Search Results for author:
Found 105 papers, 42 papers with code
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction.
Learning A Sparse Transformer Network for Effective Image Deraining
To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction.
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation
In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.
Binary Embedding-based Retrieval at Tencent
To tackle the challenge, we propose a binary embedding-based retrieval (BEBR) engine equipped with a recurrent binarization algorithm that enables customized bits per dimension.
One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER
Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain.
Reasoning with Language Model Prompting: A Survey
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.
On Robust Observer Design for System Motion on SE(3) Using Onboard Visual Sensors
The key design idea for this observer is to estimate the visible set and identify the mis-identified features from the measurements.
Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images
In such scenarios, enforcing smooth, globally continuous deformation fields leads to incorrect/implausible registration results.
On Analyzing the Role of Image for Visual-enhanced Relation Extraction
Multimodal relation extraction is an essential task for knowledge graph construction.
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset
To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction.
Robust output regulation of linear system subject to modeled and unmodeled uncertainty
The output regulation scheme is utilized in the framework to track the reference in the presence of modeled disturbance, and the effect of unmodeled disturbance is reduced by an $\mathcal{H}_\infty$ compensator.
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
We study the problem of extracting N-ary relation tuples from scientific articles.
Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction
In this paper, we propose a novel approach of schema-aware Reference As Prompt (RAP), which dynamically leverage schema and knowledge inherited from global (few-shot) training data for each sample.
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
This paper presents an empirical study to build relation extraction systems in low-resource settings.
Multimodal Analogical Reasoning over Knowledge Graphs
Analogical reasoning is fundamental to human cognition and holds an important place in various fields.
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees.
QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM
We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem.
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data
Then during training, DASK injects pivot-related knowledge graph information into source domain texts.
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.
Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance.
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction.
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test.
BI-GreenNet: Learning Green's functions by boundary integral network
In addition, we also use the Green's function calculated by our method to solve a class of PDE, and also obtain high-precision solutions, which shows the good generalization ability of our method on solving PDEs.
M2N: Mesh Movement Networks for PDE Solvers
However, mesh movement methods, such as the Monge-Ampere method, require the solution of auxiliary equations, which can be extremely expensive especially when the mesh is adapted frequently.
Dense Learning based Semi-Supervised Object Detection
Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data.
Distributed Optimal Control with Recovered Robustness for Uncertain Network Systems: A Complementary Design Approach
A novel two-step complementary design approach is proposed.
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration
The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries.
Optimization of Directional Landmark Deployment for Visual Observer on SE(3)
An optimization method is proposed in this paper for novel deployment of given number of directional landmarks (location and pose) within a given region in the 3-D task space.
NeuralReshaper: Single-image Human-body Retouching with Deep Neural Networks
In this paper, we present NeuralReshaper, a novel method for semantic reshaping of human bodies in single images using deep generative networks.
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning
Then these negative adversaries are trained end-to-end together with the backbone representation network to enhance the discriminative information and promote factor disentanglement performance by maximizing the adversarial contrastive loss.
Single UHD Image Dehazing via Interpretable Pyramid Network
Currently, most single image dehazing models cannot run an ultra-high-resolution (UHD) image with a single GPU shader in real-time.
AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems
Since proposed in the 70s, the Non-Equilibrium Green Function (NEGF) method has been recognized as a standard approach to quantum transport simulations.
Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations
The Schr\"odinger equation is at the heart of modern quantum mechanics.
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task which aims to extract the aspects from sentences and identify their corresponding sentiments.
Aspect-Based Sentiment Analysis (ABSA)
named-entity-recognition
+3
Ontology-enhanced Prompt-tuning for Few-shot Learning
Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they typically struggle to reason rare or emerging unseen entities.
Ranked #1 on
Link Prediction
on FB15k-237-ind
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
Attribute Extraction
Cross-Domain Named Entity Recognition
+4
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model
They are lack of self-evaluation ability, that is, to examine the rationality of their prediction results, thus failing to guide users to identify high-quality ones from their candidate results.
