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Found 802 papers, 256 papers with code
Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations
We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.
Self-Supervised Quality Estimation for Machine Translation
Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT.
Effective Convolutional Attention Network for Multi-label Clinical Document Classification
Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels.
Ranked #2 on
Medical Code Prediction
on MIMIC-III
Modeling Entity Knowledge for Fact Verification
Fact verification is a challenging task of identifying the truthfulness of given claims based on the retrieval of relevant evidence texts.
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts
Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs.
Interpolation between CNNs and ResNets
Although ordinary differential equations (ODEs) provide insights for designing networks architectures, its relationship with the non-residual convolutional neural networks (CNNs) is still unclear.
A Hybrid System for NLPTEA-2020 CGED Shared Task
As a result, among the six tracks in the shared task, our system performs well in the correction tracks: measured in F1 score, we rank first, with the highest precision, in the TOP3 correction track and third in the TOP1 correction track, also with the highest precision.
Think Before You Speak: Learning to Generate Implicit Knowledge for Response Generation by Self-Talk
We further investigate can such models identify when to generate implicit background knowledge and when it is not necessary.
Latent Topic-aware Multi-Label Classification
In real-world applications, data are often associated with different labels.
Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual Environment
At present, more and more work has begun to pay attention to the long-term housekeeping robot scene.
基于词信息嵌入的汉语构词结构识别研究(Chinese Word-Formation Prediction based on Representations of Word-Related Features)
“作为一种意合型语言, 汉语中的构词结构刻画了构词成分之间的组合关系, 是认知、理解词义的关键。在中文信息处理领域, 此前的构词结构识别工作大多沿用句法层面的粗粒度标签, 且主要基于上下文等词间信息建模, 忽略了语素义、词义等词内信息对构词结构识别的作用。本文采用语言学视域下的构词结构标签体系, 构建汉语构词结构及相关信息数据集, 提出了一种基于Bi-LSTM和Self-attention的模型, 以此来探究词内、词间等多方面信息对构词结构识别的潜在影响和能达到的性能。实验取得了良好的预测效果, 准确率77. 87%, F1值78. 36%;同时, 对比测试揭示, 词内的语素义信息对构词结构识别具有显著的贡献, 而词间的上下文信息贡献较弱且带有较强的不稳定性。该预测方法与数据集, 将为中文信息处理的多种任务, 如语素和词结构分析、词义识别与生成、语言文字研究与词典编纂等提供新的观点和方案。”
Rethinking Data Augmentation in Text-to-text Paradigm
As manually labelling data can be costly, some recent studies tend to augment the training data for improving the generalization power of machine learning models, known as data augmentation (DA).
Policy-Driven Neural Response Generation for Knowledge-Grounded Dialog Systems
Open-domain dialog systems aim to generate relevant, informative and engaging responses.
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs.
Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation
Seq2seq language generation models that are trained offline with multiple domains in a sequential fashion often suffer from catastrophic forgetting.
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction
Few-shot Relation Extraction refers to fast adaptation to novel relation classes with few samples through training on the known relation classes.
Automatically Detecting Reduced-formed English Pronunciations by Using Deep Learning
Reduced form pronunciations are widely used by native English speakers, especially in casual conversations.
中美学者学术英语写作中词汇难度特征比较研究——以计算语言学领域论文为例(A Comparative Study of the Features of Lexical Sophistication in Academic English Writing by Chinese and American)
“学术英语写作在国际学术交流中的作用日益凸显, 然而对于英语非母语者, 学术英语写作是困难的, 为此本文对计算语言领域中美学者学术英语写作中词汇难度特征做比较研究。自构建1132篇中美论文全文语料库, 统计语料中484个词汇难度特征值。经过特征筛选与因子分析的降维处理得到表现较好的五个维度。最后计算中美学者论文的维度分从而比较差异, 发现美国学者的论文相较中国学者的论文中词汇单位更具常用性、二元词串更具稳固性、三元词串更具稳固性、虚词更具复杂性、词类更具关联性。主要原因在于统计特征值时借助的外部资源库与美国学者的论文更贴近, 且中国学者没有完全掌握该领域学术写作的习惯。因此, 中国学者可充分利用英语本族语者构建的资源库, 从而产出更为地道与流利的学术英语论文。”
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues
We propose a shared task on summarizing real-life scenario dialogues, DialogSum Challenge, to encourage researchers to address challenges in dialogue summarization, which has been less studied by the summarization community.
