Search Results for author:
Found 16 papers, 7 papers with code
Language Model Crossover: Variation through Few-Shot Prompting
The promise of such language model crossover (which is simple to implement and can leverage many different open-source language models) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models.
Evolving Deep Neural Networks
The success of deep learning depends on finding an architecture to fit the task.
Evolutionary Neural AutoML for Deep Learning
However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters.
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
Using this data, it is possible to learn a surrogate model, and with that model, evolve a decision strategy that optimizes the outcomes.
From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school and business closures.
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
In many such tasks, the point prediction is not enough: the uncertainty (i. e. risk or confidence) of that prediction must also be estimated.
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back
Deep multitask learning boosts performance by sharing learned structure across related tasks.
Evolutionary Architecture Search For Deep Multitask Networks
Multitask learning, i. e. learning several tasks at once with the same neural network, can improve performance in each of the tasks.
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering.
Discovering Evolutionary Stepping Stones through Behavior Domination
The conclusion is that behavior domination can help illuminate the complex dynamics of behavior-driven search, and can thus lead to the design of more scalable and robust algorithms.
Reuse of Neural Modules for General Video Game Playing
A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain.
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
Deep multitask learning boosts performance by sharing learned structure across related tasks.
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks?
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others.
Simple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings)
The conclusion is that, across evolutionary computation areas as diverse as genetic programming, neuroevolution, genetic algorithms, and theory, expressive encodings can be a key to understanding and realizing the full power of evolution.
Discovering Effective Policies for Land-Use Planning with Neuroevolution
How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate change.
