR

Int. J. Autom. Comput.

Publications

Rylan Schaeffer, Hailey Schoelkopf, Brando Miranda, Gabriel Mukobi, Varun Madan, Adam Ibrahim, Herbie Bradley, Stella Biderman, Sanmi Koyejo
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive? CoRR (2024)
Krrish Chawla, Mario DePavia, Aryan Sahai, Brando Miranda
A Systematic Study of the Role of Data Quality and Alignment for Fine-tuning LLMs for Enhanced Autoformalization. Tiny Papers @ ICLR (2024)
Aryan Gulati, Devanshu Ladsaria, Shubhra Mishra, Jasdeep Sidhu, Brando Miranda
An Evaluation Benchmark for Autoformalization in Lean4. CoRR (2024)
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
When Do Universal Image Jailbreaks Transfer Between Vision-Language Models? CoRR (2024)
Jasdeep Sidhu, Shubhra Mishra, Aryan Gulati, Devanshu Ladsaria, Brando Miranda
An Evaluation Benchmark for Autoformalization in Lean4. Tiny Papers @ ICLR (2024)
Alycia Lee, Brando Miranda, Sanmi Koyejo
Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data. CoRR (2023)
Brando Miranda, Avi Shinnar, Vasily Pestun, Barry M. Trager
Transformer Models for Type Inference in the Simply Typed Lambda Calculus: A Case Study in Deep Learning for Code. CoRR (2023)
Rylan Schaeffer, Brando Miranda, Sanmi Koyejo
Are Emergent Abilities of Large Language Models a Mirage? CoRR (2023)
Rylan Schaeffer, Brando Miranda, Sanmi Koyejo
Are Emergent Abilities of Large Language Models a Mirage? NeurIPS (2023)
Brando Miranda, Patrick Yu, Saumya Goyal, Yu-Xiong Wang, Sanmi Koyejo
Is Pre-training Truly Better Than Meta-Learning? CoRR (2023)
Brando Miranda, Patrick Yu, Yu-Xiong Wang, Sanmi Koyejo
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence. CoRR (2022)
Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo
Does MAML Only Work via Feature Re-use? A Data Centric Perspective. CoRR (2021)
Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence. CoRR (2021)
Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso A. Poggio
Theory III: Dynamics and Generalization in Deep Networks. CoRR (2019)
Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio
Theory of Deep Learning IIb: Optimization Properties of SGD. CoRR (2018)
Tomaso A. Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary
Theory IIIb: Generalization in Deep Networks. CoRR (2018)
Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso A. Poggio
A Surprising Linear Relationship Predicts Test Performance in Deep Networks. CoRR (2018)
Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar
Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR (2018)
Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. Int. J. Autom. Comput. 14 (5) (2017)
Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao
Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review. CoRR (2016)