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Found 23 papers, 3 papers with code
Alibaba Speech Translation Systems for IWSLT 2018
This work describes the En→De Alibaba speech translation system developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2018.
PigV$^2$: Monitoring Pig Vital Signs through Ground Vibrations Induced by Heartbeat and Respiration
In this paper, we introduce PigV$^2$, the first system to monitor pig heart rate and respiratory rate through ground vibrations.
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?
The lack of competency awareness makes NMT untrustworthy.
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures.
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range.
Sharp Attention for Sequence to Sequence Learning
Attention mechanism has been widely applied to tasks that output some sequence from an input image.
PI3NN: Out-of-distribution-aware prediction intervals from three neural networks
First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple PIs.
A Data-driven feature selection and machine-learning model benchmark for the prediction of longitudinal dispersion coefficient
Then, a global optimal feature set (the channel width, the flow velocity, the channel slope and the cross sectional area) was proposed through numerical comparison of the distilled local optimums in performance with representative ML models.
A data-based comparative review and AI-driven symbolic model for longitudinal dispersion coefficient in natural streams
Whereas implicit ML-driven methods are black-boxes in nature, explicit ML-driven methods have more potential in prediction of LDC.
Context-Interactive Pre-Training for Document Machine Translation
Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information.
Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement
Multi-view clustering is an important yet challenging task in machine learning and data mining community.
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment
iii) The partition level information has not been utilized in existing work.
Retrieving High-Dimensional Quantum Steering From a Noisy Environment with N Measurement Settings
One of the most often implied benefits of high-dimensional (HD) quantum systems is to lead to stronger forms of correlations, featuring increased robustness to noise.
Quantum Physics
Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation
In this paper, we research extensively the pros and cons of the standard transformer in document-level translation, and find that the auto-regressive property can simultaneously bring both the advantage of the consistency and the disadvantage of error accumulation.
Extending Label Smoothing Regularization with Self-Knowledge Distillation
The experiment results confirm that the TC can help LsrKD and MrKD to boost training, especially on the networks they are failed.
LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
and yields 100% lane classification accuracy with 200 meters of driving data, achieving over 90% with just 100 m (correspondingly to roughly one minute of driving).
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
Visual Agreement Regularized Training for Multi-Modal Machine Translation
Further analysis demonstrates that the proposed regularized training can effectively improve the agreement of attention on the image, leading to better use of visual information.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
Lattice Transformer for Speech Translation
Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results.
Alibaba's Neural Machine Translation Systems for WMT18
We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese.
PSA: A novel optimization algorithm based on survival rules of porcellio scaber
Bio-inspired algorithms such as neural network algorithms and genetic algorithms have received a significant amount of attention in both academic and engineering societies.
