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Found 35 papers, 9 papers with code
SATr: Slice Attention with Transformer for Universal Lesion Detection
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis.
DNN Training Acceleration via Exploring GPGPU Friendly Sparsity
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy.
MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds
The pipeline of most pointwise point cloud semantic segmentation methods includes points sampling, neighbor searching, feature aggregation, and classification.
Pareto Domain Adaptation
Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source.
Incentive Compatible Pareto Alignment for Multi-Source Large Graphs
Challenges of the problem include 1) how to align large-scale entities between sources to share information and 2) how to mitigate negative transfer from joint learning multi-source data.
Hierarchical Graph Networks for 3D Human Pose Estimation
Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton.
Context-aware Tree-based Deep Model for Recommender Systems
Vertically, a parent fusion layer is designed in M to transmit the user preference representation in higher levels of T to the current level, grasping the essence that tree-based methods are generating the candidate set from coarse to detail during the beam search retrieval.
Learning Slice-Aware Representations with Mixture of Attentions
Real-world machine learning systems are achieving remarkable performance in terms of coarse-grained metrics like overall accuracy and F-1 score.
Conditional Training with Bounding Map for Universal Lesion Detection
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis.
Ranked #2 on
Medical Object Detection
on DeepLesion
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration
Ensuring model robustness or resilience in the skill routing component is an important problem since skills may dynamically change their subscription in the ontology after the skill routing model has been deployed to production.
Phase Diagram of Triangular Lattice Quantum Ising Model under External Field
Quantum Ising model on a triangular lattice hosts a finite temperature Berezinskii-Kosterlitz-Thouless (BKT) phase with emergent U(1) symmetry, and it will transit into an up-up-down (UUD) phase with $C_3$ symmetry breaking upon an infinitesimal external field along the longitudinal direction, but the overall phase diagram spanned by the axes of external field and temperature remains opaque due to the lack of systematic invesitgations with controlled methodologies.
Strongly Correlated Electrons Statistical Mechanics
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
Bounding Maps for Universal Lesion Detection
The bounding maps (BMs) are used in two-stage anchor-based ULD frameworks to reduce the FP rate.
The Statistical Characteristics of Power-Spectrum Subband Energy Ratios under Additive Gaussian White Noise
When Gaussian white noise was mixed with the known signal, the resulting PSER followed a doubly non-central beta distribution.
Handling Rare Entities for Neural Sequence Labeling
One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases.
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
Learning Optimal Tree Models Under Beam Search
Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.
Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition
We hereby propose a decomposition generative adversarial network (DecGAN) to anatomically decompose a CXR image but with unpaired data.
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
We refer to this process as Leverage.
Spectral-based Graph Convolutional Network for Directed Graphs
Our approach can work directly on directed graph data in semi-supervised nodes classification tasks.
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features
Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company{'}s customer service agents have the option of escalating their complaints by filing grievances with a government authority.
Continuous Learning for Large-scale Personalized Domain Classification
This is a major component in mainstream IPDAs in industry.
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index.
Learning Adaptive Display Exposure for Real-Time Advertising
In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?
Deep Learning for Entity Matching: A Design Space Exploration
Entity matching (EM) finds data instances that refer to the same real-world entity.
Ranked #5 on
Entity Resolution
on Abt-Buy
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
Real-time advertising allows advertisers to bid for each impression for a visiting user.
Learning Tree-based Deep Model for Recommender Systems
In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult.
Toward a System Building Agenda for Data Integration
They provide tools to address the "pain points" of the steps, and tools are built on top of the Python data science and Big Data ecosystem (PyData).
Databases
Deep Interest Network for Click-Through Rate Prediction
In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.
Ranked #1 on
Click-Through Rate Prediction
on Amazon
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
Optimized Cost per Click in Taobao Display Advertising
Moreover, the platform has to be responsible for the business revenue and user experience.
