53 citations · 59 across the 6 of their papers we have counts for
6 papers
Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning
Kavi Gupta, Kate Sanders, Armando Solar-Lezama
While LLMs have revolutionized the field of machine learning due to their high performance on a strikingly wide range of problems, they are also known to hallucinate false answers…
Learning a Hierarchical Planner from Humans in Multiple Generations
Leonardo Hernandez Cano, Yewen Pu, Robert D. Hawkins +2
A typical way in which a machine acquires knowledge from humans is by programming. Compared to learning from demonstrations or experiences, programmatic learning allows the machine…
LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers
Theo X. Olausson, Alex Gu, Benjamin Lipkin +4
Logical reasoning, i.e., deductively inferring the truth value of a conclusion from a set of premises, is an important task for artificial intelligence with wide potential impacts…
SPARLING: Learning Latent Representations with Extremely Sparse Activations
Kavi Gupta, Osbert Bastani, Armando Solar-Lezama
Real-world processes often contain intermediate state that can be modeled as an extremely sparse activation tensor. In this work, we analyze the identifiability of such sparse and…
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Karl D. D. Willis, Pradeep Kumar Jayaraman, Hang Chu +8
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software. CAD designers build up these assemblies by alignin…
Safe Human-Interactive Control via Shielding
Jeevana Priya Inala, Yecheng Jason Ma, Osbert Bastani +2
Ensuring safety for human-interactive robotics is important due to the potential for human injury. The key challenge is defining safety in a way that accounts for the complex range…