Introducing the Hidden Neural Markov Chain framework

• URL: http://arxiv.org/abs/2102.11038v1
• Date: Wed, 17 Feb 2021 20:13:45 GMT
• Title: Introducing the Hidden Neural Markov Chain framework
• Authors: Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon, Wojciech Pieczynski
• Abstract summary: This paper proposes the original Hidden Neural Markov Chain (HNMC) framework, a new family of sequential neural models. We propose three different models: the classic HNMC, the HNMC2, and the HNMC-CN. It shows this new neural sequential framework's potential, which can open the way to new models and might eventually compete with the prevalent BiLSTM and BiGRU.
• Score: 7.85426761612795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, neural network models achieve state-of-the-art results in many areas as computer vision or speech processing. For sequential data, especially for Natural Language Processing (NLP) tasks, Recurrent Neural Networks (RNNs) and their extensions, the Long Short Term Memory (LSTM) network and the Gated Recurrent Unit (GRU), are among the most used models, having a "term-to-term" sequence processing. However, if many works create extensions and improvements of the RNN, few have focused on developing other ways for sequential data processing with neural networks in a "term-to-term" way. This paper proposes the original Hidden Neural Markov Chain (HNMC) framework, a new family of sequential neural models. They are not based on the RNN but on the Hidden Markov Model (HMM), a probabilistic graphical model. This neural extension is possible thanks to the recent Entropic Forward-Backward algorithm for HMM restoration. We propose three different models: the classic HNMC, the HNMC2, and the HNMC-CN. After describing our models' whole construction, we compare them with classic RNN and Bidirectional RNN (BiRNN) models for some sequence labeling tasks: Chunking, Part-Of-Speech Tagging, and Named Entity Recognition. For every experiment, whatever the architecture or the embedding method used, one of our proposed models has the best results. It shows this new neural sequential framework's potential, which can open the way to new models, and might eventually compete with the prevalent BiLSTM and BiGRU.

Related papers

• Learning Long Sequences in Spiking Neural Networks [0.0]
  Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations. Recent interest in efficient alternatives to Transformers has given rise to state-of-the-art recurrent architectures named state space models (SSMs)
  arXiv  Detail & Related papers  (2023-12-14T13:30:27Z)
• Hierarchically Gated Recurrent Neural Network for Sequence Modeling [36.14544998133578]
  We propose a gated linear RNN model dubbed Hierarchically Gated Recurrent Neural Network (HGRN) Experiments on language modeling, image classification, and long-range arena benchmarks showcase the efficiency and effectiveness of our proposed model.
  arXiv  Detail & Related papers  (2023-11-08T16:50:05Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• Analyzing Populations of Neural Networks via Dynamical Model Embedding [10.455447557943463]
  A core challenge in the interpretation of deep neural networks is identifying commonalities between the underlying algorithms implemented by distinct networks trained for the same task. Motivated by this problem, we introduce DYNAMO, an algorithm that constructs low-dimensional manifold where each point corresponds to a neural network model, and two points are nearby if the corresponding neural networks enact similar high-level computational processes. DYNAMO takes as input a collection of pre-trained neural networks and outputs a meta-model that emulates the dynamics of the hidden states as well as the outputs of any model in the collection.
  arXiv  Detail & Related papers  (2023-02-27T19:00:05Z)
• Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement [11.591247347259317]
  We propose the first approach to decompose an RNN into modules. We study different types of RNNs, i.e., Vanilla, LSTM, and GRU. We show how such RNN modules can be reused and replaced in various scenarios.
  arXiv  Detail & Related papers  (2022-12-09T03:29:38Z)
• MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering [96.65234340724237]
  We show the equivalence between some state-of-the-art GNN-based CF models and a traditional 1-layer NRL model based on context encoding. We present Markov Graph Diffusion Collaborative Filtering (MGDCF) to generalize some state-of-the-art GNN-based CF models.
  arXiv  Detail & Related papers  (2022-04-05T17:24:32Z)
• Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers [67.688697838109]
  This paper presents a novel method to train quantized RNNLMs from scratch using alternating direction methods of multipliers (ADMM) Experiments on two tasks suggest the proposed ADMM quantization achieved a model size compression factor of up to 31 times over the full precision baseline RNNLMs.
  arXiv  Detail & Related papers  (2021-11-29T09:30:06Z)
• A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
  Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem. We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
  arXiv  Detail & Related papers  (2020-10-05T04:57:18Z)
• Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequence [24.594587557319837]
  We develop a graph filter framework for approximating the modelling of hidden states in Recurrent Neural Networks (RNNs) The proposed framework is validated through several multi-way sequence modelling tasks and benchmarked against traditional RNNs. We show that the proposed RGTN is capable of not only out-performing standard RNNs, but also mitigating the Curse of Dimensionality associated with traditional RNNs.
  arXiv  Detail & Related papers  (2020-09-18T10:13:36Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

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.