34 citations · 72 across the 14 of their papers we have counts for
14 papers
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
Yi Ren, Shangmin Guo, Linlu Qiu +2
With the widespread adoption of Large Language Models (LLMs), the prevalence of iterative interactions among these models is anticipated to increase. Notably, recent advancements i…
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design
Samuel Garcin, James Doran, Shangmin Guo +2
Autonomous agents trained using deep reinforcement learning (RL) often lack the ability to successfully generalise to new environments, even when these environments share character…
Economics Arena for Large Language Models
Shangmin Guo, Haoran Bu, Haochuan Wang +4
Large language models (LLMs) have been extensively used as the backbones for general-purpose agents, and some economics literature suggest that LLMs are capable of playing various…
lpNTK: Better Generalisation with Less Data via Sample Interaction During Learning
Shangmin Guo, Yi Ren, Stefano V. Albrecht +1
Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed…
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning
Samuel Garcin, James Doran, Shangmin Guo +2
A key limitation preventing the wider adoption of autonomous agents trained via deep reinforcement learning (RL) is their limited ability to generalise to new environments, even wh…
How to prepare your task head for finetuning
Yi Ren, Shangmin Guo, Wonho Bae +1
In deep learning, transferring information from a pretrained network to a downstream task by finetuning has many benefits. The choice of task head plays an important role in fine-t…
Deep Reinforcement Learning for Multi-Agent Interaction
Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho +14
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Tow…
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
Runfa Chen, Yu Rong, Shangmin Guo +4
After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation. Unfortunately,…
Better Supervisory Signals by Observing Learning Paths
Yi Ren, Shangmin Guo, Danica J. Sutherland
Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing…
Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability
Shangmin Guo, Yi Ren, Kory Mathewson +3
Researchers are using deep learning models to explore the emergence of language in various language games, where agents interact and develop an emergent language to solve tasks. We…
Inductive Bias and Language Expressivity in Emergent Communication
Shangmin Guo, Yi Ren, Agnieszka Słowik +1
Referential games and reconstruction games are the most common game types for studying emergent languages. We investigate how the type of the language game affects the emergent lan…
Compositional Languages Emerge in a Neural Iterated Learning Model
Yi Ren, Shangmin Guo, Matthieu Labeau +2
The principle of compositionality, which enables natural language to represent complex concepts via a structured combination of simpler ones, allows us to convey an open-ended set…
Emergence of Numeric Concepts in Multi-Agent Autonomous Communication
Shangmin Guo
With the rapid development of deep learning, most of current state-of-the-art techniques in natural langauge processing are based on deep learning models trained with argescaled st…
The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents
Shangmin Guo, Yi Ren, Serhii Havrylov +3
Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, researc…