no code implementations • 9 Jan 2019 • Maxwell Crouse, Achille Fokoue, Maria Chang, Pavan Kapanipathi, Ryan Musa, Constantine Nakos, Lingfei Wu, Kenneth Forbus, Michael Witbrock
Machine learning systems regularly deal with structured data in real-world applications.
BIG-bench Machine Learning Vocal Bursts Intensity Prediction
no code implementations • 2 Feb 2020 • Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse, Achille Fokoue
Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning.
no code implementations • 7 Jun 2021 • Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni, Vernon Austil, Cristina Cornelio, Shajith Ikbal, Pavan Kapanipathi, Ndivhuwo Makondo, Kavitha Srinivas, Michael Witbrock, Achille Fokoue
In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).
no code implementations • 7 May 2023 • Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury, Tahira Naseem, Ramon Astudillo, Achille Fokoue, Tim Klinger
Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion.
no code implementations • 31 May 2023 • Maxwell Crouse, Ramon Astudillo, Tahira Naseem, Subhajit Chaudhury, Pavan Kapanipathi, Salim Roukos, Alexander Gray
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser.
no code implementations • 18 Jun 2023 • Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan, Subhajit Chaudhury, Chulaka Gunasekara, Maxwell Crouse, Diwakar Mahajan, Ibrahim Abdelaziz, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Alexander Gray
In this work, we propose a new evaluation scheme to model human judgments in 7 NLP tasks, based on the fine-grained mismatches between a pair of texts.
no code implementations • 12 Oct 2023 • Maxwell Crouse, Ibrahim Abdelaziz, Ramon Astudillo, Kinjal Basu, Soham Dan, Sadhana Kumaravel, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Luis Lastras
We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e. g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent.
no code implementations • 23 Feb 2024 • Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis A. Lastras
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.
1 code implementation • arXiv 2020 • Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus, Achille Fokoue
Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.
Ranked #1 on Automated Theorem Proving on HolStep (Conditional)
1 code implementation • 7 Apr 2020 • Maxwell Crouse, Constantine Nakos, Ibrahim Abdelaziz, Kenneth Forbus
Analogy is core to human cognition.
1 code implementation • 28 Sep 2023 • Tim Klinger, Luke Liu, Soham Dan, Maxwell Crouse, Parikshit Ram, Alexander Gray
Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples.
1 code implementation • 5 Nov 2019 • Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni, Spencer Whitehead, Cristina Cornelio, Pavan Kapanipathi, Kavitha Srinivas, Veronika Thost, Michael Witbrock, Achille Fokoue
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
1 code implementation • 15 May 2023 • Achille Fokoue, Ibrahim Abdelaziz, Maxwell Crouse, Shajith Ikbal, Akihiro Kishimoto, Guilherme Lima, Ndivhuwo Makondo, Radu Marinescu
NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving.