Modern Hopfield Networks meet Encoded Neural Representations -- Addressing Practical Considerations

• URL: http://arxiv.org/abs/2409.16408v2
• Date: Wed, 30 Oct 2024 22:35:58 GMT
• Title: Modern Hopfield Networks meet Encoded Neural Representations -- Addressing Practical Considerations
• Authors: Satyananda Kashyap, Niharika S. D'Souza, Luyao Shi, Ken C. L. Wong, Hongzhi Wang, Tanveer Syeda-Mahmood,
• Abstract summary: This paper introduces Hopfield HEN, a framework that integrates encoded representations into MHNs to improve pattern separability and reduce meta-stable states. We show that HEN can also be used for retrieval in the context of hetero association of images with natural language queries, thus removing the limitation of requiring access to partial content in the same domain.
• Score: 5.272882258282611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Content-addressable memories such as Modern Hopfield Networks (MHN) have been studied as mathematical models of auto-association and storage/retrieval in the human declarative memory, yet their practical use for large-scale content storage faces challenges. Chief among them is the occurrence of meta-stable states, particularly when handling large amounts of high dimensional content. This paper introduces Hopfield Encoding Networks (HEN), a framework that integrates encoded neural representations into MHNs to improve pattern separability and reduce meta-stable states. We show that HEN can also be used for retrieval in the context of hetero association of images with natural language queries, thus removing the limitation of requiring access to partial content in the same domain. Experimental results demonstrate substantial reduction in meta-stable states and increased storage capacity while still enabling perfect recall of a significantly larger number of inputs advancing the practical utility of associative memory networks for real-world tasks.

Related papers

• Storing overlapping associative memories on latent manifolds in low-rank spiking networks [5.041384008847852]
  We revisit the associative memory problem in light of advances in understanding spike-based computation. We show that the spiking activity for a large class of all-inhibitory networks is situated on a low-dimensional, convex, and piecewise-linear manifold. We propose several learning rules, and demonstrate a linear scaling of the storage capacity with the number of neurons, as well as robust pattern completion abilities.
  arXiv  Detail & Related papers  (2024-11-26T14:48:25Z)
• 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)
• NAC-TCN: Temporal Convolutional Networks with Causal Dilated Neighborhood Attention for Emotion Understanding [60.74434735079253]
  We propose a method known as Neighborhood Attention with Convolutions TCN (NAC-TCN) We accomplish this by introducing a causal version of Dilated Neighborhood Attention while incorporating it with convolutions. Our model achieves comparable, better, or state-of-the-art performance over TCNs, TCAN, LSTMs, and GRUs while requiring fewer parameters on standard emotion recognition datasets.
  arXiv  Detail & Related papers  (2023-12-12T18:41:30Z)
• Heterogenous Memory Augmented Neural Networks [84.29338268789684]
  We introduce a novel heterogeneous memory augmentation approach for neural networks. By introducing learnable memory tokens with attention mechanism, we can effectively boost performance without huge computational overhead. We show our approach on various image and graph-based tasks under both in-distribution (ID) and out-of-distribution (OOD) conditions.
  arXiv  Detail & Related papers  (2023-10-17T01:05:28Z)
• Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level context memory [10.210120085157161]
  This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain. Experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-08-16T12:35:57Z)
• Pin the Memory: Learning to Generalize Semantic Segmentation [68.367763672095]
  We present a novel memory-guided domain generalization method for semantic segmentation based on meta-learning framework. Our method abstracts the conceptual knowledge of semantic classes into categorical memory which is constant beyond the domains.
  arXiv  Detail & Related papers  (2022-04-07T17:34:01Z)
• MOST-Net: A Memory Oriented Style Transfer Network for Face Sketch Synthesis [41.80739104463557]
  Face sketch synthesis has been widely used in multi-media entertainment and law enforcement. Current image-to-image translation-based face sketch synthesis frequently encounters over-fitting problems when it comes to small-scale datasets. We present an end-to-end Memory Oriented Style Transfer Network (MOST-Net) for face sketch synthesis which can produce high-fidelity sketches with limited data.
  arXiv  Detail & Related papers  (2022-02-08T01:51:24Z)
• Memory-Guided Semantic Learning Network for Temporal Sentence Grounding [55.31041933103645]
  We propose a memory-augmented network that learns and memorizes the rarely appeared content in TSG tasks. MGSL-Net consists of three main parts: a cross-modal inter-action module, a memory augmentation module, and a heterogeneous attention module.
  arXiv  Detail & Related papers  (2022-01-03T02:32:06Z)
• Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration [65.11022516031463]
  We present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction. In this paper, we propose asymmetric co-attention (AsyCA) which is attached at each interleaved node to model the feature dependencies. Our presented DIN can be trained end-to-end and applied to various image restoration tasks.
  arXiv  Detail & Related papers  (2020-10-29T15:32:00Z)
• Distributed Associative Memory Network with Memory Refreshing Loss [5.5792083698526405]
  We introduce a novel Distributed Associative Memory architecture (DAM) with Memory Refreshing Loss (MRL) Inspired by how the human brain works, our framework encodes data with distributed representation across multiple memory blocks. MRL enables MANN to reinforce an association between input data and task objective by reproducing input data from stored memory contents.
  arXiv  Detail & Related papers  (2020-07-21T07:34:33Z)

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.