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Found 18 papers, 4 papers with code
Practical Strategies of Active Learning to Rank for Web Search
To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results.
Structure-aware Protein Self-supervised Learning
Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning.
Efficient Device Scheduling with Multi-Job Federated Learning
However, the scheduling of devices for multiple jobs with FL remains a critical and open problem.
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs.
Adversarial Neural Trip Recommendation
To that end, we propose an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above challenges.
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
Recent advances in graph neural networks (GNNs) have shown great promise in applying GNNs for molecular representation learning.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.
C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak
Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city.
Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction
The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.
Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation
Point-of-Interest (POI) oriented question answering (QA) aims to return a list of POIs given a question issued by a user.
Joint Intent Detection and Entity Linking on Spatial Domain Queries
Continuous efforts have been devoted to language understanding (LU) for conversational queries with the fast and wide-spread popularity of voice assistants.
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection
To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs.
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
A mobile app interface usually consists of a set of user interface modules.
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).
Store Location Selection via Mining Search Query Logs of Baidu Maps
In this paper, we propose a Demand Distribution Driven Store Placement (D3SP) framework for business store placement by mining search query data from Baidu Maps.
A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report
We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types.
