1.5k citations · 1.5k across the 4 of their papers we have counts for
8 papers
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun +55
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex p…
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
Aditya Rawal, Joel Lehman, Felipe Petroski Such +2
Neural Architecture Search (NAS) explores a large space of architectural motifs -- a compute-intensive process that often involves ground-truth evaluation of each motif by instanti…
Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials
Christian F. Perez, Felipe Petroski Such, Theofanis Karaletsos
There is broad interest in creating RL agents that can solve many (related) tasks and adapt to new tasks and environments after initial training. Model-based RL leverages learned s…
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman +2
This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in orde…
Fully Convolutional Networks for Handwriting Recognition
Felipe Petroski Such, Dheeraj Peri, Frank Brockler +2
Handwritten text recognition is challenging because of the virtually infinite ways a human can write the same message. Our fully convolutional handwriting model takes in a handwrit…
Efficient transfer learning and online adaptation with latent variable models for continuous control
Christian F. Perez, Felipe Petroski Such, Theofanis Karaletsos
Traditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the r…