Enhancing Latent Space Clustering in Multi-filter Seq2Seq Model: A Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2109.12399v1
• Date: Sat, 25 Sep 2021 16:36:31 GMT
• Title: Enhancing Latent Space Clustering in Multi-filter Seq2Seq Model: A Reinforcement Learning Approach
• Authors: Yunhao Yang, Zhaokun Xue
• Abstract summary: We design a latent-enhanced multi-filter seq2seq model (LMS2S) that analyzes the latent space representations using a clustering algorithm. Our experiments on semantic parsing and machine translation demonstrate the positive correlation between the clustering quality and the model's performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In sequence-to-sequence language processing tasks, sentences with heterogeneous semantics or grammatical structures may increase the difficulty of convergence while training the network. To resolve this problem, we introduce a model that concentrates the each of the heterogeneous features in the input-output sequences. Build upon the encoder-decoder architecture, we design a latent-enhanced multi-filter seq2seq model (LMS2S) that analyzes the latent space representations using a clustering algorithm. The representations are generated from an encoder and a latent space enhancer. A cluster classifier is applied to group the representations into clusters. A soft actor-critic reinforcement learning algorithm is applied to the cluster classifier to enhance the clustering quality by maximizing the Silhouette score. Then, multiple filters are trained by the features only from their corresponding clusters, the heterogeneity of the training data can be resolved accordingly. Our experiments on semantic parsing and machine translation demonstrate the positive correlation between the clustering quality and the model's performance, as well as show the enhancement our model has made with respect to the ordinary encoder-decoder model.

Related papers

• Hierarchical Clustering for Conditional Diffusion in Image Generation [12.618079575423868]
  This paper introduces TreeDiffusion, a deep generative model that conditions Diffusion Models on hierarchical clusters to obtain high-quality, cluster-specific generations. The proposed pipeline consists of two steps: a VAE-based clustering model that learns the hierarchical structure of the data, and a conditional diffusion model that generates realistic images for each cluster.
  arXiv  Detail & Related papers  (2024-10-22T11:35:36Z)
• Deep Double Self-Expressive Subspace Clustering [7.875193047472789]
  We propose a double self-expressive subspace clustering algorithm. The proposed algorithm can achieve better clustering than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-20T15:10:35Z)
• Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model [79.46465138631592]
  We devise an efficient algorithm that recovers clusters using the observed labels. We present Instance-Adaptive Clustering (IAC), the first algorithm whose performance matches these lower bounds both in expectation and with high probability.
  arXiv  Detail & Related papers  (2023-06-18T08:46:06Z)
• DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks [53.88811980967342]
  This paper presents a Deep Clustering via Ensembles (DeepCluE) approach. It bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.
  arXiv  Detail & Related papers  (2022-06-01T09:51:38Z)
• Deep Attention-guided Graph Clustering with Dual Self-supervision [49.040136530379094]
  We propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC) We develop a dual self-supervision solution consisting of a soft self-supervision strategy with a triplet Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Our method consistently outperforms state-of-the-art methods on six benchmark datasets.
  arXiv  Detail & Related papers  (2021-11-10T06:53:03Z)
• Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning [72.9458277424712]
  Mixture Model Auto-Encoders (MixMate) is a novel architecture that clusters data by performing inference on a generative model. We show that MixMate achieves competitive performance compared to state-of-the-art deep clustering algorithms.
  arXiv  Detail & Related papers  (2021-10-10T02:30:31Z)
• Attention-driven Graph Clustering Network [49.040136530379094]
  We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN) AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-08-12T02:30:38Z)
• Learning the Precise Feature for Cluster Assignment [39.320210567860485]
  We propose a framework which integrates representation learning and clustering into a single pipeline for the first time. The proposed framework exploits the powerful ability of recently developed generative models for learning intrinsic features. Experimental results show that the performance of the proposed method is superior, or at least comparable to, the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-11T04:08:54Z)
• Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning [2.8028128734158164]
  Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. We present a novel evolutionary unsupervised learning representation model with iterative optimization. We jointly fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments.
  arXiv  Detail & Related papers  (2021-03-31T13:58:10Z)
• Joint Optimization of an Autoencoder for Clustering and Embedding [22.16059261437617]
  We present an alternative where the autoencoder and the clustering are learned simultaneously. That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model.
  arXiv  Detail & Related papers  (2020-12-07T14:38:10Z)
• Dual Adversarial Auto-Encoders for Clustering [152.84443014554745]
  We propose Dual Adversarial Auto-encoder (Dual-AAE) for unsupervised clustering. By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders. Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods.
  arXiv  Detail & Related papers  (2020-08-23T13:16:34Z)

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.