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Found 186 papers, 68 papers with code
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese
We investigate the usage of entity linking (EL)in downstream tasks and present the first modularized EL toolkit for easy task adaptation.
Natural Language Processing Meets Quantum Physics: A Survey and Categorization
Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers.
ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer
Other sparse methods use clustering patterns to select words, but the clustering process is separate from the training process of the target task, which causes a decrease in effectiveness.
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition
Cross-domain named entity recognition aims to improve performance in a target domain with shared knowledge from a well-studied source domain.
Cross-Domain Named Entity Recognition
Machine Reading Comprehension
+2
MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering
To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the relevant information.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation
This paper concentrates on an unexplored yet meaningful task, i. e., interactive point cloud semantic segmentation, which assigns high-quality semantic labels to all points in a scene with user corrective clicks.
Learning Expressive And Generalizable Motion Features For Face Forgery Detection
However, current sequence-based face forgery detection methods use general video classification networks directly, which discard the special and discriminative motion information for face manipulation detection.
On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models
Big models have achieved revolutionary breakthroughs in the field of AI, but they might also pose potential concerns.
Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization
Large language models (LLMs) have revolutionized the role of AI, yet also pose potential risks of propagating unethical content.
DiffSal: Joint Audio and Video Learning for Diffusion Saliency Prediction
Audio-visual saliency prediction can draw support from diverse modality complements, but further performance enhancement is still challenged by customized architectures as well as task-specific loss functions.
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities.
HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph
In recent years, temporal knowledge graph (TKG) reasoning has received significant attention.
Frequency-aware Graph Signal Processing for Collaborative Filtering
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency.
APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding
The massive adoption of large language models (LLMs) demands efficient deployment strategies.
Re-evaluating the Memory-balanced Pipeline Parallelism: BPipe
Pipeline parallelism is an essential technique in the training of large-scale Transformer models.
Rapid Open-World Adaptation by Adaptation Principles Learning
This is an important characteristic of intelligent agents, as it allows them to continue to function effectively in novel or unexpected situations, but still stands as a critical challenge for deep reinforcement learning (DRL).
Heterogeneous Graph Neural Architecture Search with GPT-4
The basic idea of GHGNAS is to design a set of prompts that can guide GPT-4 toward the task of generating new heterogeneous graph neural architectures.
MaTe3D: Mask-guided Text-based 3D-aware Portrait Editing
To address this limitation, we propose \textbf{MaTe3D}: mask-guided text-based 3D-aware portrait editing.
Resilience-Assuring Hydrogen-Powered Microgrids
Green hydrogen has shown great potential to power microgrids as a primary source, yet the operation methodology under extreme events is still an open area.
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network
Oscillatory combustion in aero engines and modern gas turbines often has significant adverse effects on their operation, and accurately recognizing various oscillation modes is the prerequisite for understanding and controlling combustion instability.
VividTalk: One-Shot Audio-Driven Talking Head Generation Based on 3D Hybrid Prior
Audio-driven talking head generation has drawn much attention in recent years, and many efforts have been made in lip-sync, expressive facial expressions, natural head pose generation, and high video quality.
Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation
Character Animation aims to generating character videos from still images through driving signals.
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks
Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance.
Software-Defined Virtual Synchronous Condenser
Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids.
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
In addition, we present a new architecture of assigning independent FR modules to separate sub-networks for parallel CTR models, as opposed to the conventional method of inserting a shared FR module on top of the embedding layer.
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
It is crucial to effectively model feature interactions to improve the prediction performance of CTR models.
Ranked #1 on
Click-Through Rate Prediction
on Criteo
Mid-Long Term Daily Electricity Consumption Forecasting Based on Piecewise Linear Regression and Dilated Causal CNN
This study decomposes the daily electricity consumption series into three components: trend, seasonal, and residual, and constructs a two-stage prediction method using piecewise linear regression as a filter and Dilated Causal CNN as a predictor.
Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning
We discovered that most models are essentially misaligned, necessitating further ethical value alignment.
MuseChat: A Conversational Music Recommendation System for Videos
The reasoning module, equipped with the power of Large Language Model (Vicuna-7B) and extended to multi-modal inputs, is able to provide reasonable explanation for the recommended music.
