Connectionist Temporal Classification: Labelling Unsegmented Sequence Data With Recurrent Neural Networks - Alex Graves, Santiago Fernandez, Faustino Gomez, Jurgen Schmidhuber

"Connectionist Temporal Classification: Labelling Unsegmented Sequence Data With Recurrent Neural Networks" by Alex Graves, Santiago Fernandez, Faustino Gomez, Jurgen Schmidhuber is a research paper that introduces a novel method for labeling unsegmented sequence data using Recurrent Neural Networks (RNNs).

The Connectionist Temporal Classification: Labelling Unsegmented Sequence Data With Recurrent Neural Networks paper was filed by Alex Graves, Santiago Fernandez, Faustino Gomez, and Jurgen Schmidhuber.

FAQ

Q: What is the title of the document?A: Connectionist Temporal Classification: Labelling Unsegmented Sequence Data With Recurrent Neural Networks

Q: Who are the authors of the document?A: Alex Graves, Santiago Fernandez, Faustino Gomez, Jurgen Schmidhuber

Q: What is the topic of the document?A: Labelling unsegmented sequence data using recurrent neural networks

Q: What is Connectionist Temporal Classification (CTC)?A: A method for training sequence prediction models that do not require aligned input-output pairs

Q: How does CTC work?A: It introduces a special blank label and a labeling procedure that allows the model to learn both the correct sequence and the alignment simultaneously

Q: What is a recurrent neural network (RNN)?A: A type of artificial neural network that has feedback connections, allowing it to process sequential data

Q: What is the advantage of using RNNs for sequence labeling?A: RNNs can learn to capture the dependencies between elements in a sequence, making them suitable for tasks like sequence labeling

Q: What are some applications of CTC and RNNs?A: Speech recognition, handwriting recognition, machine translation, and audio event detection are some examples

Q: Who might be interested in this document?A: Researchers and practitioners in the field of machine learning, specifically in the area of sequence prediction