Search Results for author:
Found 120 papers, 48 papers with code
Cross Attention Augmented Transducer Networks for Simultaneous Translation
This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Large Language Model based Long-tail Query Rewriting in Taobao Search
In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue.
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems.
Generative Input: Towards Next-Generation Input Methods Paradigm
The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task.
Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Sequential recommender systems (SRS) have gained widespread popularity in recommendation due to their ability to effectively capture dynamic user preferences.
Woodpecker: Hallucination Correction for Multimodal Large Language Models
Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content.
Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment
One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.
Large-Scale OD Matrix Estimation with A Deep Learning Method
To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints.
Interactive Graph Convolutional Filtering
Furthermore, we employ Bayesian meta-learning methods to effectively address the cold-start problem and derive theoretical regret bounds for our proposed method, ensuring a robust performance guarantee.
Identifiable Cognitive Diagnosis with Encoder-decoder for Modelling Students' Performance
Cognitive diagnosis aims to diagnose students' knowledge proficiencies based on their response scores on exam questions, which is the basis of many domains such as computerized adaptive testing.
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i. e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels.
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
Inspired by natural evolution, we propose a general Cognitive EvoLutionary Search (CELS) framework, where cognitive ability refers to the malleability of organisms to orientate to the environment.
Ranked #4 on
Click-Through Rate Prediction
on Avazu
When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities
The advent of large language models marks a revolutionary breakthrough in artificial intelligence.
Multi-Grained Multimodal Interaction Network for Entity Linking
Then, to derive the similarity matching score for each mention-entity pair, we device three interaction units to comprehensively explore the intra-modal interaction and inter-modal fusion among features of entities and mentions.
Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms
Machine learning algorithms have become ubiquitous in a number of applications (e. g. image classification).
A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence
To this end, this paper proposes an integrated method, which uses deep learning methods to infer the structure of OD sequence and uses structural constraints to guide traditional numerical optimization.
Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including the promotion of out-of-distribution (OOD) application.
A Solution to CVPR'2023 AQTC Challenge: Video Alignment for Multi-Step Inference
In this paper, we present a solution for enhancing video alignment to improve multi-step inference.
A Survey on Multimodal Large Language Models
Multimodal Large Language Model (MLLM) recently has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks.
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design
Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, coupled with the availability of accurate protein 3D structure predictions from tools like AlphaFold, have greatly advanced the field of structure-based drug design.
Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective
Different tests for different models using efficient adaptive testing -- we believe this has the potential to become a new norm in evaluating large language models.
Description-Enhanced Label Embedding Contrastive Learning for Text Classification
Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets.
Multi-Temporal Relationship Inference in Urban Areas
Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL).
Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.
A Survey on Large Language Models for Recommendation
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).
An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.
Quiz-based Knowledge Tracing
In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students' knowledge states according to their quiz-based learning interactions.
AU-aware graph convolutional network for Macro- and Micro-expression spotting
Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs.
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks.
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation
Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item.
Federated Nearest Neighbor Machine Translation
To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention.
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
Deep learning-based algorithms, e. g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task.
A Novel Approach for Auto-Formulation of Optimization Problems
In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem.
Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training
To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer.
More is Better: A Database for Spontaneous Micro-Expression with High Frame Rates
To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7, 526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years.
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation
Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various safety-critical applications.
Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF
Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research.
Medical Named Entity Recognition
named-entity-recognition
+3
One Person, One Model--Learning Compound Router for Sequential Recommendation
The core idea of CANet is to route the input user behaviors with a light-weighted router module.
Towards Robust Recommender Systems via Triple Cooperative Defense
Notably, TCD adds pseudo label data instead of deleting abnormal data, which avoids the cleaning of normal data, and the cooperative training of the three models is also beneficial to model generalization.
Model Inversion Attacks against Graph Neural Networks
One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns.
Multi-modal Siamese Network for Entity Alignment
To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect.
Ranked #7 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
Cooperative Retriever and Ranker in Deep Recommenders
Deep recommender systems (DRS) are intensively applied in modern web services.