Tackling Data Bias in MUSIC-AVQA: Crafting a Balanced Dataset for Unbiased Question-Answering
In recent years, there has been a growing emphasis on the intersection of audio, vision, and text modalities, driving forward the advancements in multimodal research.
Early Warning Prediction with Automatic Labeling in Epilepsy Patients
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures.
Graph Neural Architecture Search with GPT-4
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
Scalable Neural Dynamic Equivalence for Power Systems
Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic components.
Physics-Aware Neural Dynamic Equivalence of Power Systems
The contributions are threefold: (1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; (2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; (3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models.
Physics-Informed Induction Machine Modelling
This rapid communication devises a Neural Induction Machine (NeuIM) model, which pilots the use of physics-informed machine learning to enable AI-based electromagnetic transient simulations.
3D Multiple Object Tracking on Autonomous Driving: A Literature Review
This paper undertakes a comprehensive examination, assessment, and synthesis of the research landscape in this domain, remaining attuned to the latest developments in 3D MOT while suggesting prospective avenues for future investigation.
UGC: Unified GAN Compression for Efficient Image-to-Image Translation
Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation.
Segment Anything Model for Brain Tumor Segmentation
Glioma is a prevalent brain tumor that poses a significant health risk to individuals.
UniST: Towards Unifying Saliency Transformer for Video Saliency Prediction and Detection
While many approaches have crafted task-specific training paradigms for either video saliency prediction or video salient object detection tasks, few attention has been devoted to devising a generalized saliency modeling framework that seamlessly bridges both these distinct tasks.
RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents
The rapid evolution of the web has led to an exponential growth in content.
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users.
Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning
Different from existing methods which integrate multiple-granularity features or multiple outputs, we resort to the perspective of representation learning in which auxiliary tasks are utilized to enable the encoder to jointly learn robust features with the main task of per-pixel classification during optimization.
Induction Network: Audio-Visual Modality Gap-Bridging for Self-Supervised Sound Source Localization
By decoupling the gradients of visual and audio modalities, the discriminative visual representations of sound sources can be learned with the designed Induction Vector in a bootstrap manner, which also enables the audio modality to be aligned with the visual modality consistently.
Recommendation Unlearning via Matrix Correction
We conducted comprehensive experiments to validate the effectiveness of IMCorrect and the results demonstrate that IMCorrect is superior in completeness, utility, and efficiency, and is applicable in many recommendation unlearning scenarios.
Entropy Neural Estimation for Graph Contrastive Learning
In this paper, we theoretically illustrate that the entropy of a dataset can be approximated by maximizing the lower bound of the mutual information across different views of a graph, \ie, entropy is estimated by a neural network.
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure Network
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks
To that end, the observed randomness and spatial-correlations are captured by learning the drift and diffusion terms of the stochastic differential equation with a Gumble matrix embedding, respectively.
FTFDNet: Learning to Detect Talking Face Video Manipulation with Tri-Modality Interaction
DeepFake based digital facial forgery is threatening public media security, especially when lip manipulation has been used in talking face generation, and the difficulty of fake video detection is further improved.
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries.
MachMap: End-to-End Vectorized Solution for Compact HD-Map Construction
This report introduces the 1st place winning solution for the Autonomous Driving Challenge 2023 - Online HD-map Construction.
Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease
Validation analysis revealed that the AUCs of DLRP were 0. 91 for GEV (95% CI 0. 90 to 0. 93, p < 0. 05) and 0. 88 for HRV (95% CI 0. 86 to 0. 89, p < 0. 01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM.
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM).
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method
To address these problems, we propose a general language model distillation (GLMD) method that performs two-stage word prediction distillation and vocabulary compression, which is simple and surprisingly shows extremely strong performance.
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model
Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices.
Revisiting Acceptability Judgements
We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language.
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates.
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
Specifically, TriSIM4Rec consists of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence information in user-item interactions, which is implemented by incremental singular value decomposition (SVD); 2) a parameter-free attention module to capture sequential information of user interactions effectively and efficiently; and 3) an item transition matrix to store the transition probabilities of item pairs.
A Measurement-Based Quantum-Like Language Model for Text Matching
We take the word density matrix in one sentence as a set of measurement operators to measure another sentence, which is consistent with the definition of measurement operators in quantum theory and has a specific semantic interpretation.
