Proceedings ArticleDOI
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves,Santiago Fernández,Faustino Gomez,Jürgen Schmidhuber +3 more
- pp 369-376
TLDR
This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems of sequence learning and post-processing.Abstract:
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.read more
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On Generalization Bounds of a Family of Recurrent Neural Networks.
Minshuo Chen,Xingguo Li,Tuo Zhao +2 more
TL;DR: This work studies the generalization properties of vanilla RNNs as well as their variants, including Minimal Gated Unit (MGU), Long Short Term Memory (LSTM), and Convolutional (Conv) Rnns, and establishes refined generalization bounds with additional norm assumptions.
Proceedings ArticleDOI
MASR: A Modular Accelerator for Sparse RNNs
Udit Gupta,Brandon Reagen,Lillian Pentecost,Marco Donato,Thierry Tambe,Alexander M. Rush,Gu-Yeon Wei,David Brooks +7 more
TL;DR: MASR as mentioned in this paper accelerates bidirectional RNNs for on-chip ASR by exploiting sparsity in both dynamic activations and static weights, which enables designs that efficiently scale from resource-constrained low-power IoT applications to large-scale, highly parallel datacenter deployments.
Journal ArticleDOI
Long short-term memory recurrent neural network-based acoustic model using connectionist temporal classification on a large-scale training corpus
TL;DR: A LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated and is similar to a performance of the acoustic model based on the hybrid method.
Journal ArticleDOI
Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier
TL;DR: A new method of feature extraction and classification based on gray-level difference method and hybrid MLPNN-ICA classifier is proposed, which is implemented on CASIA-Iris V3 dataset and UCI machine learning repository datasets.
Posted Content
Fooling OCR Systems with Adversarial Text Images.
Congzheng Song,Vitaly Shmatikov +1 more
TL;DR: It is demonstrated that state-of-the-art optical character recognition (OCR) based on deep learning is vulnerable to adversarial images.
References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.