Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in human intelligence, computational models of associative memories have been developed for several decades now. They include autoassociative memories, which allow for storing data points and retrieving a stored data point $s$ when provided with a noisy or partial variant of $s$, and heteroassociative memories, able to store and recall multi-modal data. In this paper, we present a novel neural model for realizing associative memories, based on a hierarchical generative network that receives external stimuli via sensory neurons. This model is trained using predictive coding, an error-based learning algorithm inspired by information processing in the cortex. To test the capabilities of this model, we perform multiple retrieval experiments from both corrupted and incomplete data points. In an extensive comparison, we show that this new model outperforms in retrieval accuracy and robustness popular associative memory models, such as autoencoders trained via backpropagation, and modern Hopfield networks. In particular, in completing partial data points, our model achieves remarkable results on natural image datasets, such as ImageNet, with a surprisingly high accuracy, even when only a tiny fraction of pixels of the original images is presented. Furthermore, we show that this method is able to handle multi-modal data, retrieving images from descriptions, and vice versa. We conclude by discussing the possible impact of this work in the neuroscience community, by showing that our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.
Learning deep representations to solve complex machine learning tasks has become the prominent trend in the past few years. Indeed, Deep Neural Networks are now the golden standard in domains as various as computer vision, natural language processing or even playing combinatorial games. However, problematic limitations are hidden behind this surprising universal capability. Among other things, explainability of the decisions is a major concern, especially since deep neural networks are made up of a very large number of trainable parameters. Moreover, computational complexity can quickly become…
An ensemble method should cleverly combine a group of base classifiers to yield an improved classifier. The majority vote is an example of a methodology used to combine classifiers in an ensemble method. In this paper, we propose to combine classifiers using an associative memory model. Precisely, we introduce ensemble methods based on recurrent correlation associative memories (RCAMs) for binary classification problems. We show that an RCAM-based ensemble classifier can be viewed as a majority vote classifier whose weights depend on the similarity between the base classifiers and the resultin…
In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this layer can help design Deep Neural Networks which support incremental learning and that can be (partially) trained in real time on embedded devices. Experiments on the ImageNet dataset and other different domain specific datasets show that it is possible to design more flexible and faster-to-train Neural Networks at the cost of a slight decrease in accuracy.
Telecom Bretagne（布列塔尼电信学校）；University of Münster（明斯特大学）；Université de Bretagne Occidentale（布列塔尼西方大学）
Nearest neighbor search is a very active field in machine learning for it appears in many application cases, including classification and object retrieval. In its canonical version, the complexity of the search is linear with both the dimension and the cardinal of the collection of vectors the search is performed in. Recently many works have focused on reducing the dimension of vectors using quantization techniques or hashing, while providing an approximate result. In this paper we focus instead on tackling the cardinal of the collection of vectors. Namely, we introduce a technique that partit…
Hyperparameter Transfer for Dense Associative Memories
Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK（鲁道夫·皮尔士理论物理中心，牛津大学，牛津 OX1 3PU, 英国）；Dynamical Mind, IBM Research（动态思维，IBM研究）；Princeton ORFE（普林斯顿ORFE）
Semantically-correlated memories in a dense associative model
Institute for Computational and Experimental Research in Mathematics, Brown University（布朗大学计算与实验数学研究所）；SciAI Center, Cornell University（康奈尔大学SciAI中心）；Neural Coding and Brain Computing Unit, OIST Graduate University（冲绳科学技术大学院大学神经编码与脑计算单元）
National Institute of Standards and Technology（美国国家标准与技术研究院）
Simulations of complex-valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay elements that implement complex weights by phase shifting the oscillatory output of the oscillators. Pseudo-inverse training suffices to store at least 12 images in a set of 192 oscillators, representing 16$\times$12 pixel images. The energy required to recover an image depends on the desired error level. For the oscillators and circuitry considered here, 5 % root mean square deviations from the ideal image require a…
