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Operations Guided Neural Networks for High Fidelity Data-To-Text Generation [article]

Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong Pan, Chin-Yew Lin
2018 arXiv   pre-print
Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks.  ...  In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations.  ...  The contact author of this paper, according to the meaning given to this role by Sun Yat-Sen University, is Rong Pan.  ... 
arXiv:1809.02735v1 fatcat:hivn2d7jprdbllteni5mhpnsdy

Operation-guided Neural Networks for High Fidelity Data-To-Text Generation

Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong Pan, Chin-Yew Lin
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
Recent neural models for data-to-text generation are mostly based on data-driven end-toend training over encoder-decoder networks.  ...  In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations.  ...  The contact author of this paper, according to the meaning given to this role by Sun Yat-Sen University, is Rong Pan.  ... 
doi:10.18653/v1/d18-1422 dblp:conf/emnlp/NieWYPL18 fatcat:hi5coo5ypfbpvnpjrx4wxl2764

Multi-fidelity information fusion with concatenated neural networks [article]

Suraj Pawar, Omer San, Prakash Vedula, Adil Rasheed, Trond Kvamsdal
2021 arXiv   pre-print
(high-fidelity models) through a concatenated neural network.  ...  The proposed framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data.  ...  Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness  ... 
arXiv:2110.04170v1 fatcat:igabq3c735bjfdq5tmnjk3eq5a

A hybrid machine-learning algorithm for designing quantum experiments [article]

L. O'Driscoll, R. Nichols, P. A. Knott
2019 arXiv   pre-print
The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search we incorporate a neural network that classifies  ...  Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over 96%.  ...  Acknowledgements: We thank Joseph Namara Hollis for the artwork in  ... 
arXiv:1812.03183v2 fatcat:vo3dvtahf5bkboprx62z2yrqoq

A hybrid machine learning algorithm for designing quantum experiments

L. O'Driscoll, R. Nichols, P. A. Knott
2019 Quantum Machine Intelligence  
The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search, we incorporate a neural network that classifies  ...  Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over 96%.  ...  Acknowledgements We thank Joseph Namara Hollis for the artwork in Fig 2. We acknowledge discussions with Gerardo Adesso, Ryuji Takagi, Tom Bromley and EnderÖzcan. P.K. acknowledges support  ... 
doi:10.1007/s42484-019-00003-8 dblp:journals/qmi/ODriscollNK19 fatcat:cxpmqn5oxjburehziqufll2cy4

Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue [article]

Vahidullah Tac, Vivek D. Sree, Manuel K. Rausch, Adrian B. Tepole
2021 arXiv   pre-print
Crucially, we show that a multi-fidelity scheme which combines high fidelity experimental data with low fidelity analytical data yields the best performance.  ...  We showcase the ability of the neural network to learn the mechanical behavior of porcine and murine skin from biaxial test data.  ...  Tepole and the National Science Foundation through awards 1916663 and 1916665 to Manuel K.  ... 
arXiv:2107.05388v1 fatcat:kxrpcheyfjefbeaig7noir3ou4

Repetitive readout enhanced by machine learning

Genyue Liu, Mo Chen, Yi-Xiang Liu, David Layden, Paola Cappellaro
2020 Machine Learning: Science and Technology  
Here we show by using machine learning (ML), one obtains higher readout fidelity by taking advantage of the time trace data.  ...  Since the information is already recorded (but usually discarded), this improvement in fidelity does not consume additional experimental time, and could be directly applied to preparation-by-measurement  ...  The data that support the findings of this study are openly available at https://doi.org/10.6084/m9.figshare. 9924911.v1.  ... 
doi:10.1088/2632-2153/ab4e24 fatcat:qwaoex6nkraflff5jnngzkxvgu

Neural Data-to-Text Generation with Dynamic Content Planning [article]

Kai Chen, Fayuan Li, Baotian Hu, Weihua Peng, Qingcai Chen, Hong Yu
2020 arXiv   pre-print
To alleviate these problems, we propose a Neural data-to-text generation model with Dynamic content Planning, named NDP for abbreviation.  ...  Neural data-to-text generation models have achieved significant advancement in recent years.  ...  Nie et al. (2018) proposes operation-guided attention to improve the fidelity of the generated text. points ( 6 -13 FG , 1 -1 3Pt ) and 10 rebounds of his own .  ... 
arXiv:2004.07426v2 fatcat:pq3a5srnare7hl4g2orh4g6o6e

