Search Results for author:
Found 94 papers, 24 papers with code
Smart Pacing for Effective Online Ad Campaign Optimization
In this paper, we propose a smart pacing approach in which the delivery pace of each campaign is learned from both offline and online data to achieve smooth delivery and optimal performance goals.
Lift-Based Bidding in Ad Selection
In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events.
Two-View Label Propagation to Semi-supervised Reader Emotion Classification
In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm.
Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval
In this paper, we propose a simple but effective semantic part-based weighting aggregation (PWA) for image retrieval.
Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
Without post-processing, Our IME layer achieves a boost in performance of state-of-the-art image retrieval methods with post-processing on most datasets, and needs less computational cost.
Ranked #1 on
Image Retrieval
on Paris6k
Semantic Frame Labeling with Target-based Neural Model
This paper explores the automatic learning of distributed representations of the target{'}s context for semantic frame labeling with target-based neural model.
Detecting Anomalies in Sequential Data with Higher-order Networks
A major branch of anomaly detection methods relies on dynamic networks: raw sequence data is first converted to a series of networks, then critical change points are identified in the evolving network structure.
Social and Information Networks Physics and Society
Advancing System Performance with Redundancy: From Biological to Artificial Designs
We envision that our theory would provide a framework for the future development of bio-inspired redundant artificial systems as well as assist the studies of the fundamental mechanisms governing various biological processes.
A Bio-inspired Redundant Sensing Architecture
However, this upper bound is hard to achieve in many man-made systems.
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint.
Locally Adaptive Learning Loss for Semantic Image Segmentation
We propose a novel locally adaptive learning estimator for enhancing the inter- and intra- discriminative capabilities of Deep Neural Networks, which can be used as improved loss layer for semantic image segmentation tasks.
Unsupervised Semantic-based Aggregation of Deep Convolutional Features
In this paper, we propose a simple but effective semantic-based aggregation (SBA) method.
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
The data involved in CTR prediction are typically multi-field categorical data, i. e., every feature is categorical and belongs to one and only one field.
Excavate Condition-invariant Space by Intrinsic Encoder
Our method excavates the space of intrinsic structure and semantic information by proposed self-supervised encoder loss.
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Despite the fact that nonlinear subspace learning techniques (e. g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization).
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising
With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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?
Adversarial Soft-detection-based Aggregation Network for Image Retrieval
Our weakly supervised ASDA method achieves state-of-the-art performance on most datasets.
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.
Attention-based 3D Convolutional Network for Alzheimer’s Disease Diagnosis and Biomarkers Exploration
Modern advancements in deep learning provide a powerful framework for disease classification based on neuroimaging data.
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
We refer to this process as Leverage.
A Deep Recurrent Survival Model for Unbiased Ranking
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.
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.
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.
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
GmFace: A Mathematical Model for Face Image Representation Using Multi-Gaussian
The GmNet is then designed using Gaussian functions as neurons, with parameters that correspond to each of the parameters of GmFace in order to transform the problem of GmFace parameter solving into a network optimization problem of GmNet.
Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network
Real-time cardiac cine MRI does not require ECG gating in the data acquisition and is more useful for patients who can not hold their breaths or have abnormal heart rhythms.
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.
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.
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction
Conventional nonlinear subspace learning techniques (e. g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination).
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
Calibrating User Response Predictions in Online Advertising
Due to the sparsity and latency of the user response behaviors such as clicks and conversions, traditional calibration methods may not work well in real-world online advertising systems.
Computation Resource Allocation Solution in Recommender Systems
On the other hand, the response time of these systems is strictly limited to a short period, e. g. 300 milliseconds in our real system, which is also being exhausted by the increasingly complex models and algorithms.
A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control
This enables the implementation of the neuroprosthetic hand as a portable and self-contained unit with real-time control of individual finger movements.
Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction
To the best of our knowledge, this is the first work to evaluate the cine MRI with deep learning reconstruction for cardiac function analysis and compare it with other conventional methods.
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning
We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI.
Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction
Most of the existing methods adopt a DNN-based model to capture the cross features in an implicit manner.
Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted.
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Semantic Similarity
on CHIP-STS
Binary Code based Hash Embedding for Web-scale Applications
In these applications, embedding learning of categorical features is crucial to the success of deep learning models.