CASP-Net: Rethinking Video Saliency Prediction from an Audio-VisualConsistency Perceptual Perspective
Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain.
NovPhy: A Testbed for Physical Reasoning in Open-world Environments
But is it enough to only have physical reasoning capabilities to operate in a real physical environment?
A network-based biomarkers discovery of Cold/Hot ZHENG chronic gastritis and Cold/Hot herbs of formulae
And we collected 25 formulae (with traditional effects related to Cold/Hot ZHENG) for CG and corresponding 89 Cold/Hot herbs (including Warm/Cool herbs) to discover features and construct target networks of Cold/Hot herbs on the basis of network target and enrichment analysis.
Personalized Graph Signal Processing for Collaborative Filtering
However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy.
ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing
Specifically, in Phase I, a W-space-oriented StyleGAN inversion network is trained and used to perform image inversion and editing, which assures the editability but sacrifices reconstruction quality.
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization
With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to manifest competitive advantages.
GIPA: A General Information Propagation Algorithm for Graph Learning
Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
CASP-Net: Rethinking Video Saliency Prediction From an Audio-Visual Consistency Perceptual Perspective
Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain.
CL4CTR: A Contrastive Learning Framework for CTR Prediction
Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e. g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance.
A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering
One of the key challenges of knowledge base question answering (KBQA) is the multi-hop reasoning.
Recovering Fine Details for Neural Implicit Surface Reconstruction
Recent works on implicit neural representations have made significant strides.
Knowing the Past to Predict the Future: Reinforcement Virtual Learning
Reinforcement Learning (RL)-based control system has received considerable attention in recent decades.
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations
Secondly, the WHCN takes the hypergraph as input and learns to predict high-precision point-level pseudo labels by label propagation.
AS-PD: An Arbitrary-Size Downsampling Framework for Point Clouds
Given an input point cloud of arbitrary size, we first perform a task-agnostic pre-sampling on the input point cloud to a specified sample size.
MorphTE: Injecting Morphology in Tensorized Embeddings
In the era of deep learning, word embeddings are essential when dealing with text tasks.
Parameter-free Dynamic Graph Embedding for Link Prediction
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time.
DART: Articulated Hand Model with Diverse Accessories and Rich Textures
Unity GUI is also provided to generate synthetic hand data with user-defined settings, e. g., pose, camera, background, lighting, textures, and accessories.
Multi-Scale Wavelet Transformer for Face Forgery Detection
To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection.
GLM-130B: An Open Bilingual Pre-trained Model
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on
Language Modelling
on CLUE (OCNLI_50K)
Enhanced Secure Wireless Transmission Using IRS-aided Directional Modulation
To reduce the computational complexity, a new method of maximizing receive power with zero-forcing constraint (Max-RP-ZFC) of only reflecting CM and no AN is proposed.
Multi-frequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure
Finally, an adaptive weighting fusion (AWF) strategy is proposed to merge inference from different bands, so as to make the MF joint classification decisions of SIC and TPC.
Neuro-Dynamic State Estimation for Networked Microgrids
We devise neuro-dynamic state estimation (Neuro-DSE), a learning-based dynamic state estimation (DSE) algorithm for networked microgrids (NMs) under unknown subsystems.
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query
Paper recommendation with user-generated keyword is to suggest papers that simultaneously meet user's interests and are relevant to the input keyword.
Click-Through Rate Prediction
Graph Representation Learning
+1
Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs
Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage.
Measuring Difficulty of Novelty Reaction
In this paper, we propose to define the novelty reaction difficulty as a relative difficulty of performing the known task after the introduction of the novelty.
TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents
Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents.
A Framework Based on Generational and Environmental Response Strategies for Dynamic Multi-objective Optimization
In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages.
Measurement of carbon finance level and exploration of its influencing factors
Faced with increasingly severe environmental problems, carbon trading markets and related financial activities aiming at limiting carbon dioxide emissions are booming.
Resilience in Industrial Internet of Things Systems: A Communication Perspective
Industrial Internet of Things is an ultra-large-scale system that is much more sophisticated and fragile than conventional industrial platforms.
Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength
Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in many business problems.
AI-aided multiscale modeling of physiologically-significant blood clots
We have developed an AI-aided multiple time stepping (AI-MTS) algorithm and multiscale modeling framework (AI-MSM) and implemented them on the Summit-like supercomputer, AIMOS.