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework
Although the Trajectory Prediction (TP) model has achieved great success in computer vision and robotics fields, its architecture and training scheme design rely on heavy manual work and domain knowledge, which is not friendly to common users.
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection.
Harmonic Retrieval with $L_1$-Tucker Tensor Decomposition
We also develop a novel subcarrier recovery method for the proposed model.
Fed2: Feature-Aligned Federated Learning
Federated learning learns from scattered data by fusing collaborative models from local nodes.
A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities
With both scaling trends, new problems and challenges emerge in DL inference serving systems, which gradually trends towards Large-scale Deep learning Serving systems (LDS).
A neural network framework for learning Green's function
Green's function plays a significant role in both theoretical analysis and numerical computing of partial differential equations (PDEs).
Performance-Guaranteed ODE Solvers with Complexity-Informed Neural Networks
Traditionally, we provide technical parameters for ODE solvers, such as the order, the stepsize and the local error threshold.
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets.
Aspect-Based Sentiment Analysis (ABSA)
Language Modelling
+2
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar.
Unpaired Deep Image Deraining Using Dual Contrastive Learning
Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible.
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data.
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners.
Ranked #1 on
Few-Shot Learning
on SST-2 Binary classification
A Deep Discontinuity-Preserving Image Registration Network
Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions.
Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences
In turn, it also collects the reinforced features from each modality and uses them to generate a reinforced common message.
CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning
Image registration is a fundamental building block for various applications in medical image analysis.
Document-level Relation Extraction as Semantic Segmentation
Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.
Ranked #4 on
Relation Extraction
on ReDocRED
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search.
Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining
Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density.
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1 (v1) metric)
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges
We advocate a new resource allocation framework, which we term resource rationing, for wireless federated learning (FL).
Disentangled Contrastive Learning for Learning Robust Textual Representations
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks.
Proposal for a Bell test in cavity optomagnonics
Entanglement between magnon mode and one of the two optical modes will be generated by the first pulse, and the state of magnon mode is subsequently mapped into another optical mode via the second pulse.
Quantum Physics
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM).
Ranked #1 on
Reading Comprehension
on ReCAM
(using extra training data)
Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow
As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning.
Towards Robust Textual Representations with Disentangled Contrastive Learning
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
Design and Analysis of Uplink and Downlink Communications for Federated Learning
Comprehensive numerical evaluation on various real-world datasets reveals that the benefit of a FL-tailored uplink and downlink communication design is enormous - a carefully designed quantization and transmission achieves more than 98% of the floating-point baseline accuracy with fewer than 10% of the baseline bandwidth, for majority of the experiments on both i. i. d.
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning
In this paper, we propose a contrastive learning framework for cross-domain sentiment classification.
Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction
DGANN distinguishes from previous models with those features: (1) It learns the local chemical environment encoding by graph attention mechanism on chemical bonds.
Third ArchEdge Workshop: Exploring the Design Space of Efficient Deep Neural Networks
This paper gives an overview of our ongoing work on the design space exploration of efficient deep neural networks (DNNs).
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization
Due to scarcity of labels on the target domain, we introduce mutual information maximization (MIM) apart from CL to exploit the features that best support the final prediction.
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography
Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs).
On Robustness and Bias Analysis of BERT-based Relation Extraction
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks.
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.
Heterogeneous Federated Learning
Specifically, we design a feature-oriented regulation method ({$\Psi$-Net}) to ensure explicit feature information allocation in different neural network structures.
AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency
Based on the neural network attention mechanism, we propose a comprehensive dynamic optimization framework including (1) testing-phase channel and column feature map pruning, as well as (2) training-phase optimization by targeted dropout.
LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery
This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation.
Generative Feature Replay with Orthogonal Weight Modification for Continual Learning
For this scenario, generative replay is a promising strategy which generates and replays pseudo data for previous tasks to alleviate catastrophic forgetting.
Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios
Results show that the adversarial scenarios generated by our method significantly degrade the performance of the tested vehicles.
Logic-based switching finite-time stabilization with applications in mechanical systems
Different from the existing adaptive controllers for structured/parametric uncertainties, a new switching barrier Lyapunov method and supervisory functions are introduced to overcome the obstacles caused by unstructured uncertainties and unknown control directions.
COKE: Communication-Censored Decentralized Kernel Learning
This paper studies the decentralized optimization and learning problem where multiple interconnected agents aim to learn an optimal decision function defined over a reproducing kernel Hilbert space by jointly minimizing a global objective function, with access to their own locally observed dataset.
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms.
SQLFlow: A Bridge between SQL and Machine Learning
Previous database systems extended their SQL dialect to support ML.
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration
In this paper, we propose Helios, a heterogeneity-aware FL framework to tackle the straggler issue.
Distributed, Parallel, and Cluster Computing
Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning
In this work, we show that such adversarial-based methods can only reduce the domain style gap, but cannot address the domain content distribution gap that is shown to be important for object detectors.
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications
Based on the detection result, we further propose a data recovery methodology to defend the physical adversarial attacks.
Gradient-free Neural Network Training by Multi-convex Alternating Optimization
To overcome these drawbacks, alternating minimization-based methods for deep neural network optimization have attracted fast-increasing attention recently.
Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation
To mitigate the challenges, the memristor crossbar array has emerged as an intrinsically suitable matrix computation and low-power acceleration framework for DNN applications.
Multi-stage Deep Classifier Cascades for Open World Recognition
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase.
ADMM for Efficient Deep Learning with Global Convergence
However, as an emerging domain, several challenges remain, including 1) The lack of global convergence guarantees, 2) Slow convergence towards solutions, and 3) Cubic time complexity with regard to feature dimensions.
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks
To address this issue, we propose DoPa -- a comprehensive CNN detection methodology for various physical adversarial attacks.
Interpreting and Evaluating Neural Network Robustness
Recently, adversarial deception becomes one of the most considerable threats to deep neural networks.
INTERPRETABLE CONVOLUTIONAL FILTER PRUNING
As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction.
FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation
With a novel fully-trained generative network, FTGAN can synthesize higher-quality images and urge the outputs of the FTGAN are more relevant to the input sentences.
DeepCount: Crowd Counting with WiFi via Deep Learning
By massive training samples, our end-to-end learning approach can achieve an average of 86. 4% prediction accuracy in an environment of up to 5 people.
Background Subtraction with Real-time Semantic Segmentation
Our proposed framework consists of two components, a traditional BGS segmenter $\mathcal{B}$ and a real-time semantic segmenter $\mathcal{S}$.
Demystifying Neural Network Filter Pruning
We find that the filter magnitude based method fails to eliminate the filters with repetitive functionality.
Distilling Critical Paths in Convolutional Neural Networks
Neural network compression and acceleration are widely demanded currently due to the resource constraints on most deployment targets.
Learning color space adaptation from synthetic to real images of cirrus clouds
With our adapted synthetic data for training the semantic segmentation, we achieve an improvement of 6:59% when applied to real images, superior to alternative methods.
Progressive Weight Pruning of Deep Neural Networks using ADMM
Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates.
Functionality-Oriented Convolutional Filter Pruning
As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction.
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization.
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition
In this work, we propose HASP -- a high-performance security enhancement approach to solve this security issue on mobile devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients
In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications. However, recent works have shown that neural networks are susceptible to adversarial examples, indicating possible flaws intrinsic to the network structures.
How convolutional neural network see the world - A survey of convolutional neural network visualization methods
Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc.
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction
By comparing the analyzed saliency map and the adversarial perturbation distribution, we proposed a new evaluation scheme to comprehensively assess the adversarial attack precision and efficiency.
