Search Results for author:
Found 106 papers, 36 papers with code
Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation
Collectively, we envision our method will become an important benchmark for evaluating Graph2Text methods and advance biomedical research for complex diseases.
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference
Knowledge graph inference has been studied extensively due to its wide applications.
Focus-Driven Contrastive Learning for Medical Question Summarization
Automatic medical question summarization can significantly help the system to understand consumer health questions and retrieve correct answers.
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios.
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting
This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past.
The Rise and Potential of Large Language Model Based Agents: A Survey
Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks.
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years.
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution.
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes.
Learning on Graphs under Label Noise
Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise.
Towards Semi-supervised Universal Graph Classification
To tackle class shifts, we estimate the certainty of unlabeled graphs using multiple subgraphs, which facilities the discovery of unlabeled data from unknown categories.
MolXPT: Wrapping Molecules with Text for Generative Pre-training
Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text.
Ranked #1 on
Molecular Property Prediction
on SIDER
TGNN: A Joint Semi-supervised Framework for Graph-level Classification
The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data.
A Diffusion model for POI recommendation
In this paper, we propose Diff-POI: a Diffusion-based model that samples the user's spatial preference for the next POI recommendation.
Learning Graph ODE for Continuous-Time Sequential Recommendation
Technically, GDERec is characterized by an autoregressive graph ordinary differential equation consisting of two components, which are parameterized by two tailored graph neural networks (GNNs) respectively to capture user preference from the perspective of hybrid dynamical systems.
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data
How to automatically synthesize natural-looking dance movements based on a piece of music is an incrementally popular yet challenging task.
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
In particular, GraphVF represents the first controllable geometry-aware, protein-specific molecule generation method, which can generate binding 3D molecules with tailored sub-structures and physio-chemical properties.
2D Human Pose Estimation with Explicit Anatomical Keypoints Structure Constraints
Recently, human pose estimation mainly focuses on how to design a more effective and better deep network structure as human features extractor, and most designed feature extraction networks only introduce the position of each anatomical keypoint to guide their training process.
TIER-A: Denoising Learning Framework for Information Extraction
Our framework consists of several neural models with identical structures.
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services.
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.
Ranked #5 on
Link Prediction
on UMLS
GLCC: A General Framework for Graph-Level Clustering
In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs.
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks
Meta-path, a sequence of node types and edge types, is the core technique to embed HINs.
Kernel-based Substructure Exploration for Next POI Recommendation
To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way.
Focus-Driven Contrastive Learniang for Medical Question Summarization
Automatic medical question summarization can significantly help the system to understand consumer health questions and retrieve correct answers.
Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor
Based on the feature extractor trained on J-TEXT, the disruption prediction model was transferred to EAST data with mere 20 discharges from EAST experiment.
Taxonomy and evolution predicting using deep learning in images
Molecular and morphological characters, as important parts of biological taxonomy, are contradictory but need to be integrated.
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration.
Ranked #8 on
Link Property Prediction
on ogbl-wikikg2
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
This problem is typically solved by using graph neural networks (GNNs), which yet rely on a large number of labeled graphs for training and are unable to leverage unlabeled graphs.
End-to-End Rubbing Restoration Using Generative Adversarial Networks
Specifically, we collect characters from the Zhang Menglong Bei and build up the first rubbing restoration dataset.
A Probabilistic Model-Based Robust Waveform Design for MIMO Radar Detection
This paper addresses robust waveform design for multiple-input-multiple-output (MIMO) radar detection.
Graphine: A Dataset for Graph-aware Terminology Definition Generation
Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation.
Generate & Rank: A Multi-task Framework for Math Word Problems
Math word problem (MWP) is a challenging and critical task in natural language processing.
Ranked #1 on
Math Word Problem Solving
on Math23K
Orthonormal Product Quantization Network for Scalable Face Image Retrieval
Experiments are conducted on four commonly-used face datasets under both seen and unseen identities retrieval settings.
DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
In this paper, we propose a novel disentangled heterogeneous graph attention network DisenHAN for top-$N$ recommendation, which learns disentangled user/item representations from different aspects in a heterogeneous information network.