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.
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance.
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications.
Personalized Image Aesthetics Assessment with Rich Attributes
To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists of 31, 220 images with annotations by 438 subjects.
An Audio-Visual Attention Based Multimodal Network for Fake Talking Face Videos Detection
DeepFake based digital facial forgery is threatening the public media security, especially when lip manipulation has been used in talking face generation, the difficulty of fake video detection is further improved.
Attention-Based Lip Audio-Visual Synthesis for Talking Face Generation in the Wild
Rather than focusing on the unimportant regions of the face image, the proposed AttnWav2Lip model is able to pay more attention on the lip region reconstruction.
Audio-visual speech separation based on joint feature representation with cross-modal attention
Multi-modal based speech separation has exhibited a specific advantage on isolating the target character in multi-talker noisy environments.
Look\&Listen: Multi-Modal Correlation Learning for Active Speaker Detection and Speech Enhancement
Active speaker detection and speech enhancement have become two increasingly attractive topics in audio-visual scenario understanding.
Absolute Zero-Shot Learning
Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i. e., training a classifier with zero real data.
Learning Multiple Explainable and Generalizable Cues for Face Anti-spoofing
However, many other generalizable cues are unexplored for face anti-spoofing, which limits their performance under cross-dataset testing.
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.
Signal Shaping for Semantic Communication Systems with A Few Message Candidates
Semantic communications target to reliably convey the semantic meaning of messages.
An Intelligent Self-driving Truck System For Highway Transportation
Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry.
MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer
Pancreatic cancer is one of the most malignant cancers in the world, which deteriorates rapidly with very high mortality.
Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT
Correlations between imaging findings and clinical lab tests suggested the value of this system as a potential tool to assess disease severity of COVID-19.
Prior Attention Network for Multi-Lesion Segmentation in Medical Images
The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks.
Modeling Variable Space with Residual Tensor Networks for Multivariate Time Series
In this framework, we derive the mathematical representation of the variable space, and then use a tensor network based on the idea of low-rank approximation to model the variable space.
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing
To address these limitations, we design a Dynamic Style Manipulation Network (DyStyle) whose structure and parameters vary by input samples, to perform nonlinear and adaptive manipulation of latent codes for flexible and precise attribute control.
Phy-Q as a measure for physical reasoning intelligence
Inspired by how human IQ is calculated, we define the physical reasoning quotient (Phy-Q score) that reflects the physical reasoning intelligence of an agent using the physical scenarios we considered.
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking
To solve this problem, we propose to distill representations of the TIR modality from the RGB modality with Cross-Modal Distillation (CMD) on a large amount of unlabeled paired RGB-TIR data.
Hi-Phy: A Benchmark for Hierarchical Physical Reasoning
In this paper, we propose a new benchmark for physical reasoning that allows us to test individual physical reasoning capabilities.
WASE: Learning When to Attend for Speaker Extraction in Cocktail Party Environments
In the speaker extraction problem, it is found that additional information from the target speaker contributes to the tracking and extraction of the target speaker, which includes voiceprint, lip movement, facial expression, and spatial information.
Path-based Deep Network for Candidate Item Matching in Recommenders
PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.
VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations
To address the above limitations, we propose a unified framework VSR for document layout analysis, combining vision, semantics and relations.
Ranked #3 on
Document Layout Analysis
on PubLayNet val
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features.
Ranked #7 on
Table Recognition
on PubTabNet
GIPA: General Information Propagation Algorithm for Graph Learning
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
A Feature Fusion-Net Using Deep Spatial Context Encoder and Nonstationary Joint Statistical Model for High Resolution SAR Image Classification
To address this problem, a novel end-to-end supervised classification method is proposed for HR SAR images by considering both spatial context and statistical features.
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
Herein, we present GraphTheta, the first distributed and scalable graph learning system built upon vertex-centric distributed graph processing with neural network operators implemented as user-defined functions.
Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems
Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids.
Learning Position and Target Consistency for Memory-based Video Object Segmentation
To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM.
A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
This review summarizes the work of Raman spectroscopy in identifying the composition of substances and reviews the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence.
OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services
Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools.
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism
In this paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT slices based on dilated convolution and a novel dual attention mechanism to address the issues above.
