activity
20222026
most citedDistributed Evolution Strategies Using TPUs for Meta-Learning

4 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Retrieval, Refinement, and Ranking for Text-to-Video Generation via Prompt Optimization and Test-Time Scaling

Zillur Rahman, Alex Sheng, Cristian Meo

While large-scale datasets have driven significant progress in Text-to-Video (T2V) generative models, these models remain highly sensitive to input prompts, demonstrating that prom…

cs.AI2024★ 2 cited

From Language Models to Practical Self-Improving Computer Agents

Alex Sheng

We develop a simple and straightforward methodology to create AI computer agents that can carry out diverse computer tasks and self-improve by developing tools and augmentations to…

cs.CL2022

Task Transfer and Domain Adaptation for Zero-Shot Question Answering

Xiang Pan, Alex Sheng, David Shimshoni +3

Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning method…

cs.AI2022★ 4 cited

Self-Programming Artificial Intelligence Using Code-Generating Language Models

Alex Sheng, Shankar Padmanabhan

Recent progress in large-scale language models has enabled breakthroughs in previously intractable computer programming tasks. Prior work in meta-learning and neural architecture s…

cs.NE2022★ 4 cited

Distributed Evolution Strategies Using TPUs for Meta-Learning

Alex Sheng, Derek He

Meta-learning traditionally relies on backpropagation through entire tasks to iteratively improve a model's learning dynamics. However, this approach is computationally intractable…