274 research outputs found
Sequential Pattern Mining with Multidimensional Interval Items
In real sequence pattern mining scenarios, the interval information between two item sets is very important. However, although existing algorithms can effectively mine frequent subsequence sets, the interval information is ignored. This paper aims to mine sequential patterns with multidimensional interval items in sequence databases. In order to address this problem, this paper defines and specifies the interval event problem in the sequential pattern mining task. Then, the interval event items framework is proposed to handle the multidimensional interval event items. Moreover, the MII-Prefixspan algorithm is introduced for the sequential pattern with multidimensional interval event items mining tasks. This algorithm adds the processing of interval event items in the mining process. We can get richer and more in line with actual needs information from mined sequence patterns through these methods. This scheme is applied to the actual website behaviour analysis task to obtain more valuable information for web optimization and provide more valuable sequence pattern information for practical problems. This work also opens a new pathway toward more efficient sequential pattern mining tasks
An RNN Model for Generating Sentences with a Desired Word at a Desired Position
Generating sentences with a desired word is useful in many natural language processing tasks. State-of-the-art recurrent neural network (RNN)-based models mainly generate sentences in a left-to-right manner, which does not allow explicit and direct constraints on the words at arbitrary positions in a sentence. To address this issue, we propose a generative model of sentences named Coupled-RNN. We employ two RNN\u27s to generate sentences backwards and forwards respectively starting from a desired word, and inject position embeddings into the model to solve the problem of position information loss. We explore two coupling mechanisms to optimize the reconstruction loss globally. Experimental results demonstrate that Coupled-RNN can generate high quality sentences that contain a desired word at a desired position
Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images
Targeting at depicting land covers with pixel-wise semantic categories,
semantic segmentation in remote sensing images needs to portray diverse
distributions over vast geographical locations, which is difficult to be
achieved by the homogeneous pixel-wise forward paths in the architectures of
existing deep models. Although several algorithms have been designed to select
pixel-wise adaptive forward paths for natural image analysis, it still lacks
theoretical supports on how to obtain optimal selections. In this paper, we
provide mathematical analyses in terms of the parameter optimization, which
guides us to design a method called Hidden Path Selection Network (HPS-Net).
With the help of hidden variables derived from an extra mini-branch, HPS-Net is
able to tackle the inherent problem about inaccessible global optimums by
adjusting the direct relationships between feature maps and pixel-wise path
selections in existing algorithms, which we call hidden path selection. For the
better training and evaluation, we further refine and expand the 5-class Gaofen
Image Dataset (GID-5) to a new one with 15 land-cover categories, i.e., GID-15.
The experimental results on both GID-5 and GID-15 demonstrate that the proposed
modules can stably improve the performance of different deep structures, which
validates the proposed mathematical analyses
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Federated learning has become a popular method to learn from decentralized
heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train
models from a small fraction of labeled data due to label scarcity on
decentralized clients. Existing FSSL methods assume independent and identically
distributed (IID) labeled data across clients and consistent class distribution
between labeled and unlabeled data within a client. This work studies a more
practical and challenging scenario of FSSL, where data distribution is
different not only across clients but also within a client between labeled and
unlabeled data. To address this challenge, we propose a novel FSSL framework
with dual regulators, FedDure.} FedDure lifts the previous assumption with a
coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg): C-reg
regularizes the updating of the local model by tracking the learning effect on
labeled data distribution; F-reg learns an adaptive weighting scheme tailored
for unlabeled instances in each client. We further formulate the client model
training as bi-level optimization that adaptively optimizes the model in the
client with two regulators. Theoretically, we show the convergence guarantee of
the dual regulators. Empirically, we demonstrate that FedDure is superior to
the existing methods across a wide range of settings, notably by more than 11%
on CIFAR-10 and CINIC-10 datasets
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