Search Results for author:
Found 388 papers, 153 papers with code
Optimality of Graphlet Screening in High Dimensional Variable Selection
Compared to m-variate brute-forth screening that has a computational cost of p^m, the GS only has a computational cost of p (up to some multi-log(p) factors) in screening.
Distance function of D numbers
A distance function of D numbers is proposed to measure the distance between two D numbers.
100+ Times Faster Weighted Median Filter (WMF)
Weighted median, in the form of either solver or filter, has been employed in a wide range of computer vision solutions for its beneficial properties in sparsity representation.
2D Semantic Segmentation task 1 (8 classes)
Optical Flow Estimation
+2
Multiscale probability transformation of basic probability assignment
In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the plausibility function is proposed, which is a generalization of the pignistic probability transformation.
Learning Word Embeddings from Intrinsic and Extrinsic Views
To prove the effectiveness of our model, we evaluate it on four tasks, including word similarity, reverse dictionaries, Wiki link prediction, and document classification.
Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention
By incorporating the hierarchical attention mechanism, the relative improvement in the proposed method over the state-of-the-art method is around 67. 9{\%} in the F1-score.
Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making
In cooperative multiagent planning, it can often be beneficial for an agent to make commitments about aspects of its behavior to others, allowing them in turn to plan their own behaviors without taking the agent's detailed behavior into account.
Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network
We investigate the task of open domain opinion relation extraction.
4D Light Field Superpixel and Segmentation
Superpixel segmentation of 2D image has been widely used in many computer vision tasks.
Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
In this work, we study the problem of part-of-speech tagging for Tweets.
Ranked #3 on
Part-Of-Speech Tagging
on Ritter
A Learning Error Analysis for Structured Prediction with Approximate Inference
In this work, we try to understand the differences between exact and approximate inference algorithms in structured prediction.
Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors
In this paper, the line spectral estimation (LSE) problem with multiple measurement vectors (MMVs) is studied utilizing the Bayesian methods.
Information Theory Information Theory
Hybrid Point Cloud Attribute Compression Using Slice-based Layered Structure and Block-based Intra Prediction
In intra-frame compression of point cloud color attributes, results demonstrate that our method performs better than the state-of-the-art region-adaptive hierarchical transform (RAHT) system, and on average a 29. 37$\%$ BD-rate gain is achieved.
Multimedia
Dynamic Feature Learning for Partial Face Recognition
Partial face recognition (PFR) in unconstrained environment is a very important task, especially in video surveillance, mobile devices, etc.
Cross-Domain Sentiment Classification with Target Domain Specific Information
And we introduce a few target domain labeled data for learning domain-specific information.
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis
Attention mechanisms have been leveraged for sentiment classification tasks because not all words have the same importance.
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model
In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments.
A Generic Multi-Projection-Center Model and Calibration Method for Light Field Cameras
Based on the MPC model and projective transformation, we propose a calibration algorithm to verify our light field camera model.
Vector Approximate Message Passing Algorithm for Structured Perturbed Sensing Matrix
In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing matrix is not precisely known.
Signal Processing
Predicting protein inter-residue contacts using composite likelihood maximization and deep learning
We further present successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset.
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging
However, from a linguistic perspective, Twitter users not only tend to mimic the formal expressions of traditional media, like news, but they also appear to be developing linguistically informal styles.
Ranked #2 on
Part-Of-Speech Tagging
on Ritter
A Survey: Non-Orthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC
One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases.
A Comparative Measurement Study of Deep Learning as a Service Framework
Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices.
Long Short-Term Memory with Dynamic Skip Connections
In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length.
Ranked #33 on
Sentiment Analysis
on IMDb
Anisotropic structural dynamics of monolayer crystals revealed by femtosecond surface x-ray scattering
X-ray scattering is one of the primary tools to determine crystallographic configuration with atomic accuracy.
Materials Science
Learning Fast Matching Models from Weak Annotations
The proposed training framework targets on mitigating both issues, by treating the stronger but undeployable models as annotators, and learning a deployable model from both human provided relevance labels and weakly annotated search log data.
