Search Results for author:
Found 217 papers, 68 papers with code
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications.
LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
Namely, the accuracy of most current LLM-based methods and the generality of optimization problem types that they can model are still limited.
From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning
Large vision language models (VLMs) combine large language models with vision encoders, demonstrating promise across various tasks.
Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images
The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction.
Koopman Spectral Analysis from Noisy Measurements based on Bayesian Learning and Kalman Smoothing
Koopman spectral analysis plays a crucial role in understanding and modeling nonlinear dynamical systems as it reveals key system behaviors and long-term dynamics.
SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers
This is achieved through the analysis and grouping of tokens according to their spatial autocorrelation scores prior to their input into the Feed-Forward Network (FFN) block of the self-attention mechanism.
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications.
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Knowledge representation has been a central aim of AI since its inception.
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs
This paper introduces a novel and efficient One-pass Generation and retrieval framework (OneGen), designed to improve LLMs' performance on tasks that require both generation and retrieval.
Power Line Aerial Image Restoration under dverse Weather: Datasets and Baselines
To circumvent this problem, we propose a new task of Power Line Aerial Image Restoration under Adverse Weather (PLAIR-AW), which aims to recover clean and high-quality images from degraded images with bad weather thus improving detection performance for PLAI.
Making Large Vision Language Models to be Good Few-shot Learners
By constructing a rich set of meta-tasks for instruction fine-tuning, LVLMs enhance the ability to extract information from few-shot support data for classification.
EraW-Net: Enhance-Refine-Align W-Net for Scene-Associated Driver Attention Estimation
Associating driver attention with driving scene across two fields of views (FOVs) is a hard cross-domain perception problem, which requires comprehensive consideration of cross-view mapping, dynamic driving scene analysis, and driver status tracking.
Generative Sentiment Analysis via Latent Category Distribution and Constrained Decoding
To address the challenges related to category semantic inclusion and overlap, a latent category distribution variable is introduced.
Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting
Despite recent advances with transformer-based forecasting models, challenges remain due to the non-stationary, nonlinear characteristics of workload time series and the long-term dependencies.
Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information
Existing cross-document event coreference resolution models, which either compute mention similarity directly or enhance mention representation by extracting event arguments (such as location, time, agent, and patient), lacking the ability to utilize document-level information.
Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm
Document-level event extraction aims to extract structured event information from unstructured text.
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
LLMs fine-tuned with this data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval.
Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration
A comprehensive guidance view for cardiac interventional surgery can be provided by the real-time fusion of the intraoperative 2D images and preoperative 3D volume based on the ultrasound frame-to-volume registration.
GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting
These methods merely take the temporal hierarchical structure to maintain coherence without improving the forecasting accuracy.
Asymmetrical Siamese Network for Point Clouds Normal Estimation
Evaluation of existing methods on this new dataset reveals their inability to adapt to different types of shapes, indicating a degree of overfitting.
Personalized Binomial DAGs Learning with Network Structured Covariates
Causal discovery aims to recover the DAG structure using observational data.
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
In recent years, large language models (LLMs) have made remarkable achievements in various domains.
Vim-F: Visual State Space Model Benefiting from Learning in the Frequency Domain
In recent years, State Space Models (SSMs) with efficient hardware-aware designs, known as the Mamba deep learning models, have made significant progress in modeling long sequences such as language understanding.
Your decision path does matter in pre-training industrial recommenders with multi-source behaviors
Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in.
Keypoint-based Progressive Chain-of-Thought Distillation for LLMs
Chain-of-thought distillation is a powerful technique for transferring reasoning abilities from large language models (LLMs) to smaller student models.
Scale-Invariant Feature Disentanglement via Adversarial Learning for UAV-based Object Detection
Specifically, a Scale-Invariant Feature Disentangling module is designed to disentangle scale-related and scale-invariant features.
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
Going beyond the mainstream paradigms of plain decomposition and multiperiodicity analysis, we analyze temporal variations in a novel view of multiscale-mixing, which is based on an intuitive but important observation that time series present distinct patterns in different sampling scales.
Refining 3D Point Cloud Normal Estimation via Sample Selection
In recent years, point cloud normal estimation, as a classical and foundational algorithm, has garnered extensive attention in the field of 3D geometric processing.
Hummer: Towards Limited Competitive Preference Dataset
Preference datasets are essential for incorporating human preferences into pre-trained language models, playing a key role in the success of Reinforcement Learning from Human Feedback.
CSA-Net: Channel-wise Spatially Autocorrelated Attention Networks
Inspired by geographical analysis, the proposed CSA exploits the spatial relationships between channels of feature maps to produce an effective channel descriptor.
Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration
By using topology-preserved deformation features extracted from the probabilistic diffeomorphic registration model, abdominal motion can be accurately obtained and utilized for DVF estimation.
