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Found 21 papers, 4 papers with code
Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description
Emotion classification of mi-croblogs is a process of reading the content of microblogs and combining the semantics of emo-tion categories to understand whether it contains a certain emotion.
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping
However, when applied to the infrared destriping task, it becomes challenging for the vanilla auxiliary generator to consistently produce vertical noise under unsupervised constraints.
SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection
In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full scales.
ARCNet: An Asymmetric Residual Wavelet Column Correction Network for Infrared Image Destriping
Our neural model leverages a novel downsampler, residual haar discrete wavelet transform (RHDWT), stripe directional prior knowledge and data-driven learning to induce a model with enriched feature representation of stripe noise and background.
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
Fair comparisons based on nineteen famous large-scale instances (with 10, 000 to 10, 000, 000 cities) show that HDR is highly competitive against existing state-of-the-art TSP algorithms, in terms of both efficiency and solution quality.
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand.
On the Convergence of Fictitious Play: A Decomposition Approach
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in $n$-player games, which builds the foundation for modern multi-agent learning algorithms.
MVP-Human Dataset for 3D Human Avatar Reconstruction from Unconstrained Frames
In this paper, we consider a novel problem of reconstructing a 3D human avatar from multiple unconstrained frames, independent of assumptions on camera calibration, capture space, and constrained actions.
A Context-Integrated Transformer-Based Neural Network for Auction Design
One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue.
Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation
In the second stage, we design a mesh refinement transformer (MRT) to respectively refine each coarse reconstruction result via a self-attention mechanism.
Ranked #64 on
3D Human Pose Estimation
on 3DPW
(MPJPE metric)
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
The Empirical Revenue Maximization (ERM) is one of the most important price learning algorithms in auction design: as the literature shows it can learn approximately optimal reserve prices for revenue-maximizing auctioneers in both repeated auctions and uniform-price auctions.
Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
Furthermore, the paper provides a comprehensive comparison of student models with the benchmark model (decision tree) and the teacher model (gradient boosting trees) to quantify the fidelity and accuracy of the students' interpretations.
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
The results on the case study show that the machine-learning classifier, together with model-agnostic interpretation tools, provides valuable insights on travel mode switching behavior for different individuals and population segments.
Cost-Effective Incentive Allocation via Structured Counterfactual Inference
We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback.
Reinforcement Learning for Uplift Modeling
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response.
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
In other words, how to draw behavioral insights from the high-performance "black-box" machine-learning models remains largely unsolved in the field of travel behavior modeling.
Latent Dirichlet Allocation for Internet Price War
Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors.
Unsupervised Deep Multi-focus Image Fusion
Convolutional neural networks have recently been used for multi-focus image fusion.
Deep Keyframe Detection in Human Action Videos
Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background.
Directed Regression
When used to guide decisions, linear regression analysis typically involves estimation of regression coefficients via ordinary least squares and their subsequent use to make decisions.
