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Found 21 papers, 11 papers with code
Subspace Decomposition based DNN algorithm for elliptic-type multi-scale PDEs
Numerical results show that the SD$^2$NN model is superior to existing models such as MscaleDNN.
A multi-scale DNN algorithm for nonlinear elliptic equations with multiple scales
Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community.
Computational Physics Analysis of PDEs
Multi-scale Receptive Fields Graph Attention Network for Point Cloud Classification
The proposed MRFGAT architecture is tested on ModelNet10 and ModelNet40 datasets, and results show it achieves state-of-the-art performance in shape classification tasks.
Deep Transformers with Latent Depth
As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair.
Multilingual Translation with Extensible Multilingual Pretraining and Finetuning
Recent work demonstrates the potential of multilingual pretraining of creating one model that can be used for various tasks in different languages.
Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation
This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs).
Findings of the Fourth Workshop on Neural Generation and Translation
We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020).
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
Cross-lingual Retrieval for Iterative Self-Supervised Training
Recent studies have demonstrated the cross-lingual alignment ability of multilingual pretrained language models.
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data
(1) We formalize the problem of validating the textual attribute values of products from a variety of categories as a natural language inference task in the few-shot learning setting, and propose a meta-learning latent variable model to jointly process the signals obtained from product profiles and textual attribute values.
Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation
For BART we get the best performance by freezing most of the model parameters, and adding extra positional embeddings.
Multilingual Denoising Pre-training for Neural Machine Translation
This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.
Improved Variational Neural Machine Translation by Promoting Mutual Information
Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders.
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens.
Ranked #3 on
Machine Translation
on WMT2016 English-Romanian
Efficient Knowledge Graph Accuracy Evaluation
To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts.
Databases
Findings of the First Shared Task on Machine Translation Robustness
We share the findings of the first shared task on improving robustness of Machine Translation (MT).
On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models
Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance.
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models
Adversarial examples --- perturbations to the input of a model that elicit large changes in the output --- have been shown to be an effective way of assessing the robustness of sequence-to-sequence (seq2seq) models.
A Corpus for Multilingual Document Classification in Eight Languages
In addition, we have observed that the class prior distributions differ significantly between the languages.
CVTE at IJCNLP-2017 Task 1: Character Checking System for Chinese Grammatical Error Diagnosis Task
Grammatical error diagnosis is an important task in natural language processing.
Deep Voice: Real-time Neural Text-to-Speech
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks.