Partial Feature Selection and Alignment for Multi-Source Domain Adaptation
By assuming that the source and target domains share consistent key feature representations and identical label space, existing studies on MSDA typically utilize the entire union set of features from both the source and target domains to obtain the feature map and align the map for each category and domain.
RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union
Benefiting from the dense representation of the range image, RangeIoUDet is entirely constructed based on 2D convolution, making it possible to have a fast inference speed.
FNAS: Uncertainty-Aware Fast Neural Architecture Search
Further, an architecture knowledge pool together with a block similarity function is proposed to utilize parameter knowledge and reduces the searching time by 2 times.
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior.
IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach
In this work, we propose a novel method for performing inertial aided navigation, by using deep neural networks (DNNs).
Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval
Recently, deep classwise hashing introduced a classwise loss supervised by class labels information alternatively; however, we find it still has its drawback.
Topologically protected valley-dependent quantum photonic circuits
Topological photonics has been introduced as a powerful platform for integrated optics, since it can deal with robust light transport, and be further extended to the quantum world.
Quantum Physics Optics
Time-dependent Clearance of Cyclosporine in Adult Renal Transplant Recipients: A Population Pharmacokinetic Perspective
The CL/F of the non-CGC haplotype carrier was 14. 4% lower than that of the CGC haplotype carrier at 3 months post operation.
$P-V$ criticality and Joule-Thomson Expansion of Hayward-AdS black holes in 4D Einstein-Gauss-Bonnet gravity
In this paper, the $P-V$ criticality and Joule-Thomson Expansion of Hayward-AdS black holes in 4D Einstein-Gauss-Bonnet gravity are studied in the extended phase space.
Action Detection
High Energy Physics - Theory
MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping
However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label.
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning
Further, a life-long knowledge pool together with a block similarity function is proposed to utilize the lifelong parameter knowledge and reduces the searching time by 2 times.
Tomographic imaging of complete quantum state of matter by ultrafast diffraction
With the ability to directly obtain the Wigner function and density matrix of photon states, quantum tomography (QT) has had a significant impact on quantum optics, quantum computing and quantum information.
Quantum Physics
Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction
The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image.
$K$-theoretic quasimap wall-crossing
In this paper, we prove a K-theoretic wall-crossing formula for $\epsilon$-stable quasimaps for all GIT targets in all genera.
Algebraic Geometry Mathematical Physics Mathematical Physics 14N35
Multi-agent Trajectory Prediction with Fuzzy Query Attention
Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning.
Partial FC: Training 10 Million Identities on a Single Machine
The experiment demonstrates no loss of accuracy when training with only 10\% randomly sampled classes for the softmax-based loss functions, compared with training with full classes using state-of-the-art models on mainstream benchmarks.
Ranked #2 on
Face Identification
on MegaFace
Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation
Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases.
MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion
Experimental results on the KITTI dataset demonstrate significant improvement in filtering false positive over the approach using only point cloud data.
New gedanken experiment on higher-dimensional asymptotically AdS Reissner-Nordström black hole
Viewing the negative cosmological constant as a dynamical quantity derived from the matter field, we study the weak cosmic censorship conjecture for the higher-dimensional asymptotically AdS Reissner-Nordstr\"om black hole.
General Relativity and Quantum Cosmology High Energy Physics - Theory
Snowmass 2021 LoI: Determination of cosmic ray properties in the local interstellar medium with all-sky anisotropy observations
Propagation of Galactic cosmic rays (CR) in the interstellar medium (ISM) is among the unsolved problems in particle astrophysics.
High Energy Astrophysical Phenomena
RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation
We present RangeRCNN, a novel and effective 3D object detection framework based on the range image representation.
Augmented Bi-path Network for Few-shot Learning
Finally, the model learns to compare global and local features separately, i. e., in two paths, before merging the similarities.
EasyQuant: Post-training Quantization via Scale Optimization
The 8 bits quantization has been widely applied to accelerate network inference in various deep learning applications.
Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration
The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks.
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications
Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation.
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare.
