16 citations · 23 across the 4 of their papers we have counts for
4 papers
Generalisable Agents for Neural Network Optimisation
Kale-ab Tessera, Callum Rhys Tilbury, Sasha Abramowitz +5
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long training times. To address this difficulty, we pro…
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
Andries Smit, Paul Duckworth, Nathan Grinsztajn +2
Recent advancements in large language models (LLMs) underscore their potential for responding to inquiries in various domains. However, ensuring that generative agents provide accu…
Mava: a research library for distributed multi-agent reinforcement learning in JAX
Ruan de Kock, Omayma Mahjoub, Sasha Abramowitz +5
Multi-agent reinforcement learning (MARL) research is inherently computationally expensive and it is often difficult to obtain a sufficient number of experiment samples to test hyp…
Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Kale-ab Tessera, Sara Hooker, Benjamin Rosman
Training sparse networks to converge to the same performance as dense neural architectures has proven to be elusive. Recent work suggests that initialization is the key. However, w…