Search Results for author:
Found 25 papers, 10 papers with code
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
(2) How to cohere with context and preserve the knowledge when generating a stylized response.
Learning Multi-granularity User Intent Unit for Session-based Recommendation
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
Multimodal Dialogue Response Generation
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 Training Sets via Weak Indirect Supervision
Creating labeled training sets has become one of the major roadblocks in machine learning.
Graph Pointer Neural Networks
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.
WRENCH: A Comprehensive Benchmark for Weak Supervision
To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.
Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation
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.
Click-Through Rate Prediction
Knowledge-Aware Recommendation
+1
Customizing Graph Neural Networks using Path Reweighting
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.
TS2Vec: Towards Universal Representation of Time Series
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.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees
Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.
Evolving Attention with Residual Convolutions
In this paper, we propose a novel and generic mechanism based on evolving attention to improve the performance of transformers.
Predictive Attention Transformer: Improving Transformer with Attention Map Prediction
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.
AutoADR: Automatic Model Design for Ad Relevance
We add the model designed by AutoADR as a sub-model into the production Ad Relevance model.
CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
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)
Multivariate Time-series Anomaly Detection via Graph Attention Network
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering
Finally, we perform graph neural networks to infer the global-optimal answer by jointly considering all the concepts.
Automated Model Selection for Time-Series Anomaly Detection
The task is challenging because of the complex characteristics of time-series, which are messy, stochastic, and often without proper labels.
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering
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.
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks.
Deeper Insights into Weight Sharing in Neural Architecture Search
With the success of deep neural networks, Neural Architecture Search (NAS) as a way of automatic model design has attracted wide attention.
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Learning text representation is crucial for text classification and other language related tasks.
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications
For these applications, graph embedding is crucial as it provides vector representations of the graph.
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
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.
Time-Series Anomaly Detection Service at Microsoft
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.
