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Found 9 papers, 4 papers with code
GP-NAS-ensemble: a model for NAS Performance Prediction
It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture.
DQN Control Solution for KDD Cup 2021 City Brain Challenge
From our perspective, the major challenge of this competition is how to extend the classical DQN framework to traffic signals control in real-world complex road network and traffic flow situation.
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities.
Question Directed Graph Attention Network for Numerical Reasoning over Text
Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.
Ranked #1 on
Question Answering
on DROP Test
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN).
Symmetric Regularization based BERT for Pair-wise Semantic Reasoning
To remedy this, we propose to augment the NSP task to a 3-class categorization task, which includes a category for previous sentence prediction (PSP).
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities.
Convolutional Sequence to Sequence Non-intrusive Load Monitoring
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper.
Short-term Load Forecasting with Deep Residual Networks
We present in this paper a model for forecasting short-term power loads based on deep residual networks.
