Search Results for author:
Found 124 papers, 63 papers with code
Nested Named Entity Recognition with Span-level Graphs
In this work, we try to improve the span representation by utilizing retrieval-based span-level graphs, connecting spans and entities in the training data based on n-gram features.
CSPRD: A Financial Policy Retrieval Dataset for Chinese Stock Market
In recent years, great advances in pre-trained language models (PLMs) have sparked considerable research focus and achieved promising performance on the approach of dense passage retrieval, which aims at retrieving relative passages from massive corpus with given questions.
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
CodeApex comprises three types of multiple-choice questions: conceptual understanding, commonsense reasoning, and multi-hop reasoning, designed to evaluate LLMs on programming comprehension tasks.
ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently.
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases.
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances.
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items.
How Can Recommender Systems Benefit from Large Language Models: A Survey
For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i. e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller.
Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation
Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm.
MADiff: Offline Multi-agent Learning with Diffusion Models
MADiff is realized with an attention-based diffusion model to model the complex coordination among behaviors of multiple diffusion agents.
Refined Edge Usage of Graph Neural Networks for Edge Prediction
However, existing methods lack elaborate design regarding the distinctions between two tasks that have been frequently overlooked: (i) edges only constitute the topology in the node classification task but can be used as both the topology and the supervisions (i. e., labels) in the edge prediction task; (ii) the node classification makes prediction over each individual node, while the edge prediction is determinated by each pair of nodes.
Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer
Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i. e., sim-to-real transfer).
A Bird's-eye View of Reranking: from List Level to Page Level
Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists.
RITA: Boost Autonomous Driving Simulators with Realistic Interactive Traffic Flow
High-quality traffic flow generation is the core module in building simulators for autonomous driving.
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
In this paper, we present a general framework for benchmarking the degree of manipulations of recommendation algorithms, in both slate recommendation and sequential recommendation scenarios.
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning.
Multi-Scale User Behavior Network for Entire Space Multi-Task Learning
Modelling the user's multiple behaviors is an essential part of modern e-commerce, whose widely adopted application is to jointly optimize click-through rate (CTR) and conversion rate (CVR) predictions.
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers.
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives.
An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages
However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i. e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately.
A Graph-Enhanced Click Model for Web Search
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback.
Multi-Level Interaction Reranking with User Behavior History
MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list.
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization
Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions.
Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection
We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP.
Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation
With the prevalence of live broadcast business nowadays, a new type of recommendation service, called live broadcast recommendation, is widely used in many mobile e-commerce Apps.
Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data
More concretely, we first design a search-based module to retrieve a user's relevant historical behaviors, which are then mixed up with her recent records to be fed into a time-aware sequential network for capturing her time-sensitive demands.
Efficient Policy Space Response Oracles
Policy Space Response Oracle methods (PSRO) provide a general solution to learn Nash equilibrium in two-player zero-sum games but suffer from two drawbacks: (1) the computation inefficiency due to the need for consistent meta-game evaluation via simulations, and (2) the exploration inefficiency due to finding the best response against a fixed meta-strategy at every epoch.
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Neural architecture search (NAS) has shown encouraging results in automating the architecture design.
Generative Adversarial Exploration for Reinforcement Learning
Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel.
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation
While end-to-end neural machine translation (NMT) has achieved impressive progress, noisy input usually leads models to become fragile and unstable.
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback
Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user's information need so as to improve the search results.
On Effective Scheduling of Model-based Reinforcement Learning
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency.
Learning Logic Rules for Document-level Relation Extraction
LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor.
Ranked #21 on
Relation Extraction
on DocRED
AIM: Automatic Interaction Machine for Click-Through Rate Prediction
To address these three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), to select significant feature interactions, appropriate interaction functions and necessary embedding dimensions automatically in a unified framework.
Context-aware Reranking with Utility Maximization for Recommendation
As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.
Why Propagate Alone? Parallel Use of Labels and Features on Graphs
In this regard, it has recently been proposed to use a randomly-selected portion of the training labels as GNN inputs, concatenated with the original node features for making predictions on the remaining labels.
Inductive Relation Prediction Using Analogy Subgraph Embeddings
Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i. e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training.
Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning
Goal-oriented Reinforcement Learning (GoRL) is a promising approach for scaling up RL techniques on sparse reward environments requiring long horizon planning.
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization
State-only imitation learning (SOIL) enables agents to learn from massive demonstrations without explicit action or reward information.
Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction
To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.
Retrieval & Interaction Machine for Tabular Data Prediction
Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.
Learning to Select Cuts for Efficient Mixed-Integer Programming
In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12. 42% over the production solver without any accuracy loss of solution.
MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks
In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.
An Adversarial Imitation Click Model for Information Retrieval
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.
Bag of Tricks for Node Classification with Graph Neural Networks
Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Searching for novel molecules with desired chemical properties is crucial in drug discovery.
Universal Trading for Order Execution with Oracle Policy Distillation
As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.
Regioned Episodic Reinforcement Learning
Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration.
Non-iterative Parallel Text Generation via Glancing Transformer
Although non-autoregressive models with one-iteration generation achieves remarkable inference speed-up, they still falls behind their autoregressive counterparts inprediction accuracy.
Explore with Dynamic Map: Graph Structured Reinforcement Learning
In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation.
Improving Knowledge Tracing via Pre-training Question Embeddings
Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response.
Ranked #3 on
Knowledge Tracing
on EdNet
Towards Generalized Implementation of Wasserstein Distance in GANs
Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models.
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations.
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.