Neuro-Reachability of Networked Microgrids
A neural ordinary differential equations network (ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties.
Understanding and Leveraging Causal Relations in Deep Reinforcement Learning
Reinforcement Learning (RL) has shown great potential to deal with sequential decision-making problems.
TextTN: Probabilistic Encoding of Language on Tensor Network
As a novel model that bridges machine learning and quantum theory, tensor network (TN) has recently gained increasing attention and successful applications for processing natural images.
Fluctuations in crystalline plasticity
We link this new size effect with other related phenomena like size dependence of strength ("smaller is stronger") and the size induced switch between different hardening mechanisms.
Mesoscale and Nanoscale Physics Materials Science Statistical Mechanics Computational Physics
Residual Matrix Product State for Machine Learning
Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning.
Overcoming Language Priors with Self-supervised Learning for Visual Question Answering
Our method can compensate for the data biases by generating balanced data without introducing external annotations.
A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes
To handle different types of Many-Objective Optimization Problems (MaOPs), Many-Objective Evolutionary Algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space.
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.
Audio-visual Speech Separation with Adversarially Disentangled Visual Representation
In our model, we use the face detector to detect the number of speakers in the scene and use visual information to avoid the permutation problem.
Nonlinear Cooperative Control of Double Drone-Bar Transportation System
Due to the limitation of the drone's load capacity, various specific tasks need to be accomplished by multiple drones in collaboration.
Dynamical Systems Systems and Control Systems and Control
Noise Reduction Technique for Raman Spectrum using Deep Learning Network
It is shown that output SNR of the proposed noise reduction technology is 10. 24 dB greater than that of the wavelet noise reduction method while the RMSE and the MAPE are 292. 63 and 10. 09, which are much better than the proposed technique.
Differential Reflecting Modulation for Reconfigurable Intelligent Surface Based Communications
Reconfigurable intelligent surface (RIS) based communications have emerged as a new paradigm.
TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling
Transformer has been widely-used in many Natural Language Processing (NLP) tasks and the scaled dot-product attention between tokens is a core module of Transformer.
Signal Shaping for Non-Uniform Beamspace Modulated mmWave Hybrid MIMO Communications
Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems.
TRIE: End-to-End Text Reading and Information Extraction for Document Understanding
Since real-world ubiquitous documents (e. g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic.
Revisiting Regex Generation for Modeling Industrial Applications by Incorporating Byte Pair Encoder
The results demonstrate the effectiveness of our method, which outperforms the baseline on 10 kinds of data and achieves nearly 50 percent improvement on average.
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization
However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i. e., localization without training data that have known location labels).
Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures: A Machine Learning Based Approach
This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures.
KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge
Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to fine-tune suboptimal prior knowledge.
Encoding word order in complex embeddings
The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions.
Reflecting Modulation
This paper proposes a reflecting modulation (RM) scheme for RIS-based communications, where both the reflecting patterns and transmit signals can carry information.
Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis
Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge.
Balancing Covariates in Randomized Experiments with the Gram-Schmidt Walk Design
Asymptotically, the design perfectly balances all linear functions of a growing number of covariates with a diminishing reduction in robustness, effectively allowing experimenters to escape the compromise between balance and robustness in large samples.
Methodology Data Structures and Algorithms Statistics Theory Statistics Theory
Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network
CV-FCN employs a complex downsampling-then-upsampling scheme to extract dense features.
Leveraging Entanglement Entropy for Deep Understanding of Attention Matrix in Text Matching
We prove that by a mapping (via the trace operator) on the high-dimensional matching matrix, a low-dimensional attention matrix can be derived.
Interpretable Network Structure for Modeling Contextual Dependency
Then, we show that the lower bound of such a separation rank can reveal the quantitative relation between the network structure (e. g. depth/width) and the modeling ability for the contextual dependency.
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots
In this work, we propose a context-aware self-attentive NLU (CASA-NLU) model that uses multiple signals, such as previous intents, slots, dialog acts and utterances over a variable context window, in addition to the current user utterance.
MarlRank: Multi-agent Reinforced Learning to Rank
By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure.
Explainable Video Action Reasoning via Prior Knowledge and State Transitions
Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking by humans.
REAPS: Towards Better Recognition of Fine-grained Images by Region Attending and Part Sequencing
Fine-grained image recognition has been a hot research topic in computer vision due to its various applications.
ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment Analysis
Existing sentiment analysis approaches are insufficient in modelling the interactions among people.
A new Majorana platform in an Fe-As bilayer superconductor
On the other hand, some iron-pnictide (Fe-As) superconductors, such as LiFeAs, have been demonstrated to have a similar topological band structure, yet no MZM has been observed in its vortex cores, raising a question of universality of MZM presence in iron-based superconductors.
Superconductivity Strongly Correlated Electrons
A Tensorized Transformer for Language Modeling
In this paper, based on the ideas of tensor decomposition and parameters sharing, we propose a novel self-attention model (namely Multi-linear attention) with Block-Term Tensor Decomposition (BTD).
Using Restart Heuristics to Improve Agent Performance in Angry Birds
Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds.
Support Relation Analysis for Objects in Multiple View RGB-D Images
Understanding physical relations between objects, especially their support relations, is crucial for robotic manipulation.
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Face Anti-spoofing gains increased attentions recently in both academic and industrial fields.
Ranked #3 on
Face Anti-Spoofing
on SiW-Enroll5
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy.
Ranked #13 on
Node Classification
on PPI
Enhancing the long-term performance of recommender system
Notably, an optimal parameter n* of ARL existed in long-term recommendation, indicating that there is a trade-off between keeping diversity of item and user's preference to maximize the long-term recommendation accuracy.
Predictability of diffusion-based recommender systems
The results show that the higher recommendation accuracy based on diffusion algorithms can still be achieved by optimizing the way of resource allocation on a density network.
Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network
A challenge in speech production research is to predict future tongue movements based on a short period of past tongue movements.
Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning
We ex-plored the superior learning capability of deep autoencoders for unsupervised clustering of 1. 39 mil-lion bioactive molecules into band-clusters in a 3-dimensional latent chemical space.
A Generalized Language Model in Tensor Space
Theoretically, we prove that such tensor representation is a generalization of the n-gram language model.
Transformed $\ell_1$ Regularization for Learning Sparse Deep Neural Networks
To this end, we introduce a new non-convex integrated transformed $\ell_1$ regularizer to promote sparsity for DNNs, which removes both redundant connections and unnecessary neurons simultaneously.
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations.
Binarized Attributed Network Embedding
To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.
Ranked #1 on
Link Prediction
on Wiki
Unsupervised Online Video Object Segmentation with Motion Property Understanding
Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset.
A Quantum Many-body Wave Function Inspired Language Modeling Approach
The recently proposed quantum language model (QLM) aimed at a principled approach to modeling term dependency by applying the quantum probability theory.
An Efficient Approach to Learning Chinese Judgment Document Similarity Based on Knowledge Summarization
By utilizing domain ontologies for judgment documents, the core semantics of Chinese judgment documents is summarized based on knowledge blocks.
AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture
Such a well-defined template is able to support efficient accelerator designs for a broad class of computation kernels, and more importantly, drastically reduce the design space.
Distributed, Parallel, and Cluster Computing Hardware Architecture
Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition
Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning.
Diffusion Based Network Embedding
In order to solve the limitations, we propose in this paper a network diffusion based embedding method.
The 2017 AIBIRDS Competition
This paper presents an overview of the sixth AIBIRDS competition, held at the 26th International Joint Conference on Artificial Intelligence.
Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach
In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given application, targeting the Intel XeonPhi architecture.
Performance
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
Supporting Navigation of Outdoor Shopping Complexes for Visually-impaired Users through Multi-modal Data Fusion
Outdoor shopping complexes (OSC) are extremely difficult for people with visual impairment to navigate.
Temporal Feature Selection on Networked Time Series
For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a time series.
Yin and Yang: Balancing and Answering Binary Visual Questions
If the concept can be found in the image, the answer to the question is "yes", and otherwise "no".
SOM: Semantic Obviousness Metric for Image Quality Assessment
We propose to extract two types of features, one to measure the semantic obviousness of the image and the other to discover local characteristic.
Image Quality Estimation
No-Reference Image Quality Assessment
+1
Predicting Failures of Vision Systems
We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely accuracy (or failure) of a variety of computer vision systems semantic segmentation, vanishing point and camera parameter estimation, and image memorability prediction on individual input images.
