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Found 11 papers, 1 papers with code
CNNBiF: CNN-based Bigram Features for Named Entity Recognition
More importantly our fine-tuned CoNLL2003 model displays significant gains in generalization to out of domain datasets: on the OntoNotes subset we achieve an F1 of 72. 67 which is 0. 49 points absolute better than the baseline, and on the WNUT16 set an F1 of 68. 22 which is a gain of 0. 48 points.
Unsupervised Adaptation of Question Answering Systems via Generative Self-training
Nevertheless, additional pre-training closer to the end-task, such as training on synthetic QA pairs, has been shown to improve performance.
Embedding-Based Speaker Adaptive Training of Deep Neural Networks
An embedding-based speaker adaptive training (SAT) approach is proposed and investigated in this paper for deep neural network acoustic modeling.
McGan: Mean and Covariance Feature Matching GAN
We introduce new families of Integral Probability Metrics (IPM) for training Generative Adversarial Networks (GAN).
Self-critical Sequence Training for Image Captioning
In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized.
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition
We show that dense prediction view of framewise classification offers several advantages and insights, including computational efficiency and the ability to apply batch normalization.
Co-Occuring Directions Sketching for Approximate Matrix Multiply
We introduce co-occurring directions sketching, a deterministic algorithm for approximate matrix product (AMM), in the streaming model.
Advances in Very Deep Convolutional Neural Networks for LVCSR
We demonstrate the performance of our models both on larger scale data than before, and after sequence training.
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval
Joint modeling of language and vision has been drawing increasing interest.
Random Maxout Features
In this paper, we propose and study random maxout features, which are constructed by first projecting the input data onto sets of randomly generated vectors with Gaussian elements, and then outputing the maximum projection value for each set.
Deep Multimodal Learning for Audio-Visual Speech Recognition
In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic Speech Recognition (AV-ASR).
Audio-Visual Speech Recognition
Automatic Speech Recognition
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