1 citations · 2 across the 6 of their papers we have counts for
6 papers
Hierarchical Solomonoff Induction: An Unbounded Machine Learning Model
Nathan Young
Solomonoff Induction, or SolInd, provides an ideal unbounded model of a priori sequence prediction but cannot naturally describe extrapolation from a given training dataset, as per…
RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities
Tarek Elsayed, Shiping Yang, Eunsong Koh +13
LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable snippets, focus on binary cla…
Transformers As Approximations of Solomonoff Induction
Nathan Young, Michael Witbrock
Solomonoff Induction is an optimal-in-the-limit unbounded algorithm for sequence prediction, representing a Bayesian mixture of every computable probability distribution and perfor…
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Qiming Bao, Alex Yuxuan Peng, Zhenyun Deng +10
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical re…
AbductionRules: Training Transformers to Explain Unexpected Inputs
Nathan Young, Qiming Bao, Joshua Bensemann +1
Transformers have recently been shown to be capable of reliably performing logical reasoning over facts and rules expressed in natural language, but abductive reasoning - inference…
Decentralized Control Systems Laboratory Using Human Centered Robotic Actuators
Binghan He, Kunye Chen, Rachel Schlossman +5
University laboratories deliver unique hands-on experimentation for STEM students but often lack state-of-the-art equipment and provide limited access to their equipment. The Unive…