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification
To harness such information to better serve customers, in this paper, we created a machine learning approach to automatically identify product issues and uncover root causes from the customer feedback text.
Amplifying Key Cues for Human-Object-Interaction Detection
In this paper we introduce two methods to amplify key cues in the image, and also a method to combine these and other cues when considering the interaction between a human and an object.
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation
In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD.
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation
Moreover, this task can be used to improve visual question generation and visual question answering.
Knowledge Representation Learning with Contrastive Completion Coding
Knowledge representation learning (KRL) has been used in plenty of knowledge-driven tasks.
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision
Besides, the robustness of the previous neural methods is limited by the large-scale annotated data.
GPTEval: NLG Evaluation using GPT-4 with Better Human Alignment
In this work, we present GPTEval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs.
Federated Learning without Full Labels: A Survey
Therefore, the problem of federated learning without full labels is important in real-world FL applications.
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning.
Urban Regional Function Guided Traffic Flow Prediction
The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest.
Visual-Linguistic Causal Intervention for Radiology Report Generation
Automatic radiology report generation is essential for computer-aided diagnosis and medication guidance.
3D Face Arbitrary Style Transfer
To solve this problem, we propose a novel method, namely Face-guided Dual Style Transfer (FDST).
A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples
Result: The result of the 3DVNET model with the augmentation method for pulmonary nodule detection shows that the proposed data augmentation method outperforms the method based on generative adversarial network (GAN) framework, training accuracy improved by 1. 5%, and with embedding mechanism for pulmonary nodules classification shows that the embedding mechanism improves the accuracy and robustness for the classification of pulmonary nodules obviously, the model training accuracy is close to 1 and the model testing F1-score is 0. 90. Conclusion:he proposed data augmentation method and embedding mechanism are beneficial to improve the accuracy and robustness of the model, and can be further applied in other common diagnostic imaging tasks.
Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training
The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding.
Factual Consistency Oriented Speech Recognition
This paper presents a novel optimization framework for automatic speech recognition (ASR) with the aim of reducing hallucinations produced by an ASR model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
How Does In-Context Learning Help Prompt Tuning?
This motivates the use of parameter-efficient adaptation methods such as prompt tuning (PT), which adds a small number of tunable embeddings to an otherwise frozen model, and in-context learning (ICL), in which demonstrations of the task are provided to the model in natural language without any additional training.
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a useful global model.
KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions.
Semi-Analytical Electromagnetic Transient Simulation Using Differential Transformation
For electromagnetic transient (EMT) simulation of a power system, a state-space-based approach needs to solve state-space EMT equations by using numerical integration methods, e. g., the Euler method, Runge-Kutta methods, and trapezoidal-rule method, at small time steps.
Multimodal Federated Learning via Contrastive Representation Ensemble
In this work, we propose Contrastive Representation Ensemble and Aggregation for Multimodal FL (CreamFL), a multimodal federated learning framework that enables training larger server models from clients with heterogeneous model architectures and data modalities, while only communicating knowledge on public dataset.
KILM: Knowledge Injection into Encoder-Decoder Language Models
Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters.
Adversarial Contrastive Distillation with Adaptive Denoising
To this end, we propose a novel structured ARD method called Contrastive Relationship DeNoise Distillation (CRDND).
Numerical analysis of a multistable capsule system under the delayed feedback control with a constant delay
In this paper, we study the control of coexisting attractors in this system by using a delayed feedback controller (DFC) with a constant delay.
A Survey on Spectral Graph Neural Networks
Graph neural networks (GNNs) have attracted considerable attention from the research community.
Debiasing Recommendation by Learning Identifiable Latent Confounders
Confounding bias arises due to the presence of unmeasured variables (e. g., the socio-economic status of a user) that can affect both a user's exposure and feedback.
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
This work focuses on in-context data augmentation for intent detection.
Ranked #1 on
Intent Detection
on HWU64 5-shot
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models
Video Anomaly Event Detection (VAED) is the core technology of intelligent surveillance systems aiming to temporally or spatially locate anomalous events in videos.
Mining Effective Features Using Quantum Entropy for Humor Recognition
Humor recognition has been extensively studied with different methods in the past years.