Winning the CVPR'2022 AQTC Challenge: A Two-stage Function-centric Approach
Affordance-centric Question-driven Task Completion for Egocentric Assistant(AQTC) is a novel task which helps AI assistant learn from instructional videos and scripts and guide the user step-by-step.
Boosting Factorization Machines via Saliency-Guided Mixup
Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM).
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss.
Non-Parametric Domain Adaptation for End-to-End Speech Translation
End-to-End Speech Translation (E2E-ST) has received increasing attention due to the potential of its less error propagation, lower latency, and fewer parameters.
Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification
Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i. e., the part-of-speech tags and dependency relations).
Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction
To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i. e., preference topics generation, preference shift modeling, and social influence activation.
Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation
Such NWD can be coupled with the classifier to serve as a discriminator satisfying the K-Lipschitz constraint without the requirements of additional weight clipping or gradient penalty strategy.
Ranked #1 on
Domain Adaptation
on ImageCLEF-DA
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence.
Reinforcement Routing on Proximity Graph for Efficient Recommendation
Instead of searching the nearest neighbor for the query, we search the item with maximum inner product with query on the proximity graph.
Online Allocation Problem with Two-sided Resource Constraints
As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed.
VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction
With the booming of pre-trained transformers, representation-based models based on Siamese transformer encoders have become mainstream techniques for efficient text matching.
Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference.
Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering.
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency.
SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation
Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching
In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations.
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching
Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective.
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective
Usually, this prediction is always with great challenges to making a comprehensive understanding of both the start-up project and market environment.
A Survey of Knowledge Tracing
In contrast to traditional face-to-face classroom education, online education enables us to record and research a large amount of learning data for offering intelligent educational services.
XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner.
Ranked #21 on
Click-Through Rate Prediction
on Criteo
Towards Variable-Length Textual Adversarial Attacks
In this way, our approach is able to more comprehensively find adversarial examples around the decision boundary and effectively conduct adversarial attacks.
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
Text to speech (TTS) has been broadly used to synthesize natural and intelligible speech in different scenarios.
Drug Package Recommendation via Interaction-aware Graph Induction
Then, the drug interaction graph will be initialized based on medical records and domain knowledge.
Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis
Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making.
Learning the Implicit Semantic Representation on Graph-Structured Data
Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited.
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing
In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models.
Adam revisited: a weighted past gradients perspective
Though many algorithms, such as AMSGRAD and ADAMNC, have been proposed to fix the non-convergence issues, achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods.
R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching
Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences.
Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction
In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.
Sampling-Decomposable Generative Adversarial Recommender
The GAN-style recommenders (i. e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective.
Incorporating BERT into Parallel Sequence Decoding with Adapters
Our framework is based on a parallel sequence decoding algorithm named Mask-Predict considering the bi-directional and conditional independent nature of BERT, and can be adapted to traditional autoregressive decoding easily.
MMEA: Entity Alignment for Multi-Modal Knowledge Graphs
To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view.
Accuracy Prediction with Non-neural Model for Neural Architecture Search
Considering that most architectures are represented as sequences of discrete symbols which are more like tabular data and preferred by non-neural predictors, in this paper, we study an alternative approach which uses non-neural model for accuracy prediction.
Ranked #81 on
Neural Architecture Search
on ImageNet
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.
Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation
In this work, we introduce a jointly masked sequence-to-sequence model and explore its application on non-autoregressive neural machine translation{\textasciitilde}(NAT).
Lightrec: A memory and search-efficient recommender system
On top of such a structure, LightRec will have an item represented as additive composition of B codewords, which are optimally selected from each of the codebooks.
Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking
These 1D heatmaps reduce spatial complexity significantly compared to current heatmap regression methods, which use 2D heatmaps to represent the joint distributions of x and y coordinates.
Semi-Supervised Neural Architecture Search
On ImageNet, it achieves 23. 5% top-1 error rate (under 600M FLOPS constraint) using 4 GPU-days for search.