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
Long-range spatial-spectral correlation modeling is beneficial for HSI denoising but often comes with high computational complexity.
ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving
ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions.
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
Bayesian flow networks (BFNs) iteratively refine the parameters, instead of the samples in diffusion models (DMs), of distributions at various noise levels through Bayesian inference.
Aligning Knowledge Graphs Provided by Humans and Generated from Neural Networks in Specific Tasks
This paper develops an innovative method that enables neural networks to generate and utilize knowledge graphs, which describe their concept-level knowledge and optimize network parameters through alignment with human-provided knowledge.
TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting
Leveraging the point-based Gaussian Splatting, facial motions can be represented in our method by applying smooth and continuous deformations to persistent Gaussian primitives, without requiring to learn the difficult appearance change like previous methods.
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
Previous works only address part of the stragglers and could not adaptively solve various stragglers in practice.
AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster
Offline batch inference is a common task in the industry for deep learning applications, but it can be challenging to ensure stability and performance when dealing with large amounts of data and complicated inference pipelines.
Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder
These approaches overlook the potential benefits of integrating multiple data sources, such as Light Detection and Ranging (LiDAR), and is further challenged by the limited availability of labeled data in HSI processing, which represents a significant obstacle.
LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification
The fusion of hyperspectral and LiDAR data has been an active research topic.
AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation
Blind super-resolution methods based on stable diffusion showcase formidable generative capabilities in reconstructing clear high-resolution images with intricate details from low-resolution inputs.
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
In this paper, we propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures.
HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification
HSIMamba is designed to process data bidirectionally, significantly enhancing the extraction of spectral features and integrating them with spatial information for comprehensive analysis.
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors
However, we argue that a critical obstacle remains in deploying LLMs for practical use: the efficiency of LLMs when processing long textual user behaviors.
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students).
Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness
It is also significant to model the \textit{semantic relatedness} reflected in content features, e. g., images and text.
DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization
Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting, despite it will encounter a geometry degradation when input views decrease.
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift.
Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning
In this paper, we fill in this gap by proposing a motif-preserving Graph Neural Network with curriculum learning (MotifGNN) to jointly learn the lower-order structures from the original graph and higherorder structures from multi-view motif-based graphs for financial default prediction.
ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
This motivated us to explore domain-specific modeling in chat-based language models as a solution for extracting structured information from natural language.
Can Small Language Models be Good Reasoners for Sequential Recommendation?
We encode the generated rationales from the student model into a dense vector, which empowers recommendation in both ID-based and ID-agnostic scenarios.
Resolution-Agnostic Neural Compression for High-Fidelity Portrait Video Conferencing via Implicit Radiance Fields
In this paper, we propose a novel low bandwidth neural compression approach for high-fidelity portrait video conferencing using implicit radiance fields to achieve both major objectives.
CMNER: A Chinese Multimodal NER Dataset based on Social Media
Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images.
Enhancing Zero-shot Counting via Language-guided Exemplar Learning
Recently, Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency compared to Category-Specific Counting (CSC).
MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction
The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment.
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems
However, the existing systems are not tailored for meta learning based DLRM models and have critical problems regarding efficiency in distributed training in the GPU cluster.
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry.
XUAT-Copilot: Multi-Agent Collaborative System for Automated User Acceptance Testing with Large Language Model
In past years, we have been dedicated to automating user acceptance testing (UAT) process of WeChat Pay, one of the most influential mobile payment applications in China.
A Comprehensive Study of Knowledge Editing for Large Language Models
In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches.
Ranked #1 on
knowledge editing
on zsRE
(using extra training data)
FC-GNN: Recovering Reliable and Accurate Correspondences from Interferences
Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades.
Efficient Model Stealing Defense with Noise Transition Matrix
With the escalating complexity and investment cost of training deep neural networks safeguarding them from unauthorized usage and intellectual property theft has become imperative.
Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer
This block consists of a spatial branch and a spectral branch.
GazeCLIP: Towards Enhancing Gaze Estimation via Text Guidance
Over the past decade, visual gaze estimation has garnered increasing attention within the research community, owing to its wide-ranging application scenarios.
An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training
To address these issues, we propose a flexible model placement framework that offers two general and agile model placement strategies.
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
In this paper, we seek to carefully prompt a Large Language Model (LLM) with domain-level knowledge as a better marketing-oriented knowledge miner for marketing-oriented knowledge graph construction, which is however non-trivial, suffering from several inevitable issues in real-world marketing scenarios, i. e., uncontrollable relation generation of LLMs, insufficient prompting ability of a single prompt, the unaffordable deployment cost of LLMs.
Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction
In particular, we elaborately devise a Meta-learning Supported Teacher-student GNN (MST-GNN) that is not only built upon teacher-student architecture for alleviating the migration between "easy" and "hard" samples but also equipped with a meta learning based sample re-weighting module for helping the student GNN distinguish "hard" samples in a fine-grained manner.
