Search Results for author:
Found 90 papers, 27 papers with code
Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics
In this paper, we present an approach for scene understanding by reasoning physical stability of objects from point cloud.
Robust 3D Features for Matching between Distorted Range Scans Captured by Moving Systems
Our feature description is designed as two steps: 1) we normalize the detected local regions to canonical shapes for robust matching; 2) we encode each key point with multiple vectors at different Morse function values.
Chinese Grammatical Error Diagnosis with Long Short-Term Memory Networks
This paper introduces our Chinese Grammatical Error Diagnosis (CGED) system in the NLP-TEA-3 shared task for CGED.
The HIT-SCIR System for End-to-End Parsing of Universal Dependencies
Our system includes three pipelined components: \textit{tokenization}, \textit{Part-of-Speech} (POS) \textit{tagging} and \textit{dependency parsing}.
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation
This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies.
Ranked #3 on
Dependency Parsing
on Universal Dependencies
An AMR Aligner Tuned by Transition-based Parser
In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.
Ranked #2 on
AMR Parsing
on LDC2014T12:
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
A Minimax Game for Instance based Selective Transfer Learning
To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning.
A bi-diffusion based layer-wise sampling method for deep learning in large graphs
The Graph Convolutional Network (GCN) and its variants are powerful models for graph representation learning and have recently achieved great success on many graph-based applications.
RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search
Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM).
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations.
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or more entities inside the long answer).
Global Daily CO$_2$ emissions for the year 2020
That even substantial world-wide lockdowns of activity led to a one-time decline in global CO$_2$ emissions of only 5. 4% in one year highlights the significant challenges for climate change mitigation that we face in the post-COVID era.
Atmospheric and Oceanic Physics General Economics Economics
Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multiple Data Sources
Accordingly, a full-coverage high-resolution air pollutant dataset with timely updates and historical long-term records is essential to support both research and environmental management.
4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface
Tracking non-rigidly deforming scenes using range sensors has numerous applications including computer vision, AR/VR, and robotics.
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.
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.
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment
The cross-lingual language models are typically pretrained with masked language modeling on multilingual text or parallel sentences.
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.
Consistency Regularization for Cross-Lingual Fine-Tuning
Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others.
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA
In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training.
Ranked #1 on
Zero-Shot Cross-Lingual Transfer
on XTREME
Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network
Real-world video deblurring in real time still remains a challenging task due to the complexity of spatially and temporally varying blur itself and the requirement of low computational cost.
Ranked #34 on
Image Deblurring
on GoPro
(using extra training data)
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.
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.
Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training
We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity.
Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching
When speaking of sponsored search, bid keyword recommendation is the fundamental service.
Characterizing the adversarial vulnerability of speech self-supervised learning
As the paradigm of the self-supervised learning upstream model followed by downstream tasks arouses more attention in the speech community, characterizing the adversarial robustness of such paradigm is of high priority.
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
We further show that such a process is equivalent to adding an adversarial perturbation to the model input, and thereby name our proposed approach as an the Adversarial Gradient Driven Exploration (AGE).
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.
UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation
Then a student model is trained on both clicked and unclicked ads with knowledge distillation, performing uncertainty modeling to alleviate the inherent noise in pseudo-labels.
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
In previous work, researchers have developed several calibration methods to post-process the outputs of a predictor to obtain calibrated values, such as binning and scaling methods.
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
In this paper, we propose a novel method to extend ANN search to arbitrary matching functions, e. g., a deep neural network.
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.
BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis
Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics.
Ranked #1 on
Gesture Generation
on BEAT
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.
Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader Packing and Soft Rasterization
HardSoftRas, our novel rendering process, is designed for inverse rendering with a graphics pipeline.
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems
However, we observe that GPU devices in training recommender systems are underutilized, and they cannot attain an expected throughput improvement as what it has achieved in CV and NLP areas.
StableMoE: Stable Routing Strategy for Mixture of Experts
We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i. e., the target expert of the same input may change along with training, but only one expert will be activated for the input during inference.
Visual Encoding and Debiasing for CTR Prediction
In this paper, we present a visual encoding framework for CTR prediction to overcome these problems.
Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising
Calibration techniques aims to post-process model predictions to posterior probabilities.
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR).
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.
Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding
Current bundle generation studies focus on generating a combination of items to improve user experience.
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.
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems.
Adaptive Domain Interest Network for Multi-domain Recommendation
Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously.
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems.
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.
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
To alleviate this issue, we propose to extract knowledge from the \textit{super-domain} that contains web-scale and long-time impression data, and further assist the online recommendation task (downstream task).
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
For this purpose, we build a relatedness prediction network, so that it can predict the contrast strength for inter-task representations of an instance.
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
However, far less attention has been paid to the overfitting problem of models in recommendation systems, which, on the contrary, is recognized as a critical issue for deep neural networks.
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.
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression.
Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation
Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains.
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i. e., the forecasts should satisfy the hierarchical aggregation constraints.
HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding
Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling.
RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads
In a whole delivery period, advertisers usually desire a certain impression count for the ads, and they also expect that the delivery performance is as good as possible (e. g., obtaining high click-through rate).
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking
In this paper, we propose a Hybrid Contrastive Constrained approach (HC^2) for multi-scenario ad ranking.
Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning
Multi-task learning (MTL) has been widely applied in online advertising and recommender systems.
Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection
This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning
We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms.
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.
On Structural Expressive Power of Graph Transformers
Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed.
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.
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.
Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning
In this regard, we explore the problem of constrained bidding in adversarial bidding environments, which assumes no knowledge about the adversarial factors.
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.
Neural-Symbolic Recommendation with Graph-Enhanced Information
Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale.
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training.
Neuro-Symbolic Recommendation Model based on Logic Query
A recommendation system assists users in finding items that are relevant to them.
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images.
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.
Combating Bilateral Edge Noise for Robust Link Prediction
To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse.
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.
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling
We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements.
Ranked #1 on
3D Face Animation
on BEAT2
E^2-LLM: Efficient and Extreme Length Extension of Large Language Models
Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources.
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.
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs).
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
By conducting a detailed analysis of real multi-turn dialogue data, we construct a three-tier hierarchical ability taxonomy comprising 4208 turns across 1388 multi-turn dialogues in 13 distinct tasks.
SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction
Recent development of large language models (LLMs) has exhibited impressive zero-shot proficiency on generic and common sense questions.
AtomoVideo: High Fidelity Image-to-Video Generation
Recently, video generation has achieved significant rapid development based on superior text-to-image generation techniques.
Making Pre-trained Language Models Great on Tabular Prediction
Condensing knowledge from diverse domains, language models (LMs) possess the capability to comprehend feature names from various tables, potentially serving as versatile learners in transferring knowledge across distinct tables and diverse prediction tasks, but their discrete text representation space is inherently incompatible with numerical feature values in tables.
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.
Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation
The experimental results demonstrate the effectiveness of our approach in improving the fidelity of generated videos.
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Due to limited user interactions for each product (i. e. item), the corresponding item embedding in the CTR model may not easily converge.
Dr.Hair: Reconstructing Scalp-Connected Hair Strands without Pre-training via Differentiable Rendering of Line Segments
In the film and gaming industries, achieving a realistic hair appearance typically involves the use of strands originating from the scalp.