Efficient Projection-Free Algorithms for Saddle Point Problems
The Frank-Wolfe algorithm is a classic method for constrained optimization problems.
Model-based Policy Optimization with Unsupervised Model Adaptation
However, due to the potential distribution mismatch between simulated data and real data, this could lead to degraded performance.
AI Chiller: An Open IoT Cloud Based Machine Learning Framework for the Energy Saving of Building HVAC System via Big Data Analytics on the Fusion of BMS and Environmental Data
Although many research works and projects turn to this direction for energy saving, the application into the optimization problem remains a challenging task.
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
The heavy traffic congestion problem has always been a concern for modern cities.
GIKT: A Graph-based Interaction Model for Knowledge Tracing
With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.
Ranked #7 on
Knowledge Tracing
on EdNet
Glancing Transformer for Non-Autoregressive Neural Machine Translation
With GLM, we develop Glancing Transformer (GLAT) for machine translation.
Ranked #67 on
Machine Translation
on WMT2014 English-German
Bidirectional Model-based Policy Optimization
Model-based reinforcement learning approaches leverage a forward dynamics model to support planning and decision making, which, however, may fail catastrophically if the model is inaccurate.
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.
User Behavior Retrieval for Click-Through Rate Prediction
These retrieved behaviors are then fed into a deep model to make the final prediction instead of simply using the most recent ones.
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.
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
We propose adversarial uncertainty sampling in discrete space (AUSDS) to retrieve informative unlabeled samples more efficiently.
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip
In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme.
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.
Ranked #27 on
Click-Through Rate Prediction
on Criteo
Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency
Recent advances in large-scale optimal transport have greatly extended its application scenarios in machine learning.
Multi-Agent Interactions Modeling with Correlated Policies
In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents' policies, which can recover agents' policies that can regenerate similar interactions.
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available.
Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
Various sequential recommendation methods are proposed to model the dynamic user behaviors.
Exploring Diverse Expressions for Paraphrase Generation
Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification.
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Towards Making the Most of BERT in Neural Machine Translation
Our experiments in machine translation show CTNMT gains of up to 3 BLEU score on the WMT14 English-German language pair which even surpasses the previous state-of-the-art pre-training aided NMT by 1. 4 BLEU score.
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatiotemporal correlations, which vary from location to location and depend on the surrounding geographical information, e. g., points of interests and road networks.
Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence
Knowledge base is one of the main forms to represent information in a structured way.
Dynamically Fused Graph Network for Multi-hop Reasoning
However, many difficult questions require multiple supporting evidence from scattered text among two or more documents.
Ranked #36 on
Question Answering
on HotpotQA
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
Deep Landscape Forecasting for Real-time Bidding Advertising
The problem is formulated as to forecast the probability distribution of market price for each ad auction.
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.
Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs
Lipschitz continuity recently becomes popular in generative adversarial networks (GANs).
Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space
In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks.
Lipschitz Generative Adversarial Nets
By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem.
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport
CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation.
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance.
Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.
AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
Adam is shown not being able to converge to the optimal solution in certain cases.
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models.
Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance.
Deep Recurrent Survival Analysis
By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e., the probability of the non-occurrence of the event, for the censored data.
Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising
To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs.
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.
Path-Level Network Transformation for Efficient Architecture Search
We introduce a new function-preserving transformation for efficient neural architecture search.
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining.
Medical Named Entity Recognition
named-entity-recognition
+3
CoT: Cooperative Training for Generative Modeling of Discrete Data
In this paper, we study the generative models of sequential discrete data.
QA4IE: A Question Answering based Framework for Information Extraction
Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts.
Neural Text Generation: Past, Present and Beyond
This paper presents a systematic survey on recent development of neural text generation models.
Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising
From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes.
Unsupervised Deep Domain Adaptation for Pedestrian Detection
This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes.
Texygen: A Benchmarking Platform for Text Generation Models
We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.
Supervised Hashing based on Energy Minimization
Recently, supervised hashing methods have attracted much attention since they can optimize retrieval speed and storage cost while preserving semantic information.
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Face Transfer with Generative Adversarial Network
Face transfer animates the facial performances of the character in the target video by a source actor.
Long Text Generation via Adversarial Training with Leaked Information
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.
Ranked #1 on
Text Generation
on COCO Captions
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning.
Efficient Architecture Search by Network Transformation
Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results.
Ranked #143 on
Image Classification
on CIFAR-10
Learning to Design Games: Strategic Environments in Reinforcement Learning
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment.
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).
Activation Maximization Generative Adversarial Nets
Our proposed model also outperforms the baseline methods in the new metric.
Unsupervised Diverse Colorization via Generative Adversarial Networks
Colorization of grayscale images has been a hot topic in computer vision.
Real-Time Bidding by Reinforcement Learning in Display Advertising
In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set.
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
Ranked #1 on
Click-Through Rate Prediction
on iPinYou
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data.
Ranked #2 on
Text Generation
on Chinese Poems
A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia
We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB).
A Latent Clothing Attribute Approach for Human Pose Estimation
Unlike previous approaches, our approach models the clothing attributes as latent variables and thus requires no explicit labeling for the clothing attributes.
A Parallel and Efficient Algorithm for Learning to Match
In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization.
Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification
In this paper, we utilize structured learning to simultaneously address two intertwined problems: human pose estimation (HPE) and garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications.
Feature-Based Matrix Factorization
Recommender system has been more and more popular and widely used in many applications recently.
Robust Recovery of Subspace Structures by Low-Rank Representation
In this work we address the subspace recovery problem.