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation
To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent.
Hy-Tracker: A Novel Framework for Enhancing Efficiency and Accuracy of Object Tracking in Hyperspectral Videos
Hyperspectral object tracking has recently emerged as a topic of great interest in the remote sensing community.
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner.
OptScaler: A Hybrid Proactive-Reactive Framework for Robust Autoscaling in the Cloud
The reactive module provides a self-tuning estimator of CPU utilization to the optimization model.
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems
We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems.
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt
In this work, we first investigate such anytime fine-tuning effectiveness of existing continual pre-training approaches, concluding with unanimously decreased performance on unseen domains.
Rethinking Memory and Communication Cost for Efficient Large Language Model Training
In this paper, we rethink the impact of memory consumption and communication costs on the training speed of large language models, and propose a memory-communication balanced strategy set Partial Redundancy Optimizer (PaRO).
Data-Centric Financial Large Language Models
Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.
Long-tail Augmented Graph Contrastive Learning for Recommendation
To make the data augmentation schema learnable, we design an auto drop module to generate pseudo-tail nodes from head nodes and a knowledge transfer module to reconstruct the head nodes from pseudo-tail nodes.
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries.
AntM$^{2}$C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
3) AntM$^{2}$C provides 1 billion CTR data with 200 features, including 200 million users and 6 million items.
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs.
Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model
In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively.
Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation
One critical challenge in 6D object pose estimation from a single RGBD image is efficient integration of two different modalities, i. e., color and depth.
Continual Learning in Predictive Autoscaling
In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set.
Integration of Large Language Models and Federated Learning
As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data.
Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis
This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size.
EasyTPP: Towards Open Benchmarking Temporal Point Processes
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
Generative Contrastive Graph Learning for Recommendation
Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.
InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs
Inspired by the philosophy of ``think-like-a-vertex", a GAS-like (Gather-Apply-Scatter) schema is proposed to describe the computation paradigm and data flow of GNN inference.
High Spectral Spatial Resolution Synthetic HyperSpectral Dataset form multi-source fusion
This research paper introduces a synthetic hyperspectral dataset that combines high spectral and spatial resolution imaging to achieve a comprehensive, accurate, and detailed representation of observed scenes or objects.
Deep Double Self-Expressive Subspace Clustering
The key idea of our solution is to view the self-expressive coefficient as a feature representation of the example to get another coefficient matrix.
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
However, these models primarily learn low-level features and require annotated data for fine-tuning.
Ranked #4 on
Cross-Modal Retrieval
on RSITMD
(using extra training data)
Description-Enhanced Label Embedding Contrastive Learning for Text Classification
Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets.
Dynamic Clustering Transformer Network for Point Cloud Segmentation
Specifically, we propose novel semantic feature-based dynamic sampling and clustering methods in the encoder, which enables the model to be aware of local semantic homogeneity for local feature aggregation.
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Large language models have shown astonishing performance on a wide range of reasoning tasks.
ALT: An Automatic System for Long Tail Scenario Modeling
In this paper, we consider the problem of long tail scenario modeling with budget limitation, i. e., insufficient human resources for model training stage and limited time and computing resources for model inference stage.
Few-shot Classification via Ensemble Learning with Multi-Order Statistics
To address this need, we prove theoretically that leveraging ensemble learning on the base classes can correspondingly reduce the true error in the novel classes.
COUPA: An Industrial Recommender System for Online to Offline Service Platforms
Aiming at helping users locally discovery retail services (e. g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems.
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
At last, we conduct extensive experiments on both offline and online environments, which demonstrates the superior capability of GARCIA in improving tail queries and overall performance in service search scenarios.
Towards Open Temporal Graph Neural Networks
This will bring two big challenges to the existing dynamic GNN methods: (i) How to dynamically propagate appropriate information in an open temporal graph, where new class nodes are often linked to old class nodes.
Spectral 3D Computer Vision -- A Review
Spectral 3D computer vision examines both the geometric and spectral properties of objects.
Improving Recommendation Fairness via Data Augmentation
We augment imbalanced training data towards balanced data distribution to improve fairness.
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
In recommendation scenarios, there are two long-standing challenges, i. e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks.
GCNet: Probing Self-Similarity Learning for Generalized Counting Network
The class-agnostic counting (CAC) problem has caught increasing attention recently due to its wide societal applications and arduous challenges.
The impact of access to credit on energy efficiency
This paper proposes a brand-new measure of energy efficiency at household level and explores how it is affected by access to credit.
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts.
Robust Direct Learning for Causal Data Fusion
In the era of big data, the explosive growth of multi-source heterogeneous data offers many exciting challenges and opportunities for improving the inference of conditional average treatment effects.