Engineering quantum current states with machine learning [article]

Tobias Haug, Rainer Dumke, Leong-Chuan Kwek, Christian Miniatura, Luigi Amico
2019 arXiv   pre-print
To this end, we exploit deep reinforcement learning to prepare prescribed quantum current states within a short time scale and with a high fidelity.  ...  Our deep reinforcement learning scheme provides solutions for known challenges in quantum technology and opens new avenues for the control of quantum devices.  ...  Neural network to optimize protocols to generate quantum states.  ... 
arXiv:1911.09578v1 fatcat:lzylwi7sv5fpxkzbatyuniwxbi

Truth-Conditional Captioning of Time Series Data [article]

Harsh Jhamtani, Taylor Berg-Kirkpatrick
2021 arXiv   pre-print
A model for this task should be able to extract high-level patterns such as presence of a peak or a dip.  ...  A program in our model is constructed from modules, which are small neural networks that are designed to capture numerical patterns and temporal information.  ...  Acknowledgements We thank anonymous EMNLP reviewers for insightful comments and feedback. We thank Nikita Duseja for useful discussions.  ... 
arXiv:2110.01839v1 fatcat:ohupdlh3c5dafdxsubqimdc57y

Multimodal Image Synthesis and Editing: A Survey [article]

Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Lingjie Liu, Adam Kortylewski, Christian Theobalt, Eric Xing
2022 arXiv   pre-print
We then describe multimodal image synthesis and editing approaches extensively with detailed frameworks including Generative Adversarial Networks (GANs), Auto-regressive models, Diffusion models, Neural  ...  Instead of providing explicit guidance for network training, multimodal guidance offers intuitive and flexible means for image synthesis and editing.  ...  image-text pairs can produce a high-fidelity generative model with controllable results through text prompts.  ... 
arXiv:2112.13592v3 fatcat:46twjhz3hbe6rpm33k6ilnisga

Neural Networks for Quantum Inverse Problems [article]

Ningping Cao, Jie Xie, Aonan Zhang, Shi-Yao Hou, Lijian Zhang, Bei Zeng
2021 arXiv   pre-print
Our method yields high fidelity, efficiency and robustness for both numerical experiments and quantum optical experiments.  ...  The proposed method utilizes the quantum-ness of the QIPs and takes advantage of the computational power of neural networks to achieve higher efficiency for the quantum state estimation.  ...  The network of p β is expected to have high fidelity for β ∈ (0, 5] and substandard performance on other parts (Figure 7c ) because of the data concentration.  ... 
arXiv:2005.01540v2 fatcat:imvdblmk7zdsngsl6uordrmjp4

Exploiting Raw Images for Real-Scene Super-Resolution [article]

Xiangyu Xu, Yongrui Ma, Wenxiu Sun, Ming-Hsuan Yang
2021 arXiv   pre-print
For the second issue, we develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images.  ...  In addition, we propose a dense channel-attention block for better image restoration as well as a learning-based guided filter network for effective color correction.  ...  Network architecture A straightforward approach to exploit raw data for superresolution is to directly learn a mapping function from raw inputs to high-resolution color images with neural networks.  ... 
arXiv:2102.01579v1 fatcat:tzanyrixcjb6dahh4dxdovancu

A Multi-Scale Time-Frequency Spectrogram Discriminator for GAN-based Non-Autoregressive TTS [article]

Haohan Guo, Hui Lu, Xixin Wu, Helen Meng
2022 arXiv   pre-print
In this paper, we propose a multi-scale time-frequency spectrogram discriminator to help NAR-TTS generate high-fidelity Mel-spectrograms.  ...  The generative adversarial network (GAN) has shown its outstanding capability in improving Non-Autoregressive TTS (NAR-TTS) by adversarially training it with an extra model that discriminates between the  ...  In mainstream TTS systems, the neural vocoder is usually adopted for waveform generation due to its high-quality generation.  ... 
arXiv:2203.01080v2 fatcat:q5a24boph5h4jez2j5bgyp5ipa

Classification and reconstruction of optical quantum states with deep neural networks

Shahnawaz Ahmed, Carlos Sánchez Muñoz, Franco Nori, Anton Frisk Kockum
2021 Physical Review Research  
Our methods demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data.  ...  We further show that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection.  ...  [84] ) to achieve high reconstruction fidelity.  ... 
doi:10.1103/physrevresearch.3.033278 fatcat:3yg7gjm3cjag3a67ddjx5vk5oi
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