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Found 11 papers, 1 papers with code
Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop
Therefore, a self-consuming training loop emerges in which new LLM generations are trained on the output from the previous generations.
Do You Trust ChatGPT? -- Perceived Credibility of Human and AI-Generated Content
This paper examines how individuals perceive the credibility of content originating from human authors versus content generated by large language models, like the GPT language model family that powers ChatGPT, in different user interface versions.
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems
However, the influence of subsampling on the solution quality of real-world symbolic regression problems has not yet been studied.
MTGP: Combining Metamorphic Testing and Genetic Programming
As in practice, the training cases have to be expensively hand-labeled by the user, we need an approach to check the program behavior with a lower number of training cases.
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions.
Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming
We find that the performance of the two approaches on the benchmark problems is quite similar, however, in comparison to GitHub Copilot, the program synthesis approaches based on genetic programming are not yet mature enough to support programmers in practical software development.
Recent Developments in Program Synthesis with Evolutionary Algorithms
On problems where this mapping is complex, e. g., if the problem consists of several sub-problems or requires iteration/recursion for a correct solution, results tend to be worse.
The Randomness of Input Data Spaces is an A Priori Predictor for Generalization
Over-parameterized models can perfectly learn various types of data distributions, however, generalization error is usually lower for real data in comparison to artificial data.
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial Optimization
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled.
Training a Restricted Boltzmann Machine for Classification by Labeling Model Samples
We propose an alternative method for training a classification model.
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization
The results are compared to the Bayesian Optimization Algorithm, a state-of-the-art EDA.
