Search Results for author:
Found 27 papers, 14 papers with code
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
The limited availability of modalities for each patient would cause information loss, adversely affecting predictive accuracy.
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios.
Boosting Offline Reinforcement Learning with Residual Generative Modeling
While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected.
Objective-aware Traffic Simulation via Inverse Reinforcement Learning
Traffic simulators act as an essential component in the operating and planning of transportation systems.
Learning to Route via Theory-Guided Residual Network
Therefore, in this paper, we propose to learn the human routing model, which is one of the most essential part in the traffic simulator.
Learning to Simulate on Sparse Trajectory Data
Simulation of the real-world traffic can be used to help validate the transportation policies.
Automatic Historical Feature Generation through Tree-based Method in Ads Prediction
Historical features are important in ads click-through rate (CTR) prediction, because they account for past engagements between users and ads.
Online Structured Meta-learning
When a new task is encountered, it constructs a meta-knowledge pathway by either utilizing the most relevant knowledge blocks or exploring new blocks.
Improving Generalization in Meta-learning via Task Augmentation
Moreover, both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets and are compatible with existing meta-learning algorithms.
How Do We Move: Modeling Human Movement with System Dynamics
To the best of our knowledge, we are the first to learn to model the state transition of moving agents with system dynamics.
A Probabilistic Simulator of Spatial Demand for Product Allocation
Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world.
Automated Relational Meta-learning
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.
Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) serve as useful resources for various natural language processing applications.
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there have been extensive studies.
Targeted Source Detection for Environmental Data
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely.
Hierarchically Structured Meta-learning
In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks.
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Phase Competition for Traffic Signal Control
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.
Diagnosing Reinforcement Learning for Traffic Signal Control
In this paper, we propose to re-examine the RL approaches through the lens of classic transportation theory.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
A Survey on Traffic Signal Control Methods
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections.
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm.
Detecting Outliers in Data with Correlated Measures
Advances in sensor technology have enabled the collection of large-scale datasets.
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i. e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical.
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations.
A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data
The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics.
Generalized Fisher Score for Feature Selection
Fisher score is one of the most widely used supervised feature selection methods.
