Search Results for author:
Found 13 papers, 7 papers with code
Making New Connections: LLMs as Puzzle Generators for The New York Times' Connections Word Game
Our findings show that LLMs are capable puzzle creators, and can generate diverse sets of enjoyable, challenging, and creative Connections puzzles as judged by human users.
From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models
Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved.
Generative Design through Quality-Diversity Data Synthesis and Language Models
We then fine-tune a language model with this dataset to generate high-level designs.
Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs
Biological nervous systems are created in a fundamentally different way than current artificial neural networks.
MarioGPT: Open-Ended Text2Level Generation through Large Language Models
MarioGPT can not only generate diverse levels, but can be text-prompted for controllable level generation, addressing one of the key challenges of current PCG techniques.
Skill Decision Transformer
However many of these methods only optimize for high returns, and may not extract much information from a diverse dataset of trajectories.
Severe Damage Recovery in Evolving Soft Robots through Differentiable Programming
Biological systems are very robust to morphological damage, but artificial systems (robots) are currently not.
Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems
Inspired by cellular growth and self-organization, Neural Cellular Automata (NCAs) have been capable of "growing" artificial cells into images, 3D structures, and even functional machines.
HyperNCA: Growing Developmental Networks with Neural Cellular Automata
In contrast to deep reinforcement learning agents, biological neural networks are grown through a self-organized developmental process.
Variational Neural Cellular Automata
Additionally, we show that the VNCA can learn a distribution of stable attractors that can recover from significant damage.
MuSLCAT: Multi-Scale Multi-Level Convolutional Attention Transformer for Discriminative Music Modeling on Raw Waveforms
MuSLCAT's backend is a modified version of BERT.
Growing 3D Artefacts and Functional Machines with Neural Cellular Automata
Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells.
Modeling natural language emergence with integral transform theory and reinforcement learning
Zipf's law predicts a power-law relationship between word rank and frequency in language communication systems and has been widely reported in a variety of natural language processing applications.
