Paper Review (8) 썸네일형 리스트형 [Paper Review] Robust Speech Recognition via Large-Scale Weak Supervision Robust Speech Recognition via Large-Scale Weak SupervisionWe study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standardarxiv.org0. AbstractSuggest large-scale and weakly-supervised speech processing mode.. [Paper Review] Conformer: Convolution-augmented Transformer for Speech Recognition Conformer: Convolution-augmented Transformer for Speech RecognitionRecently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interacarxiv.org0. AbstractTransformer models are good at capturing content-based .. [Paper Review] Sequence Transduction with Recurrent Neural Networks https://arxiv.org/pdf/1211.37110. AbstractMany machine learning tasks can be expressed as the transformation—or transduction —of input sequences into output sequences: speech recognition, machine translation and so on One of the key challenges in sequence transduction is learning to represent both the input and output sequences in a way that is invariant to sequential distortions such as shrinki.. [Paper Review] Neural RRT*: Learning-Based Optimal Path Planning Neural RRT*: Learning-Based Optimal Path PlanningRapidly random-exploring tree (RRT) and its variants are very popular due to their ability to quickly and efficiently explore the state space. However, they suffer sensitivity to the initial solution and slow convergence to the optimal solution, which meanieeexplore.ieee.org0. AbstractRapidly random-exploring tree (RRT) is popular path planning al.. [Paper Review] Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks https://www.cs.toronto.edu/~graves/icml_2006.pdf0. AbstractIn speech recognition, for example, an acoustic signal is transcribed into words or sub-word units.RNNs are powerful sequence learners but there are two problems. 0.1 Pre-segmented training dataExample: Let's say we have an audio clip of someone saying "Hello world". Pre-segmented data might look like this:"He-" (0.0s - 0.2s) "-llo" (0.2.. [Paper Review] Attention Is All You Need Attention Is All You NeedThe dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a newarxiv.org0. AbstractThe dominant sequence transduction models are based on complex recurrent or convolutional neural n.. [Paper Review] Factorization Machine Factorization Machines In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor working with any real valued feature vector. ieeexplore.ieee.org Factorization Machines(FM)은 support vector machine(SVM)과 factorization model의 장점을 합한 새로운 예측기이다. 1).. [Paper Review] Neural Machine Translation by Jointly Learning to Align and Translate Neural Machine Translation by Jointly Learning to Align and TranslateNeural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the traarxiv.org Abstractionseq2seq에서는 Encoder가 문장을 입력받아서 fixed length context v.. 이전 1 다음