PLACES: Prompting Language Models for Social Conversation Synthesis
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns.
Using In-Context Learning to Improve Dialogue Safety
Concretely, to generate a response to an unsafe dialogue context, we retrieve demonstrations of safe model responses to similar dialogue contexts.
End-to-End Full-Atom Antibody Design
Finally, the updated antibody is docked to the epitope via the alignment of the shadow paratope.
Improving Open-Domain Dialogue Evaluation with a Causal Inference Model
We project these features to the dialogue level and train a dialogue-level MLP regression model, a dialogue-level LSTM, and a novel causal inference model called counterfactual-LSTM (CF-LSTM) to predict ratings.
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
To this end, we propose to achieve personalized spatial audio by reconstructing 3D human ears with single-view images.
Restricted Orthogonal Gradient Projection for Continual Learning
The basic idea is to adopt a restricted orthogonal constraint allowing parameters optimized in the direction oblique to the whole frozen space to facilitate forward knowledge transfer while consolidating previous knowledge.
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process.
Fixed-time synchronization for quaternion-valued memristor-based neural networks with mixed delays
In this paper, the fixed-time synchronization (FXTSYN) of unilateral coefficients quaternion-valued memristor-based neural networks (UCQVMNNs) with mixed delays is investigated.
Mutual Information Regularization for Vertical Federated Learning
However, previous works show that parties without labels (passive parties) in VFL can infer the sensitive label information owned by the party with labels (active party) or execute backdoor attacks to VFL.
EVM-CNN: Real-Time Contactless Heart Rate Estimation from Facial Video
Recently, it was shown that HR information can be extracted from facial videos by spatial decomposition and temporal filtering.
Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization
Content-Controllable Summarization generates summaries focused on the given controlling signals.
Diff-Glat: Diffusion Glancing Transformer for Parallel Sequence to Sequence Learning
Autoregressive models can achieve high generation quality, but the sequential decoding scheme causes slow decoding speed.
Is Self-Attention Powerful to Learn Code Syntax and Semantics?
We focus on a well-known representative code model, CodeBERT, and study how it can learn code syntax and semantics by the self-attention mechanism and Masked Language Modelling (MLM) at the token level.
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines
These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer's expectations and intent.
Prompt Gating: A Parameter Efficient Tuning Method for Zero-Shot Multi-Source Translation
Multi-source translation (MST), which typically receives multiple source sentences of the same meaning in different languages, has been shown superior to single-source translation.
Continually Learning from Existing Models: Knowledge Accumulation for Neural Machine Translation
Although continually extending an existing NMT model to new domains or languages has attracted intensive interest in recent years, the equally valuable problem of continually improving a given NMT model in its domain by leveraging knowledge from an unlimited number of existing NMT models is not explored yet.
Quaternion Tensor Completion with Sparseness for Color Video Recovery
A novel low-rank completion algorithm based on the quaternion tensor is proposed in this paper.
Weld Defect Segmentation in Xray Image with Boundary Label Smoothing
Weld defect segmentation (WDS) is widely used to detect defects from X-ray images for welds, which is of practical importance for manufacturing in all industries.
Uncertain Facial Expression Recognition via Multi-task Assisted Correction
Specifically, a confidence estimation block and a weighted regularization module are applied to highlight solid samples and suppress uncertain samples in every batch.
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph.
Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery
Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers.
Three High-rate Beamforming Methods for Active IRS-aided Wireless Network
To fully exploit the amplifying gain achieved by active IRS, two high-rate methods, maximum ratio reflecting (MRR) and selective ratio reflecting (SRR) are presented, which are motivated by maximum ratio combining and selective ratio combining.
Unifying Vision, Text, and Layout for Universal Document Processing
UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation.
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling
Intuitively, FR algorithms can benefit from both the estimation of uncertainty and the detection of out-of-distribution (OOD) samples.
Joint Secure Transmit Beamforming Designs for Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC), which allows individual radar and communication systems to share the same spectrum bands, is an emerging and promising technique for alleviating spectrum congestion problems.
Vertical Federated Learning
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters.
GitFL: Adaptive Asynchronous Federated Learning using Version Control
Unlike traditional FL, the cloud server of GitFL maintains a master model (i. e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices.
Unsupervised Explanation Generation via Correct Instantiations
The major difficulty is finding the conflict point, where the statement contradicts our real world.
