6 citations · 6 across the 1 of their papers we have counts for
5 papers
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…
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco +2
The paper characterizes classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these cond…