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer
Each feature value is mapped to an embedding vector via an embedding learning process.
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
To this end, previous work either makes use of auxiliary data at parameter server to verify the received gradients (e. g., by computing validation error rate) or leverages statistic-based methods (e. g. median and Krum) to identify and remove malicious gradients from Byzantine clients.
Learning Continuous Face Representation with Explicit Functions
First, we propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms, where each term is an analytic function element.
Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching
When speaking of sponsored search, bid keyword recommendation is the fundamental service.
ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimation
ACR-Pose consists of a Reconstructor and a Discriminator.
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling
Advertisers play an essential role in many e-commerce platforms like Taobao and Amazon.
Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction
Moreover, to estimate CVR upon the freshly observed but biased distribution with fake negatives, the importance sampling is widely used to reduce the distribution bias.
Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval
We find that the performance of retrieval models trained on dataset from general domain will inevitably decrease on specific domain.
Impression Allocation and Policy Search in Display Advertising
In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable.
Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface
Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines.
TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed
In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training.
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System
Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval.
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
In many web applications, deep learning-based CTR prediction models (deep CTR models for short) are widely adopted.
Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image
Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications.
Visual Encoding and Debiasing for CTR Prediction
In this paper, we present a visual encoding framework for CTR prediction to overcome these problems.
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model
High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distributed training modes for recommendation models.
Hierarchically Constrained Adaptive Ad Exposure in Feeds
To this end, adaptive ad exposure has become an appealing strategy to boost the overall performance of the feed.
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization
Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention in many areas, especially in the user growth area which concerns many internet companies.
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval
To overcome these challenges, we propose a novel federated hashing framework that enables participating clients to jointly train the shared deep hashing model by leveraging the prototypical hash codes for each class.
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability.
Sustainable Online Reinforcement Learning for Auto-bidding
Due to safety concerns, it was believed that the RL training process can only be carried out in an offline virtual advertising system (VAS) that is built based on the historical data generated in the RAS.
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout
The label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning, which is particularly developed for collaborative model training over decentralized data sources while preserving user privacy.
POEM: Reconstructing Hand in a Point Embedded Multi-view Stereo
Enable neural networks to capture 3D geometrical-aware features is essential in multi-view based vision tasks.
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions.
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
Another line of work relies on costly uniform data that is inadequate to train industrial models.
COPR: Consistency-Oriented Pre-Ranking for Online Advertising
In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements.
Personalized Federated Learning with Feature Alignment and Classifier Collaboration
Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients.
Multi-Scenario Ranking with Adaptive Feature Learning
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.
CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation
This can be attributed to the fact that humans have mastered the shape prior of the 'mug' category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located.
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing Flows
Bayesian inference for these models has been extensively studied and applied in tasks such as time series prediction.
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
Furthermore, our method guarantees theoretically controlled prediction error for DGP models and demonstrates remarkable performance on various datasets.
Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction
Although previous methods attempt to address these challenges, they overlook the interference of event-unrelated sentences during event detection and neglect the mutual interference of different event roles during argument extraction.
Robust Representation Learning for Unified Online Top-K Recommendation
The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.
Sparse Variational Student-t Processes
However, despite Student-t Processes having a similar computational complexity as Gaussian Processes, there has been limited emphasis on the sparse representation of this model.
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.
A Comprehensive Overview of Fish-Eye Camera Distortion Correction Methods
The fisheye camera, with its unique wide field of view and other characteristics, has found extensive applications in various fields.
A Wind-Aware Path Planning Method for UAV-Asisted Bridge Inspection
In response to the gap in considering wind conditions in the bridge inspection using unmanned aerial vehicle (UAV) , this paper proposes a path planning method for UAVs that takes into account the influence of wind, based on the simulated annealing algorithm.
Scalable Virtual Valuations Combinatorial Auction Design by Combining Zeroth-Order and First-Order Optimization Method
Subsequently, we propose a novel optimization method that combines both zeroth-order and first-order techniques to optimize the VVCA parameters.
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
The trained policy can subsequently be deployed for further data collection, resulting in an iterative training framework, which we refer to as iterative offline RL.
MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising
Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7. 01% lift in Gross Merchandise Volume, a 7. 42% lift in Return on Investment, and a 3. 26% lift in ad buy count.
Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI
Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring.
Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI
The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processing time poses a challenge.