Associative memory plays an important role in human intelligence and its mechanisms have been linked to attention in machine learning. While the machine learning community's interest in associative memories has recently been rekindled, most work has focused on memory recall ($read$) over memory learning ($write$). In this paper, we present BayesPCN, a hierarchical associative memory capable of performing continual one-shot memory writes without meta-learning. Moreover, BayesPCN is able to gradually forget past observations ($forget$) to free its memory. Experiments show that BayesPCN can recal…
MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；IBM Research（IBM研究院）
Dense Associative Memories or Modern Hopfield Networks have many appealing properties of associative memory. They can do pattern completion, store a large number of memories, and can be described using a recurrent neural network with a degree of biological plausibility and rich feedback between the neurons. At the same time, up until now all the models of this class have had only one hidden layer, and have only been formulated with densely connected network architectures, two aspects that hinder their machine learning applications. This paper tackles this gap and describes a fully recurrent mo…
Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
Physics Department, Sapienza University of Rome（罗马大学物理系）；Institute of Nanotechnology, National Research Council of Italy, CNR-NANOTEC, Rome Unit（意大利国家研究委员会纳米技术研究所，CNR-NANOTEC，罗马分部）
Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data
Department of Computer Science, Rensselaer Polytechnic Institute ( RPI )（里士满理工学院计算机科学系）；Donders Institute for Brain, Cognition, and Behaviour, Radboud University（拉布德大学脑科学与行为研究所）；Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université（巴黎中央理工学院物理理论与建模实验室）
Posgrado en Ciencia e Ingeniería de la Computación, UNAM（墨西哥国立自治大学计算机科学与工程研究生院）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）；IIMAS（数学研究中心）；Universidad de Guadalajara（瓜达拉哈拉大学）
VERSES AI Research Lab（VERSES AI研究实验室）；Medical Research Council Brain Network Dynamics Unit（医学研究委员会脑网络动力学单元）；University of Oxford（牛津大学）；Institute of Logic and Computation（逻辑与计算研究所）；Vienna University of Technology（维也纳技术大学）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）
Phases in a class of associative memories via hidden neurons
CyberAgent AI Lab（CyberAgent AI实验室）；RIKEN iTHEMS（RIKEN跨学科理论科学研究所）；Graduate School of Artificial Intelligence and Science, Rikkyo University（立教大学人工智能与科学研究科）
Thermodynamics of bidirectional associative memories
Università del Salento（萨伦托大学）；Istituto Nazionale di Fisica Nucleare（国家核物理研究所）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）
Semantic and episodic memories in a predictive coding model of the neocortex
Centre Inria de l’université de Bordeaux（法国波尔多大学Inria研究中心）；Laboratoire Bordelais de Recherche en Informatique (LaBRI)（波尔多信息研究中心）；Institut des Maladies Neurodégénératives (IMN)（神经退行性疾病研究所）；Bordeaux, France（法国波尔多）
MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；IBM Research（IBM研究院）；Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Princeton University（普林斯顿大学）
Dense Associative Memories or modern Hopfield networks permit storage and reliable retrieval of an exponentially large (in the dimension of feature space) number of memories. At the same time, their naive implementation is non-biological, since it seemingly requires the existence of many-body synaptic junctions between the neurons. We show that these models are effective descriptions of a more microscopic (written in terms of biological degrees of freedom) theory that has additional (hidden) neurons and only requires two-body interactions between them. For this reason our proposed microscopic …
Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
Department of Computer Science, Rensselaer Polytechnic Institute ( RPI )（计算机科学系，罗切斯特理工学院（RPI））；Jheronimus Academy of Data Science, Tilburg University（数据科学学院，蒂尔堡大学）；Department of Physics, University of Rome Sapienza（物理系，罗马萨皮恩扎大学）；Donders Institute for Brain, Cognition, and Behaviour, Radboud University（大脑、认知与行为研究所，拉德堡德大学）；IBM Research（IBM研究院）
We propose that the Continual Learning desiderata can be achieved through a neuro-inspired architecture, grounded on Mountcastle's cortical column hypothesis. The proposed architecture involves a single module, called Self-Taught Associative Memory (STAM), which models the function of a cortical column. STAMs are repeated in multi-level hierarchies involving feedforward, lateral and feedback connections. STAM networks learn in an unsupervised manner, based on a combination of online clustering and hierarchical predictive coding. This short paper only presents the architecture and its connectio…