A Unified Framework for Marketing Budget Allocation
In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task.
Sogou Machine Reading Comprehension Toolkit
In this paper, we present a Sogou Machine Reading Comprehension (SMRC) toolkit that can be used to provide the fast and efficient development of modern machine comprehension models, including both published models and original prototypes.
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision
This paper provides a new way to improve the efficiency of the REINFORCE training process.
Authenticated Key-Value Stores with Hardware Enclaves
To circumvent the limited enclave memory (128 MB with the latest Intel CPUs), we propose to place the memory buffer of the eLSM store outside the enclave and protect the buffer using a new authenticated data structure by digesting individual LSM-tree levels.
Cryptography and Security Databases Distributed, Parallel, and Cluster Computing Data Structures and Algorithms
Address Instance-level Label Prediction in Multiple Instance Learning
Existent approaches in this field were mainly designed for the bag-level label prediction (predict labels for bags) but not the instance-level (predict labels for instances), with the task loss being only defined at the bag level.
Learning Task-specific Representation for Novel Words in Sequence Labeling
However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance.
Ray-Space Projection Model for Light Field Camera
A homogeneous ray-space projection matrix and a fundamental matrix are then proposed to establish ray-ray correspondences among multiple light fields.
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs
Crowd counting in single-view images has achieved outstanding performance on existing counting datasets.
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries.
Seq-SetNet: Exploring Sequence Sets for Inferring Structures
Therefore, the traditional deep neural networks designed for image processing cannot be directly applied on sequence sets.
Multiple Sequence Alignment
Protein Secondary Structure Prediction
+1
Time-Series Anomaly Detection Service at Microsoft
At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time.
Joint State Estimation Under Attack of Discrete Event Systems
The aim of the attacker is that of altering the state estimation of the operator.
Cryptography and Security Formal Languages and Automata Theory
Image Super-Resolution Using a Wavelet-based Generative Adversarial Network
Existing work like Super-Resolution Using a Generative Adversarial Network (SRGAN) can effectively restore the texture details of the image.
Person Re-identification in Aerial Imagery
Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently.
Simplify the Usage of Lexicon in Chinese NER
This method avoids designing a complicated sequence modeling architecture, and for any neural NER model, it requires only subtle adjustment of the character representation layer to introduce the lexicon information.
Ranked #8 on
Chinese Named Entity Recognition
on Resume NER
Chinese Named Entity Recognition
named-entity-recognition
+2
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs).
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Self-driving scale car trained by Deep reinforcement learning
To improve the generalization ability for the driving behavior, the reinforcement learning method requires extrinsic reward from the real environment, which may damage the car.
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis
To address this problem, we propose a modification to DIRL, obtaining a novel weighted domain-invariant representation learning (WDIRL) framework.
A Multi-Task Learning Framework for Extracting Bacteria Biotope Information
This paper presents a novel transfer multi-task learning method for Bacteria Biotope rel+ner task at BioNLP-OST 2019.
A Lexicon-Based Graph Neural Network for Chinese NER
Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success.
Ranked #13 on
Chinese Named Entity Recognition
on OntoNotes 4
Chinese Named Entity Recognition
named-entity-recognition
+3
Asynchronous Deep Interaction Network for Natural Language Inference
However, in the process of reasoning, the role of the two sentences is obviously different, and the sentence pairs for NLI are asymmetrical corpora.
Discrete Argument Representation Learning for Interactive Argument Pair Identification
In this paper, we focus on extracting interactive argument pairs from two posts with opposite stances to a certain topic.
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication
Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically.
Ranked #9 on
Visual Storytelling
on VIST
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features
In order to effectively reduce the impact of non-ideal auxiliary tasks on the main task, we further proposed a novel meta-learning-based multi-task learning approach, which trained the shared hidden layers on auxiliary tasks, while the meta-optimization objective was to minimize the loss on the main task, ensuring that the optimizing direction led to an improvement on the main task.
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.
Grid-less Variational Direction of Arrival Estimation in Heteroscedastic Noise Environment
This paper studies DOA in heteroscedastic noise (HN) environment, where the variance of noise is varied across the snapshots and the antennas.
Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study
While neural network-based models have achieved impressive performance on a large body of NLP tasks, the generalization behavior of different models remains poorly understood: Does this excellent performance imply a perfect generalization model, or are there still some limitations?
3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels
Unlike MVMS, we propose to solve the multi-view crowd counting task through 3D feature fusion with 3D scene-level density maps, instead of the 2D ground-plane ones.
Spatio-Temporal Graph Structure Learning for Traffic Forecasting
To address these issues, we propose a novel framework named Structure Learning Convolution (SLC) that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting.
Ranked #3 on
Traffic Prediction
on METR-LA
Storytelling from an Image Stream Using Scene Graphs
In particular, on the within-image level, we employ a Graph Convolution Network (GCN) to enrich local fine-grained region representations of objects on scene graphs.
Ranked #7 on
Visual Storytelling
on VIST
Context-Aware Attention Network for Image-Text Retrieval
As a typical cross-modal problem, image-text bi-directional retrieval relies heavily on the joint embedding learning and similarity measure for each image-text pair.
ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion
The release of ReCO consists of 300k questions that to our knowledge is the largest in Chinese reading comprehension.
Single-Frame based Deep View Synchronization for Unsynchronized Multi-Camera Surveillance
To handle the issue of unsynchronized multi-cameras, in this paper, we propose a synchronization model that works in conjunction with existing DNN-based multi-view models, thus avoiding the redesign of the whole model.
Bitcoin Transaction Forecasting with Deep Network Representation Learning
First, we explore three interesting properties between Bitcoin transaction accounts: topological connectivity pattern of Bitcoin accounts, transaction amount pattern, and transaction dynamics.
Two Step Joint Model for Drug Drug Interaction Extraction
When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction.
Drug–drug Interaction Extraction
named-entity-recognition
+4
Multivariate Time-series Anomaly Detection via Graph Attention Network
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
We have publicly released the benchmarking code, evaluation protocols, and hyper-parameter settings of our work to promote reproducible research in this field.
Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis
Based on the SemEval 2014 dataset, we construct the Aspect Robustness Test Set (ARTS) as a comprehensive probe of the aspect robustness of ABSA models.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization
This work addresses inverse linear optimization where the goal is to infer the unknown cost vector of a linear program.
Multi-Constitutive Neural Network for Large Deformation Poromechanics Problem
We propose a novel method "multi-constitutive neural network" (MCNN) such that one model can solve several different constitutive laws.
Succinct and Robust Multi-Agent Communication With Temporal Message Control
Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons
In this work, we explore the way to quickly adjust an existing named entity recognition (NER) system to make it capable of recognizing entity types not defined in the system.
RethinkCWS: Is Chinese Word Segmentation a Solved Task?
The performance of the Chinese Word Segmentation (CWS) systems has gradually reached a plateau with the rapid development of deep neural networks, especially the successful use of large pre-trained models.
Modeling Evolution of Message Interaction for Rumor Resolution
Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately.
Wide-Area Crowd Counting: Multi-View Fusion Networks for Counting in Large Scenes
We consider three versions of the fusion framework: the late fusion model fuses camera-view density map; the naive early fusion model fuses camera-view feature maps; and the multi-view multi-scale early fusion model ensures that features aligned to the same ground-plane point have consistent scales.
Study of $qqq\bar{q}Q$ pentaquark system in the Chiral Quark Model
With the discovery of some hidden-charm pentaquark resonances by the LHCb Collaboration, investigations of pentaquark states containing heavy quarks have aroused the interest of theorists.
High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Lattice
SenSeNet: Neural Keyphrase Generation with Document Structure
Keyphrase Generation (KG) is the task of generating central topics from a given document or literary work, which captures the crucial information necessary to understand the content.
Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling
In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics.
Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders
Automatic chat summarization can help people quickly grasp important information from numerous chat messages.
Efficient Querying for Cooperative Probabilistic Commitments
Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions.
Uncertainty-Aware Label Refinement for Sequence Labeling
Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks.
Discovery of unconventional chiral charge order in kagome superconductor KV3Sb5
Here we use high-resolution scanning tunnelling microscopy (STM) to discover an unconventional charge order in a kagome material KV3Sb5, with both a topological band structure and a superconducting ground state.
Superconductivity Materials Science Strongly Correlated Electrons
Thermodynamic properties of ε-Fe with thermal electronic excitation effects on vibrational spectra
The thermodynamic properties of hcp-iron ({\epsilon}-Fe) are essential for investigating planetary cores' internal structure and dynamic properties.
Materials Science Computational Physics
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
To further employ the power of quantization, the mixed precision technique is incorporated in our framework by approximating the inter-layer and intra-layer sensitivity.
Knowledge Infused Policy Gradients for Adaptive Pandemic Control
To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities
Our proposed framework creates 1x1 convolution layers to strengthen the learning of cross-feature spatial interaction between air pollution and important urban dynamic features, particularly road density, building density/height, and street canyon effect.
Larger-Context Tagging: When and Why Does It Work?
The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks.
Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure
Numerous online conversations are produced on a daily basis, resulting in a pressing need to conversation understanding.
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning
In response to this problem, we propose a new fine-grained evaluation metric to improve the RL framework, which considers different granularities: token-level $F_1$ score, edit distance, duplication, and prediction quantities.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning
Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustment of inference time.
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.
Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead
Deploying deep learning models in time-critical applications with limited computational resources, for instance in edge computing systems and IoT networks, is a challenging task that often relies on dynamic inference methods such as early exiting.
One2Set: Generating Diverse Keyphrases as a Set
In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases.
SENT: Sentence-level Distant Relation Extraction via Negative Training
In this work, we propose the use of negative training (NT), in which a model is trained using complementary labels regarding that ``the instance does not belong to these complementary labels".
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits
Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc.
Multi-Exit Vision Transformer for Dynamic Inference
In this work, we propose seven different architectures for early exit branches that can be used for dynamic inference in Vision Transformer backbones.
Sleep Staging Based on Multi Scale Dual Attention Network
In the literature, a large number of sleep staging methods based on single-channel EEG have been proposed with promising results and achieve the preliminary automation of sleep staging.
MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation
Due to the increasing concerns for data privacy, source-free unsupervised domain adaptation attracts more and more research attention, where only a trained source model is assumed to be available, while the labeled source data remain private.
Math Word Problem Solving with Explicit Numerical Values
In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration.
MeDiaQA: A Question Answering Dataset on Medical Dialogues
In this paper, we introduce MeDiaQA, a novel question answering(QA) dataset, which constructed on real online Medical Dialogues.
UNBERT: User-News Matching BERT for News Recommendation
Nowadays, news recommendation has become a popular channel for users to access news of their interests.
A Partition Filter Network for Joint Entity and Relation Extraction
In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition.
Ranked #1 on
Relation Extraction
on ADE Corpus
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining
With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization.
Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems
Human dialogue contains evolving concepts, and speakers naturally associate multiple concepts to compose a response.
Heterogeneous Graph Neural Networks for Keyphrase Generation
The encoder-decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not.
A Relation-Oriented Clustering Method for Open Relation Extraction
The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE).
Ranked #1 on
Relation Extraction
on FewRel
Why Do We Click: Visual Impression-aware News Recommendation
In this work, inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news, we propose to capture such visual impression information with visual-semantic modeling for news recommendation.
Template-free Prompt Tuning for Few-shot NER
Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words.
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Chaos is a Ladder: A New Understanding of Contrastive Learning
Our work suggests an alternative understanding of contrastive learning: the role of aligning positive samples is more like a surrogate task than an ultimate goal, and it is the overlapping augmented views (i. e., the chaos) that create a ladder for contrastive learning to gradually learn class-separated representations.
Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis
It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses.
Dimension Reduction for Fréchet Regression
In this paper, we introduce a flexible sufficient dimension reduction (SDR) method for Fr\'echet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors and to provide a visual inspection tool for Fr\'echet regression.
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System
In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation.
Disarranged Zone Learning (DZL): An unsupervised and dynamic automatic stenosis recognition methodology based on coronary angiography
Hence, the prediction accuracy of DZL is used as an approximator of coronary stenosis indicator.
DRL-Clusters: Buffer Management with Clustering based Deep Reinforcement Learning
Buffer cache has been widely implemented in database systems to reduce disk I/Os.
Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models
Specifically, for each task, a label word set is first constructed by selecting a high-frequency word for each class respectively, and then, task-specific vectors are inserted into the inputs and optimized to manipulate the model predictions towards the corresponding label words.
Improving Non-autoregressive Generation with Mixup Training
While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge.
Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training
Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product reviews.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Surprisingly, no existing algorithm wins every challenge in MQBench, and we hope this work could inspire future research directions.
Deblur-NeRF: Neural Radiance Fields from Blurry Images
We demonstrate that our method can be used on both camera motion blur and defocus blur: the two most common types of blur in real scenes.
HDR-NeRF: High Dynamic Range Neural Radiance Fields
The key to our method is to model the physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper.
Hallucinated Neural Radiance Fields in the Wild
This paper studies the problem of hallucinated NeRF: i. e., recovering a realistic NeRF at a different time of day from a group of tourism images.
FENeRF: Face Editing in Neural Radiance Fields
2D GANs can generate high fidelity portraits but with low view consistency.
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning
Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data.
PromptBERT: Improving BERT Sentence Embeddings with Prompts
We propose PromptBERT, a novel contrastive learning method for learning better sentence representation.
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification.
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
On the other hand, the capability of making high-CTR retrieval is optimized by learning to discriminate user's clicked ads from the entire corpus.
Post-Training Quantization for Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective
Most of the existing debiasing methods often identify and weaken these samples with biased features (i. e., superficial surface features that cause such spurious correlations).
HousE: Knowledge Graph Embedding with Householder Parameterization
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.
Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized training and policy sharing.
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
The design variables consist of a set of reactants in a reaction network and its network topology.
An Effective Way for Cross-Market Recommendation with Hybrid Pre-Ranking and Ranking Models
The Cross-Market Recommendation task of WSDM CUP 2022 is about finding solutions to improve individual recommendation systems in resource-scarce target markets by leveraging data from similar high-resource source markets.
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents
Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap
Our theory suggests an alternative understanding of contrastive learning: the role of aligning positive samples is more like a surrogate task than an ultimate goal, and the overlapped augmented views (i. e., the chaos) create a ladder for contrastive learning to gradually learn class-separated representations.
UV Volumes for Real-time Rendering of Editable Free-view Human Performance
Neural volume rendering enables photo-realistic renderings of a human performer in free-view, a critical task in immersive VR/AR applications.
CMMD: Cross-Metric Multi-Dimensional Root Cause Analysis
To this end, we propose a cross-metric multi-dimensional root cause analysis method, named CMMD, which consists of two key components: 1) relationship modeling, which utilizes graph neural network (GNN) to model the unknown complex calculation among metrics and aggregation function among dimensions from historical data; 2) root cause localization, which adopts the genetic algorithm to efficiently and effectively dive into the raw data and localize the abnormal dimension(s) once the KPI anomalies are detected.
Stereo Unstructured Magnification: Multiple Homography Image for View Synthesis
A two-stage network is developed for novel view synthesis.
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
We perform comprehensive explorations for the optimal conduct of knowledge distillation, which may provide useful insights for the learning of VQ based ANN index.
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation
Pixel synthesis is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation.
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective
NER model has achieved promising performance on standard NER benchmarks.
Ranked #8 on
Named Entity Recognition (NER)
on WNUT 2017
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience.
Multi-view Information Bottleneck Without Variational Approximation
By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks.
Process Knowledge-infused Learning for Suicidality Assessment on Social Media
Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world.
Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array
In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP.
Cross-View Cross-Scene Multi-View Crowd Counting
Multi-view crowd counting has been previously proposed to utilize multi-cameras to extend the field-of-view of a single camera, capturing more people in the scene, and improve counting performance for occluded people or those in low resolution.
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations.
Dynamic Split Computing for Efficient Deep Edge Intelligence
Split computing is a paradigm where a DNN is split into two sections; the first section is executed on the end device, and the output is transmitted to the edge server where the final section is executed.
Enhancing Event-Level Sentiment Analysis with Structured Arguments
Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e. g., subject, object, time and location) that have potential effects on the sentiment are not well studied.
A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis
Notably, we propose a Knowledge-Enhanced Adversarial Model (\texttt{KEAM}) with both implicit distributed and explicit structural knowledge to enhance the cross-lingual transfer.
A Neural Corpus Indexer for Document Retrieval
To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query.
Searching for Optimal Subword Tokenization in Cross-domain NER
Input distribution shift is one of the vital problems in unsupervised domain adaptation (UDA).
A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning
We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty.
Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games
Our focus in this paper is to study the convergence of the policy gradient method for solving MPGs under softmax policy parameterization, both tabular and parameterized with general function approximators such as neural networks.
Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning
Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is a trending yet challenging and computationally expensive task in the real world.
Neural Parameterization for Dynamic Human Head Editing
Implicit radiance functions emerged as a powerful scene representation for reconstructing and rendering photo-realistic views of a 3D scene.
Nonlinear Sufficient Dimension Reduction for Distribution-on-Distribution Regression
We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are distributional data, modeled as members of a metric space.
Neural Color Operators for Sequential Image Retouching
The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar.
Unifying Event Detection and Captioning as Sequence Generation via Pre-Training
Dense video captioning aims to generate corresponding text descriptions for a series of events in the untrimmed video, which can be divided into two sub-tasks, event detection and event captioning.
Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing.
Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
To reduce the computation time and communication overhead, we propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks of convolutional layers.
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.
Efficient High-Resolution Deep Learning: A Survey
Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos.
A Semantic Alignment System for Multilingual Query-Product Retrieval
Our models are all trained with cross-entropy loss to classify the query-product pairs into ESCI 4 categories at first, and then we use weighted sum with the 4-class probabilities to get the score for ranking.
Exploring Anchor-based Detection for Ego4D Natural Language Query
In this paper we provide the technique report of Ego4D natural language query challenge in CVPR 2022.
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training
JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings.
Causal Intervention Improves Implicit Sentiment Analysis
In this work, we propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV).
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering
In this paper, we propose an interpretable stepwise reasoning framework to incorporate both single-hop supporting sentence identification and single-hop question generation at each intermediate step, and utilize the inference of the current hop for the next until reasoning out the final result.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck
Event argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing.
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting
Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time.
An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters
Story ending generation aims at generating reasonable endings for a given story context.
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
With the trends of large NLP models, the increasing memory and computation costs hinder their efficient deployment on resource-limited devices.
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements.
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search
Sponsored search advertisements (ads) appear next to search results when consumers look for products and services on search engines.
Ranked #3 on
Image-text matching
on CommercialAdsDataset
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER
In this work, we conduct an empirical study on the "Unlabeled Entity Problem" and find that it leads to severe confusion between "O" and entities, decreasing class discrimination of old classes and declining the model's ability to learn new classes.
Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding
Dataset bias has attracted increasing attention recently for its detrimental effect on the generalization ability of fine-tuned models.
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Masked Autoencoders (MAE) based on a reconstruction task have risen to be a promising paradigm for self-supervised learning (SSL) and achieve state-of-the-art performance across different benchmark datasets.
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
Data-Driven Quickest Change Detection in (Hidden) Markov Models
The WADD is at most of the order of the logarithm of the ARL.
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
To solve the maximization problem, we propose a graph reinforcement learning-based early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios.
Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization
In this work, we consider the inverse problem where we use prior decision data to uncover the underlying decision-making process in the form of a mathematical optimization model.
Line Spectral Estimation via Unlimited Sampling
In addition, a two-stage US LSE (USLSE) is proposed, where the line spectral signal is first recovered by iteratively executing DP and OMP, and then the parameters are estimated by applying a state-of-the-art LSE algorithm.
Robust Lottery Tickets for Pre-trained Language Models
Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching accuracy comparable to the original models.
