(2) How to cohere with context and preserve the knowledge when generating a stylized response.
Specifically, we creatively propose Multi-granularity Intent Heterogeneous Session Graph which captures the interactions between different granularity intent units and relieves the burden of long-dependency.
Ranked #1 on Session-Based Recommendations on yoochoose1/4
In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.
Creating labeled training sets has become one of the major roadblocks in machine learning.
On the one hand, multi-hop-based approaches do not explicitly distinguish relevant nodes from a large number of multi-hop neighborhoods, leading to a severe over-smoothing problem.
To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.
However, simply integrating KGs in current KG-based RS models is not necessarily a guarantee to improve the recommendation performance, which may even weaken the holistic model capability.
CustomGNN can automatically learn the high-level semantics for specific downstream tasks, highlight semantically relevant paths, and filter out task-irrelevant information in the graph.
Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps.
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.
In this paper, we propose a novel and generic mechanism based on evolving attention to improve the performance of transformers.
Instead, we model their dependencies via a chain of prediction models that take previous attention maps as input to predict the attention maps of a new layer through convolutional neural networks.
We add the model designed by AutoADR as a sub-model into the production Ad Relevance model.
We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model.
Ranked #2 on Unbiased Scene Graph Generation on Visual Genome (mR@20 metric)
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.
Finally, we perform graph neural networks to infer the global-optimal answer by jointly considering all the concepts.
The task is challenging because of the complex characteristics of time-series, which are messy, stochastic, and often without proper labels.
In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features.
BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks.
With the success of deep neural networks, Neural Architecture Search (NAS) as a way of automatic model design has attracted wide attention.
Learning text representation is crucial for text classification and other language related tasks.
For these applications, graph embedding is crucial as it provides vector representations of the graph.
no code implementations • 10 Oct 2019 • Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time.