Search Results for author:
Found 6 papers, 4 papers with code
Unsupervised Learning in Complex Systems
This thesis makes the following key contributions: the development of a general complexity metric that we apply to search for complex systems that exhibit growth of complexity, the introduction of a coarse-graining method to study computations in large-scale complex systems, and the development of a metric for learning efficiency as well as a benchmark dataset for evaluating the speed of learning algorithms.
Preserving Semantics in Textual Adversarial Attacks
Our method outperforms existing sentence encoders used in adversarial attacks by achieving 1. 2x - 5. 1x better real attack success rate.
Benchmarking Learning Efficiency in Deep Reservoir Computing
In this paper, we introduce a benchmark of increasingly difficult tasks together with a data efficiency metric to measure how quickly machine learning models learn from training data.
Visualizing computation in large-scale cellular automata
Emergent processes in complex systems such as cellular automata can perform computations of increasing complexity, and could possibly lead to artificial evolution.
Evolving Structures in Complex Systems
In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata.
Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials
Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients.
