44 citations · 63 across the 5 of their papers we have counts for
5 papers
TLDChoiceNet: Quantitatively Choosing a Transfer Learning Dataset
Jing Ning, James D. Braza
Transfer learning is particularly useful in settings with limited training data, and within image classification it is common to transfer learn upon massive datasets like ImageNet…
LABBench2: An Improved Benchmark for AI Systems Performing Biology Research
Jon M Laurent, Albert Bou, Michael Pieler +9
Optimism for accelerating scientific discovery with AI continues to grow. Current applications of AI in scientific research range from training dedicated foundation models on scien…
Training a Scientific Reasoning Model for Chemistry
Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths +6
Reasoning models are large language models that emit a long chain-of-thought before answering, providing both higher accuracy and explicit reasoning for their response. A major que…
Aviary: training language agents on challenging scientific tasks
Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths +8
Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and exper…
Language agents achieve superhuman synthesis of scientific knowledge
Michael D. Skarlinski, Sam Cox, Jon M. Laurent +6
Language models are known to hallucinate incorrect information, and it is unclear if they are sufficiently accurate and reliable for use in scientific research. We developed a rigo…