Search Results for author:
Found 6 papers, 5 papers with code
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities.
Ranked #1 on
Multi-task Language Understanding
on BBH-alg
Zero-Shot Text-to-Image Generation
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Ranked #40 on
Text-to-Image Generation
on COCO
(using extra training data)
Large-$c$ conformal $(n \leq 6)$-point blocks with superlight weights and holographic Steiner trees
In this note we study CFT$_2$ Virasoro conformal blocks with heavy operators in the large-$c$ limit in the context of AdS$_3$/CFT$_2$ correspondence.
High Energy Physics - Theory
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
Run, skeleton, run: skeletal model in a physics-based simulation
In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible.
Deep Attention Recurrent Q-Network
A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels.
Ranked #24 on
Atari Games
on Atari 2600 Tutankham