Ranked #81 on
Neural Architecture Search
on ImageNet
Domain Adaption for Knowledge Tracing
We refer to this problem as domain adaptation for knowledge tracing (DAKT) which contains two aspects: (1) how to achieve great knowledge tracing performance in each domain.
Deep Technology Tracing for High-tech Companies
Technological change and innovation are vitally important, especially for high-tech companies.
Variance Reduced Local SGD with Lower Communication Complexity
To accelerate the training of machine learning models, distributed stochastic gradient descent (SGD) and its variants have been widely adopted, which apply multiple workers in parallel to speed up training.
Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model
In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms.
Efficient Pure Exploration in Adaptive Round model
For PAC problem, we achieve optimal query complexity and use only $O(\log_{\frac{k}{\delta}}^*(n))$ rounds, which matches the lower bound of round complexity, while most of existing works need $\Theta(\log \frac{n}{k})$ rounds.
A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring
To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms.
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation
Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models.
Long-term Joint Scheduling for Urban Traffic
Recently, the traffic congestion in modern cities has become a growing worry for the residents.
Balanced One-shot Neural Architecture Optimization
One-shot NAS is proposed to reduce the expense but shows inferior performance against conventional NAS and is not adequately stable.
Is Heuristic Sampling Necessary in Training Deep Object Detectors?
In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors.
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method.
Residual Objectness for Imbalance Reduction
For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds.
Neural Cognitive Diagnosis for Intelligent Education Systems
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.
EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction
In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content.
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View
Recent studies have consistently given positive hints that morphology is helpful in enriching word embeddings.
Budgeted Policy Learning for Task-Oriented Dialogue Systems
This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents.
Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems
Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multi-facet domain effects in CQA are still underexplored.
Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
When making cloth decisions, people usually show preferences for different semantic attributes (e. g., the clothes with v-neck collar).
Ranked #2 on
Recommendation Systems
on Amazon Fashion
(using extra training data)
QuesNet: A Unified Representation for Heterogeneous Test Questions
Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way.
Transcribing Content from Structural Images with Spotlight Mechanism
Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly.
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention.
Exploiting Cognitive Structure for Adaptive Learning
Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e. g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items.
Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach
The wide spread use of online recruitment services has led to information explosion in the job market.
Bidirectional Generative Adversarial Networks for Neural Machine Translation
To address this issue and stabilize the GAN training, in this paper, we propose a novel Bidirectional Generative Adversarial Network for Neural Machine Translation (BGAN-NMT), which aims to introduce a generator model to act as the discriminator, whereby the discriminator naturally considers the entire translation space so that the inadequate training problem can be alleviated.
Style Transfer as Unsupervised Machine Translation
Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content.
Ranked #3 on
Unsupervised Text Style Transfer
on GYAFC
Neural Architecture Optimization
The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy.
Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
For example, there is still a lack of theories of convergence for SGD and its variants that use stagewise step size and return an averaged solution in practice.
Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations.
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Although the convergence rates of existing variants of ADAGRAD have a better dependence on the number of iterations under the strong convexity condition, their iteration complexities have a explicitly linear dependence on the dimensionality of the problem.
Joint Training for Neural Machine Translation Models with Monolingual Data
Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough.
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
In this paper, we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously.
Finding Theme Communities from Database Networks
Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities.
Stack-based Multi-layer Attention for Transition-based Dependency Parsing
Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection.
Chinese Poetry Generation with Planning based Neural Network
Chinese poetry generation is a very challenging task in natural language processing.
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Although these works have achieved certain success, they have neglected some important facts about knowledge graphs: (i) many relationships in knowledge graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many}, rather than simply \emph{one-to-one}; (ii) most head entities and tail entities in knowledge graphs come from very different semantic spaces.
Agent Behavior Prediction and Its Generalization Analysis
Then we prove a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain, which depends on both the Markovian parameters and the covering number of the function class compounded by the loss function for behavior prediction and the behavior prediction model.
Image Denoising and Inpainting with Deep Neural Networks
Our method achieves state-of-the-art performance in the image denoising task.