A Graph to Graphs Framework for Retrosynthesis Prediction
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a. k. a.
Ranked #14 on
Single-step retrosynthesis
on USPTO-50k
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.
Ranked #1 on
Molecular Graph Generation
on MOSES
Learning to Answer Ambiguous Questions with Knowledge Graph
In the task of factoid question answering over knowledge base, many questions have more than one plausible interpretation.
Predictive Multi-level Patient Representations from Electronic Health Records
Most related studies transform EHR data of a patient into a sequence of clinical events in temporal order and then use sequential models to learn patient representations for outcome prediction.
PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction
Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction.
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.
Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation
Rather than directly applying the LSTM model to the event sequences, our proposed model firstly aggregates heterogeneous clinical events in a short period and then captures temporal interactions of the aggregated representations with LSTM.
NoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN
Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks.
Neural Correction Model for Open-Domain Named Entity Recognition
In this work, to address the low precision and recall problems, we first utilize DBpedia as the source of distant supervision to annotate abstracts from Wikipedia and design a neural correction model trained with a human-annotated NER dataset, DocRED, to correct the false entity labels.
Ekar: An Explainable Method for Knowledge Aware Recommendation
Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations.
Ranked #1 on
Recommendation Systems
on Last.FM
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction
Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies.
Session-based Social Recommendation via Dynamic Graph Attention Networks
However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends.
Ranked #1 on
Recommendation Systems
on Douban
(NDCG metric)
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
Deep hashing models have been proposed as an efficient method for large-scale similarity search.
Combating Fake News: A Survey on Identification and Mitigation Techniques
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion.
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an SNN.
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.
Ranked #4 on
Click-Through Rate Prediction
on KDD12
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution
Evolution of deep learning shows that some algorithmic tricks are more durable , while others are not.
Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control
Despite its remarkable empirical success as a highly competitive branch of artificial intelligence, deep learning is often blamed for its widely known low interpretation and lack of firm and rigorous mathematical foundation.
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records.
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.
Human-computer conversation systems have attracted much attention in Natural Language Processing.
Towards Automated ICD Coding Using Deep Learning
Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis.
Diversifying Neural Conversation Model with Maximal Marginal Relevance
However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies.
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network.
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension
Document-level multi-aspect sentiment classification is an important task for customer relation management.
Syntax Aware LSTM model for Semantic Role Labeling
In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models.
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings
In this paper, we propose a simple and effective ensemble method to further boost the performances of neural models.
Syntax Aware LSTM Model for Chinese Semantic Role Labeling
As for semantic role labeling (SRL) task, when it comes to utilizing parsing information, both traditional methods and recent recurrent neural network (RNN) based methods use the feature engineering way.
Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices
With the rapid proliferation of Internet of Things and intelligent edge devices, there is an increasing need for implementing machine learning algorithms, including deep learning, on resource-constrained mobile embedded devices with limited memory and computation power.
Less is More: Learning Prominent and Diverse Topics for Data Summarization
With this reinforced random walk as a general process embedded in classical topic models, we obtain \textit{diverse topic models} that are able to extract the most prominent and diverse topics from data.
Context-aware Natural Language Generation with Recurrent Neural Networks
This paper studied generating natural languages at particular contexts or situations.
Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems
In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain.
Dialogue Session Segmentation by Embedding-Enhanced TextTiling
In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation.
Improving Color Constancy by Discounting the Variation of Camera Spectral Sensitivity
Most color constancy (CC) models have been designed to first estimate the illuminant color, which is then removed from the color biased image to obtain an image taken under white light, without the explicit consideration of CSS effect on CC.
World Knowledge as Indirect Supervision for Document Clustering
We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network.
Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction
In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths.
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate.
Visualizing Large-scale and High-dimensional Data
We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space.
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
Ranked #5 on
Node Classification
on Wikipedia
"Look Ma, No Hands!" A Parameter-Free Topic Model
We show that the new parameter can be further eliminated by two parameter-free treatments: either by monitoring the diversity among the discovered topics or by a weak supervision from users in the form of an exemplar topic.