Background-Mixed Augmentation for Weakly Supervised Change Detection
Change detection (CD) is to decouple object changes (i. e., object missing or appearing) from background changes (i. e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc.
UniSumm: Unified Few-shot Summarization with Multi-Task Pre-Training and Prefix-Tuning
The diverse demands of different summarization tasks and their high annotation costs are driving a need for few-shot summarization.
LGN-Net: Local-Global Normality Network for Video Anomaly Detection
Existing unsupervised VAD methods tend to learn normality from training sets consisting of only normal videos and regard instances deviating from such normality as anomalies.
MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation
Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy.
MACSum: Controllable Summarization with Mixed Attributes
We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning.
Soft Augmentation for Image Classification
We draw inspiration from human visual classification studies and propose generalizing augmentation with invariant transforms to soft augmentation where the learning target softens non-linearly as a function of the degree of the transform applied to the sample: e. g., more aggressive image crop augmentations produce less confident learning targets.
Egocentric Audio-Visual Noise Suppression
In this paper, we first demonstrate that egocentric visual information is helpful for noise suppression.
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models
Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts.
Learning to Learn Domain-invariant Parameters for Domain Generalization
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice.
SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency
To bridge the gap between the reference points of salient queries and Transformer detectors, we propose SAlient Point-based DETR (SAP-DETR) by treating object detection as a transformation from salient points to instance objects.
Exploiting Spatial-temporal Correlations for Video Anomaly Detection
Video anomaly detection (VAD) remains a challenging task in the pattern recognition community due to the ambiguity and diversity of abnormal events.
SCA: Streaming Cross-attention Alignment for Echo Cancellation
End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation.
Physics-aware Graph Neural Network for Accurate RNA 3D Structure Prediction
Biological functions of RNAs are determined by their three-dimensional (3D) structures.
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning
Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning.
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings.
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
In this paper, we present a novel physics-informed neural network (PINN) model for seismic wave modeling in semi-infinite domain without the nedd of labeled data.
Context-Situated Pun Generation
In this work, we propose a new task, context-situated pun generation, where a specific context represented by a set of keywords is provided, and the task is to first identify suitable pun words that are appropriate for the context, then generate puns based on the context keywords and the identified pun words.
ExPUNations: Augmenting Puns with Keywords and Explanations
The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master.
Improving Radiology Summarization with Radiograph and Anatomy Prompts
The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists.
FedCross: Towards Accurate Federated Learning via Multi-Model Cross Aggregation
Due to the remarkable performance in preserving data privacy for decentralized data scenarios, Federated Learning (FL) has been considered as a promising distributed machine learning paradigm to deal with data silos problems.
Towards a Unified Multi-Dimensional Evaluator for Text Generation
We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.
Task Compass: Scaling Multi-task Pre-training with Task Prefix
Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language models.
Exploring Interactions and Regulations in Collaborative Learning: An Interdisciplinary Multimodal Dataset
Collaborative learning is an educational approach that enhances learning through shared goals and working together.
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Warning: this paper contains model outputs exhibiting offensiveness and biases.
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes
Our theoretical analyses show that directly using proxy models can give a false sense of (un)fairness.
Joint Beamforming Designs for Active Reconfigurable Intelligent Surface: A Sub-Connected Array Architecture
Affected by the "double fading" effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS.
Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN)
Latent variable models are crucial in scientific research, where a key variable, such as effort, ability, and belief, is unobserved in the sample but needs to be identified.
Large-Scale Spatial Cross-Calibration of Hinode/SOT-SP and SDO/HMI
Second, analysis of over 12, 000 scans show that the pointing information is often incorrect by dozens of arcseconds with a strong bias.
Resource Allocation and Resolution Control in the Metaverse with Mobile Augmented Reality
With the development of blockchain and communication techniques, the Metaverse is considered as a promising next-generation Internet paradigm, which enables the connection between reality and the virtual world.
Implicit Conversion of Manifold B-Rep Solids by Neural Halfspace Representation
We present a novel implicit representation -- neural halfspace representation (NH-Rep), to convert manifold B-Rep solids to implicit representations.
Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training.
Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance
Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand Challenge.
Optimization for Reflection and Transmission Dual-Functional Active RIS-Assisted Systems
Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices.
Learning Differential Operators for Interpretable Time Series Modeling
In this work, we propose an learning framework that can automatically obtain interpretable PDE models from sequential data.
Joint Beamforming Design for Intelligent Omni Surface Assisted Wireless Communication Systems
Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment.
Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation
Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i)the source knowledge calibration module that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii)the target semantics calibration module that calibrates the unreliable semantics.
Conjugate Natural Selection: Fisher-Rao Natural Gradient Descent Optimally Approximates Evolutionary Dynamics and Continuous Bayesian Inference
Rather than refining individual candidate solutions for a general non-convex optimization problem, by analogy to evolution, we consider minimizing the average loss for a parametric distribution over hypotheses.
Delving into the Continuous Domain Adaptation
Existing domain adaptation methods assume that domain discrepancies are caused by a few discrete attributes and variations, e. g., art, real, painting, quickdraw, etc.
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation
Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations.
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
Z-Code++ creates new state of the art on 9 out of 13 text summarization tasks across 5 languages.
I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning
To this end, we propose CoLE, a Co-distillation Learning method for KG Embedding that exploits the complementarity of graph structures and text information.
Learning Program Representations with a Tree-Structured Transformer
Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks.
CommitBART: A Large Pre-trained Model for GitHub Commits
GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution.
An axiomatic theory for anonymized risk sharing
We study an axiomatic framework for anonymized risk sharing.
Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker-chosen ones.
Conditional Antibody Design as 3D Equivariant Graph Translation
Specifically, the relative improvement to baselines is about 23% in antigen-binding CDR design and 34% for affinity optimization.
Seeing your sleep stage: cross-modal distillation from EEG to infrared video
$S^3VE$ is a large-scale dataset including synchronized infrared video and EEG signal for sleep stage classification, including 105 subjects and 154, 573 video clips that is more than 1100 hours long.
Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation
Our method exploits semantic segmentation to fuse nonlocal instance features, such as center prediction, and further enhances the fusion scheme in a multi- and cross-level way.
Ranked #1 on
3D Instance Segmentation
on PartNet
A Systematic Evaluation of Response Selection for Open Domain Dialogue
In this work, we curated a dataset where responses from multiple response generators produced for the same dialog context are manually annotated as appropriate (positive) and inappropriate (negative).
DialogSum Challenge: Results of the Dialogue Summarization Shared Task
We report the results of DialogSum Challenge, the shared task on summarizing real-life scenario dialogues at INLG 2022.
Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC) has been envisioned as a promising technology to tackle the spectrum congestion problem for future networks.
Physics-informed Deep Super-resolution for Spatiotemporal Data
High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales.
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
Neural networks have been rapidly expanding in recent years, with novel strategies and applications.
Some Practice for Improving the Search Results of E-commerce
In the Amazon KDD Cup 2022, we aim to apply natural language processing methods to improve the quality of search results that can significantly enhance user experience and engagement with search engines for e-commerce.
Learning Prototype via Placeholder for Zero-shot Recognition
Specifically, we combine seen classes to hallucinate new classes which play as placeholders of the unseen classes in the visual and semantic space.
Learning Appearance-motion Normality for Video Anomaly Detection
Video anomaly detection is a challenging task in the computer vision community.
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering
Existing visual question answering methods tend to capture the cross-modal spurious correlations, and fail to discover the true causal mechanism that facilitates reasoning truthfully based on the dominant visual evidence and the question intention.
Improving Bot Response Contradiction Detection via Utterance Rewriting
Previous work has treated contradiction detection in bot responses as a task similar to natural language inference, e. g., detect the contradiction between a pair of bot utterances.
Visible and Near Infrared Image Fusion Based on Texture Information
Multi-sensor fusion is widely used in the environment perception system of the autonomous vehicle.
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
Pretraining molecular representation models without labels is fundamental to various applications.
Adversarial Contrastive Learning via Asymmetric InfoNCE
Contrastive learning (CL) has recently been applied to adversarial learning tasks.
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval
To overcome these challenges, we propose a novel federated hashing framework that enables participating clients to jointly train the shared deep hashing model by leveraging the prototypical hash codes for each class.
Team PKU-WICT-MIPL PIC Makeup Temporal Video Grounding Challenge 2022 Technical Report
In this technical report, we briefly introduce the solutions of our team `PKU-WICT-MIPL' for the PIC Makeup Temporal Video Grounding (MTVG) Challenge in ACM-MM 2022.
Dynamic Contrastive Distillation for Image-Text Retrieval
Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major drawback: the ever-increasing size of VLP models restricts its deployment to real-world search scenarios (where the high latency is unacceptable).
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN
However, both strategies are faced with some immediate problems: raw features cannot represent various properties of nodes (e. g., structure information), and representations learned by supervised GNN may suffer from the poor performance of the classifier on the poisoned graph.
Understanding Instance-Level Impact of Fairness Constraints
A variety of fairness constraints have been proposed in the literature to mitigate group-level statistical bias.
Video Activity Localisation with Uncertainties in Temporal Boundary
Such uncertainties in temporal labelling are currently ignored in model training, resulting in learning mis-matched video-text correlation with poor generalisation in test.
SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation
We further complement the evaluation metrics of 3D generative models with the shading-image-based Fr\'echet inception distance (FID) scores to better assess visual quality and shape distribution of the generated shapes.
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL).
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection
This method can perform well with blood cell detection in our experiments.
Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging.
Metric-Fair Classifier Derandomization
We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier $f: X \to [0, 1]$, sample a deterministic classifier $\hat{f}: X \to \{0, 1\}$ that approximates the output of $f$ in aggregate over any data distribution.
To Aggregate or Not? Learning with Separate Noisy Labels
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e. g., via crowdsourcing).
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems
Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against adversarial attacks, raising significant security concerns in deploying SRSs.
Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes
Recent advances in applying Graph Neural Networks (GNNs) to molecular science have showcased the power of learning three-dimensional (3D) structure representations with GNNs.
Hybrid Models for Mixed Variables in Bayesian Optimization
We systematically describe the problem of simultaneous surrogate modeling of mixed variables (i. e., continuous, integer and categorical variables) in the Bayesian optimization (BO) context.
Multi-stage Moving Target Defense: A Security-enhanced D-FACTS Implementation Approach
We prove that a designed MMTD can significantly improve the detection capability compared to existing one-stage MTDs.
Fairness Transferability Subject to Bounded Distribution Shift
Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound?
Certifying Some Distributional Fairness with Subpopulation Decomposition
In this paper, we first formulate the certified fairness of an ML model trained on a given data distribution as an optimization problem based on the model performance loss bound on a fairness constrained distribution, which is within bounded distributional distance with the training distribution.
ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation
We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders, i. e. vertices, edges and surface patches, and the correspondence of primitives, which are holistically modeled as a chain complex, and show that by modeling such comprehensive structures more complete and regularized reconstructions can be achieved.
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns
Skeleton sequences are compact and lightweight.
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs.
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor.
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search
The key concept is to interpret mathematical operations and system state variables by computational rules and symbols, establish symbolic reasoning of mathematical formulas via expression trees, and employ a Monte Carlo tree search (MCTS) agent to explore optimal expression trees based on measurement data.
A Template-based Method for Constrained Neural Machine Translation
In this work, we propose a template-based method that can yield results with high translation quality and match accuracy and the inference speed of our method is comparable with unconstrained NMT models.
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
We argue that relation information can be introduced more explicitly and effectively into the model.
Directed Acyclic Transformer for Non-Autoregressive Machine Translation
Non-autoregressive Transformers (NATs) significantly reduce the decoding latency by generating all tokens in parallel.
PrEF: Percolation-based Evolutionary Framework for the diffusion-source-localization problem in large networks
We assume that the state of a number of nodes in a network could be investigated if necessary, and study what configuration of those nodes could facilitate a better solution for the diffusion-source-localization (DSL) problem.
Hero-Gang Neural Model For Named Entity Recognition
Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text.
Ranked #1 on
Named Entity Recognition (NER)
on WNUT 2016
Residue-based Label Protection Mechanisms in Vertical Logistic Regression
To deal with this, we then propose three protection mechanisms, e. g., additive noise mechanism, multiplicative noise mechanism, and hybrid mechanism which leverages local differential privacy and homomorphic encryption techniques, to prevent the attack and improve the robustness of the vertical logistic regression.
Investigating Generalization by Controlling Normalized Margin
Weight norm $\|w\|$ and margin $\gamma$ participate in learning theory via the normalized margin $\gamma/\|w\|$.
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention
The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks.
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.