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Found 13 papers, 8 papers with code
Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
This work offers a new perspective on constructing datasets and enhancing the generalization of TSF models through self-supervised learning.
Poly2Vec: Polymorphic Encoding of Geospatial Objects for Spatial Reasoning with Deep Neural Networks
Encoding geospatial data is crucial for enabling machine learning (ML) models to perform tasks that require spatial reasoning, such as identifying the topological relationships between two different geospatial objects.
Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version)
First, we propose a message propagation imputation network (MPIN) that is able to recover the missing values of data instances in a time window.
LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
Many deep learning models have been proposed to improve the accuracy of CTS forecasting.
Ranked #1 on
Traffic Prediction
on PeMS04
(FLOPs(M) metric, using extra
training data)
Computational Efficiency
Correlated Time Series Forecasting
+4
PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation
Recently, the practical deployment of open-domain dialogue systems has been plagued by the knowledge issue of information deficiency and factual inaccuracy.
Towards Boosting the Open-Domain Chatbot with Human Feedback
Many open-domain dialogue models pre-trained with social media comments can generate coherent replies but have difficulties producing engaging responses when interacting with real users.
RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques
Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms.
Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes
In recent years, Internet memes have been widely used in online chatting.
Relational Surrogate Loss Learning
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e. g., average precision and F1 score.
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation
To explore the limit of dialogue generation pre-training, we present the models of PLATO-XL with up to 11 billion parameters, trained on both Chinese and English social media conversations.
Learning to Select External Knowledge with Multi-Scale Negative Sampling
The Track-1 of DSTC9 aims to effectively answer user requests or questions during task-oriented dialogues, which are out of the scope of APIs/DB.
An Experimental Analysis of Indoor Spatial Queries: Modeling, Indexing, and Processing
Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries.
Databases Data Structures and Algorithms
Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks
Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps.
