Ezekiel Williams

Dynamics and Representation Structure of Local Approximations to Gradient-Based Learning in Linear Recurrent Neural Networks

Biological and neuromorphic recurrent neural networks (RNNs) are subject to spatial and temporal locality constraints on the information tha… (voir plus)t can plausibly be used during learning. A common strategy to satisfy these constraints is to modify gradient descent by neglecting non-local terms to varying degrees, as in random feedback local online (RFLO) learning and truncated backpropagation through time (tBPTT). However, the learning dynamics of these algorithms, and how they compare with BPTT, remain poorly understood. We apply dynamical systems theory to data-aligned linear RNNs -- whose dynamics can be separated into orthogonal modes -- to compare stationary solutions, stability properties, and convergence rates, finding qualitatively distinct behaviour for RFLO versus BPTT and one-step tBPTT. We further observe that the solutions learned by RFLO are restricted to low-rank perturbations of initial parameters, a result which holds beyond the data-aligned setting. Our work provides analytical insight into how locality constraints shape learning dynamics, with implications for neuroscientific models of learning and alternative optimization approaches for RNNs.

2026-05-28

arXiv (prépublication)

doi.org

arxiv.org

Position: Irresponsible AI: big tech’s influence on AI research and associated impacts

Alex Hernández-García

Alexandra Volokhova

Ezekiel Williams

Dounia Kabakibo

Mélisande Teng

The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big t… (voir plus)ech in the AI field. This trend has been accompanied by growing ethical concerns and intensified societal and environmental impacts. This position paper argues that irresponsible AI development is strongly driven by big tech's influence and involvement in the field. We develop this argument by laying out the factors through which this influence leads to irresponsible AI. First, we examine the growing and disproportionate influence of big tech in AI research and argue that its drive for scaling and general-purpose systems is fundamentally at odds with the responsible, ethical, and sustainable development of AI. Second, we review key current environmental and societal negative impacts of AI and trace their connections to big tech's influence. Third, we discuss the underlying economic forces driving big tech's actions. Finally, as a call to action, we highlight the need for AI researchers to counter big tech's influence, and review and propose strategies that build on the responsibility of implicated actors and collective action.

2025-12-31

International Conference on Machine Learning (Accept (spotlight))

openreview.net

Expressivity of Neural Networks with Random Weights and Learned Biases

Ezekiel Williams

Avery Hee-Woon Ryoo

Thomas Jiralerspong

Alexandre Payeur

Matthew G Perich

Luca Mazzucato

Guillaume Lajoie

Landmark universal function approximation results for neural networks with trained weights and biases provided the impetus for the ubiquitou… (voir plus)s use of neural networks as learning models in neuroscience and Artificial Intelligence (AI). Recent work has extended these results to networks in which a smaller subset of weights (e.g., output weights) are tuned, leaving other parameters random. However, it remains an open question whether universal approximation holds when only biases are learned, despite evidence from neuroscience and AI that biases significantly shape neural responses. The current paper answers this question. We provide theoretical and numerical evidence demonstrating that feedforward neural networks with fixed random weights can approximate any continuous function on compact sets. We further show an analogous result for the approximation of dynamical systems with recurrent neural networks. Our findings are relevant to neuroscience, where they demonstrate the potential for behaviourally relevant changes in dynamics without modifying synaptic weights, as well as for AI, where they shed light on recent fine-tuning methods for large language models, like bias and prefix-based approaches.

2025-01-21

ICLR.cc/2025/Conference (poster)

doi.org

openreview.net

Expressivity of Neural Networks with Fixed Weights and Learned Biases

Ezekiel Williams

Avery Hee-Woon Ryoo

Thomas Jiralerspong

Alexandre Payeur

Matthew G Perich

Luca Mazzucato

Guillaume Lajoie

2024-06-15

ICML.cc/2024/Workshop/HiLD (poster)

openreview.net

Flexible Phase Dynamics for Bio-Plausible Contrastive Learning

Ezekiel Williams

Colin Bredenberg

Guillaume Lajoie

Many learning algorithms used as normative models in neuroscience or as candidate approaches for learning on neuromorphic chips learn by con… (voir plus)trasting one set of network states with another. These Contrastive Learning (CL) algorithms are traditionally implemented with rigid, temporally non-local, and periodic learning dynamics that could limit the range of physical systems capable of harnessing CL. In this study, we build on recent work exploring how CL might be implemented by biological or neurmorphic systems and show that this form of learning can be made temporally local, and can still function even if many of the dynamical requirements of standard training procedures are relaxed. Thanks to a set of general theorems corroborated by numerical experiments across several CL models, our results provide theoretical foundations for the study and development of CL methods for biological and neuromorphic neural networks.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

doi.org

proceedings.mlr.press

Formalizing locality for normative synaptic plasticity models

Cristina Savin

2022-12-31

Advances in Neural Information Processing Systems (publié)