In search of dispersed memories: Generative diffusion models are associative memory networks

• URL: http://arxiv.org/abs/2309.17290v2
• Date: Fri, 17 Nov 2023 17:05:44 GMT
• Title: In search of dispersed memories: Generative diffusion models are associative memory networks
• Authors: Luca Ambrogioni
• Abstract summary: Generative diffusion models are a type of generative machine learning techniques that have shown great performance in many tasks. We show that generative diffusion models can be interpreted as energy-based models and that, when trained on discrete patterns, their energy function is identical to that of modern Hopfield networks. This equivalence allows us to interpret the supervised training of diffusion models as a synaptic learning process that encodes the associative dynamics of a modern Hopfield network in the weight structure of a deep neural network.
• Score: 6.4322891559626125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncovering the mechanisms behind long-term memory is one of the most fascinating open problems in neuroscience and artificial intelligence. Artificial associative memory networks have been used to formalize important aspects of biological memory. Generative diffusion models are a type of generative machine learning techniques that have shown great performance in many tasks. Like associative memory systems, these networks define a dynamical system that converges to a set of target states. In this work we show that generative diffusion models can be interpreted as energy-based models and that, when trained on discrete patterns, their energy function is (asymptotically) identical to that of modern Hopfield networks. This equivalence allows us to interpret the supervised training of diffusion models as a synaptic learning process that encodes the associative dynamics of a modern Hopfield network in the weight structure of a deep neural network. Leveraging this connection, we formulate a generalized framework for understanding the formation of long-term memory, where creative generation and memory recall can be seen as parts of a unified continuum.

Related papers

• Dense Associative Memory Through the Lens of Random Features [48.17520168244209]
  Dense Associative Memories are high storage capacity variants of the Hopfield networks. We show that this novel network closely approximates the energy function and dynamics of conventional Dense Associative Memories.
  arXiv  Detail & Related papers  (2024-10-31T17:10:57Z)
• Sequential Learning in the Dense Associative Memory [1.2289361708127877]
  We investigate the performance of the Dense Associative Memory in sequential learning problems. We show that existing sequential learning methods can be applied to the Dense Associative Memory to improve sequential learning performance.
  arXiv  Detail & Related papers  (2024-09-24T04:23:00Z)
• Neural Message Passing Induced by Energy-Constrained Diffusion [79.9193447649011]
  We propose an energy-constrained diffusion model as a principled interpretable framework for understanding the mechanism of MPNNs. We show that the new model can yield promising performance for cases where the data structures are observed (as a graph), partially observed or completely unobserved.
  arXiv  Detail & Related papers  (2024-09-13T17:54:41Z)
• Explosive neural networks via higher-order interactions in curved statistical manifolds [43.496401697112695]
  We introduce curved neural networks as a class of prototypical models for studying higher-order phenomena. We show that these curved neural networks implement a self-regulating process that can accelerate memory retrieval.
  arXiv  Detail & Related papers  (2024-08-05T09:10:29Z)
• Demolition and Reinforcement of Memories in Spin-Glass-like Neural Networks [0.0]
  The aim of this thesis is to understand the effectiveness of Unlearning in both associative memory models and generative models. The selection of structured data enables an associative memory model to retrieve concepts as attractors of a neural dynamics with considerable basins of attraction. A novel regularization technique for Boltzmann Machines is presented, proving to outperform previously developed methods in learning hidden probability distributions from data-sets.
  arXiv  Detail & Related papers  (2024-03-04T23:12:42Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Learning Contact Dynamics using Physically Structured Neural Networks [81.73947303886753]
  We use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations. Our results indicate that an idealised form of touch feedback is a key component of making this learning problem tractable.
  arXiv  Detail & Related papers  (2021-02-22T17:33:51Z)
• Slow manifolds in recurrent networks encode working memory efficiently and robustly [0.0]
  Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time. We use a top-down modeling approach to examine network-level mechanisms of working memory.
  arXiv  Detail & Related papers  (2021-01-08T18:47:02Z)
• Reservoir Memory Machines as Neural Computers [70.5993855765376]
  Differentiable neural computers extend artificial neural networks with an explicit memory without interference. We achieve some of the computational capabilities of differentiable neural computers with a model that can be trained very efficiently.
  arXiv  Detail & Related papers  (2020-09-14T12:01:30Z)
• Large Associative Memory Problem in Neurobiology and Machine Learning [6.41804410246642]
  We present a valid model of large associative memory with a degree of biological plausibility. The dynamics of our network and its reduced dimensional equivalent both minimize energy (Lyapunov) functions.
  arXiv  Detail & Related papers  (2020-08-16T21:03:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.