133 citations · 279 across the 13 of their papers we have counts for
13 papers
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training
Kavita Selva, Satita Vittayaareekul, Brando Miranda
Currently, data and model size dominate the narrative in the training of super-large, powerful models. However, there has been a lack of exploration on the effect of other attribut…
Is Pre-training Truly Better Than Meta-Learning?
Brando Miranda, Patrick Yu, Saumya Goyal +2
In the context of few-shot learning, it is currently believed that a fixed pre-trained (PT) model, along with fine-tuning the final layer during evaluation, outperforms standard me…
Are Emergent Abilities of Large Language Models a Mirage?
Rylan Schaeffer, Brando Miranda, Sanmi Koyejo
Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent…
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 +1
Despite a growing body of work at the intersection of deep learning and formal languages, there has been relatively little systematic exploration of transformer models for reasonin…
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 +1
Recently, it has been observed that a transfer learning solution might be all we need to solve many few-shot learning benchmarks -- thus raising important questions about when and…
Does MAML Only Work via Feature Re-use? A Data Centric Perspective
Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo
Recent work has suggested that a good embedding is all we need to solve many few-shot learning benchmarks. Furthermore, other work has strongly suggested that Model Agnostic Meta-L…