Low Latency Conversion of Artificial Neural Network Models to Rate-encoded Spiking Neural Networks
The maximum number of spikes in this time window is also the latency of the network in performing a single inference, as well as determines the overall energy efficiency of the model.
PP-StructureV2: A Stronger Document Analysis System
For Table Recognition model, we utilize PP-LCNet, CSP-PAN and SLAHead to optimize the backbone module, feature fusion module and decoding module, respectively, which improved the table structure accuracy by 6\% with comparable inference speed.
Ranked #1 on
Network Pruning
on CIFAR-100
(Inference Time (ms) metric)
Label Inference Attacks Against Vertical Federated Learning
However, we discover that the bottom model structure and the gradient update mechanism of VFL can be exploited by a malicious participant to gain the power to infer the privately owned labels.
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Further experiments bear out the quality of node representations learned with GraTO and the effectiveness of model architecture.
SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation
The SO(3)-equivariant features communicate with RGB features to deduce the (missed) geometry for detecting keypoints of an object with the reflective surface from the depth channel.
Ask Question First for Enhancing Lifelong Language Learning
Lifelong language learning aims to stream learning NLP tasks while retaining knowledge of previous tasks.
Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce
E-commerce has gone a long way in empowering merchants through the internet.
Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models
The Agriculture-Vision Challenge in CVPR is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors, aiming at agricultural pattern recognition from aerial images.
BadDet: Backdoor Attacks on Object Detection
We propose four kinds of backdoor attacks for object detection task: 1) Object Generation Attack: a trigger can falsely generate an object of the target class; 2) Regional Misclassification Attack: a trigger can change the prediction of a surrounding object to the target class; 3) Global Misclassification Attack: a single trigger can change the predictions of all objects in an image to the target class; and 4) Object Disappearance Attack: a trigger can make the detector fail to detect the object of the target class.
RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language Learning
In order to alleviate catastrophic forgetting, we propose the residual variational autoencoder (RVAE) to enhance LAMOL, a recent LLL model, by mapping different tasks into a limited unified semantic space.
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings
To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account.
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective
The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP
Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire.
Neural Graph Matching for Pre-training Graph Neural Networks
In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task.
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area.
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset.
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph.
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.
Benchmarking emergency department triage prediction models with machine learning and large public electronic health records
In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400, 000 ED visits from 2011 to 2019.
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Knowledge graph is generally incorporated into recommender systems to improve overall performance.
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Time series forecasting is widely used in business intelligence, e. g., forecast stock market price, sales, and help the analysis of data trend.
Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable
The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path.
Tackling the Local Bias in Federated Graph Learning
To solve this problem, we propose a novel FGL framework to make the local models similar to the model trained in a centralized setting.
Reminding the Incremental Language Model via Data-Free Self-Distillation
Incremental language learning with pseudo-data can alleviate catastrophic forgetting in neural networks.
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space
Although many efforts have been made in terms of backbone architecture design, loss function, and training techniques, few results have been obtained on how the sampling in latent space can affect the final performance, and existing works on latent space mainly focus on controllability.
PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System
Optical Character Recognition (OCR) systems have been widely used in various of application scenarios.
Optical Character Recognition
Optical Character Recognition (OCR)
SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation
Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.
A Unified Framework for Cross-Domain and Cross-System Recommendations
Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i. e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively.
Improving Transferability of Adversarial Patches on Face Recognition with Generative Models
However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models.
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature.
Improvement of Normal Estimation for PointClouds via Simplifying Surface Fitting
Firstly, a dynamic top-k selection strategy is introduced to better focus on the most critical points of a given patch, and the points selected by our learning method tend to fit a surface by way of a simple tangent plane, which can dramatically improve the normal estimation results of patches with sharp corners or complex patterns.
Fast and Accurate Normal Estimation for Point Cloud via Patch Stitching
At the stitching stage, we use the learned weights of multi-branch planar experts and distance weights between points to select the best normal from the overlapping parts.
Goal-Oriented Gaze Estimation for Zero-Shot Learning
Therefore, we introduce a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization based on the class-level attributes for ZSL.
Cross-Domain Recommendation: Challenges, Progress, and Prospects
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain.
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting
To battle the ingrained issue of accuracy degradation, we propose a novel and powerful network called Scale Tree Network (STNet) for accurate crowd counting.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction
In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising
This model first projects the observed HSIs into a low-dimensional orthogonal subspace, and then represents the projected image with a multidimensional dictionary.
ASFGNN: Automated Separated-Federated Graph Neural Network
Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.
Multi-layer Feature Aggregation for Deep Scene Parsing Models
The proposed module can auto-select the intermediate visual features to correlate the spatial and semantic information.
Ranked #53 on
Semantic Segmentation
on NYU Depth v2
Parameter Efficient Deep Neural Networks with Bilinear Projections
Recent research on deep neural networks (DNNs) has primarily focused on improving the model accuracy.