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Found 68 papers, 20 papers with code
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time.
CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference
In response to the rising interest in large multimodal models, we introduce Cross-Attention Token Pruning (CATP), a precision-focused token pruning method.
Open-Vocabulary Scene Text Recognition via Pseudo-Image Labeling and Margin Loss
In this paper, we propose a novel open-vocabulary text recognition framework, Pseudo-OCR, to recognize OOV words.
Understanding Missingness in Time-series Electronic Health Records for Individualized Representation
The lack of focus on missingness representation in an individualized way limits the full utilization of machine learning applications towards true personalization.
Causal Learning for Trustworthy Recommender Systems: A Survey
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records
In IGNITE, we further propose a novel individualized missingness mask (IMM), which helps our model generate values based on the individual's observed data and missingness patterns.
GanFinger: GAN-Based Fingerprint Generation for Deep Neural Network Ownership Verification
To address these issues, we propose a network fingerprinting approach, named as GanFinger, to construct the network fingerprints based on the network behavior, which is characterized by network outputs of pairs of original examples and conferrable adversarial examples.
Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNs
Despite Graph neural networks' significant performance gain over many classic techniques in various graph-related downstream tasks, their successes are restricted in shallow models due to over-smoothness and the difficulties of optimizations among many other issues.
AiluRus: A Scalable ViT Framework for Dense Prediction
Specifically, at the intermediate layer of the ViT, we utilize a spatial-aware density-based clustering algorithm to select representative tokens from the token sequence.
From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
By simply equipping it with an MLP layer for alignment, DINO surpasses CLIP in fine-grained related perception tasks.
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners
In order to better obtain both discrimination and diversity, we propose a simple but effective Hybrid Distillation strategy, which utilizes both the supervised/CL teacher and the MIM teacher to jointly guide the student model.
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology
Secondly, dependency on domain labels prevents the use of pathology images without domain labels to improve model performance.
Text Is All You Need: Learning Language Representations for Sequential Recommendation
In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets.
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation
In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas.
Learning to Personalize Recommendation based on Customers' Shopping Intents
In this work, we introduce Amazon's new system that explicitly identifies and utilizes each customer's high level shopping intents for personalizing recommendations.
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism
In response to this problem, we introduce the Coordinate Attention (CA) module to replace the Res Block to reduce the number of parameters, and cooperate with the spatial information extraction network above to strengthen the information extraction ability.
Adapting Shortcut With Normalizing Flow: An Efficient Tuning Framework for Visual Recognition
To mitigate the computational and storage demands, recent research has explored Parameter-Efficient Fine-Tuning (PEFT), which focuses on tuning a minimal number of parameters for efficient adaptation.
Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks
Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process.
A Framework for Mutual Information-based MIMO Integrated Sensing and Communication Beamforming Design
For the case of a single communication user, we consider three types of echo interference, no interference, a point interference, and an extended interference.
Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts
Histopathology images are gigapixel-sized and include features and information at different resolutions.
Integrated Sensing and Communication Beamforming Design Based on Mutual Information
A closed-form solution with low complexity and a solution based on the semidefinite relaxation (SDR) method are provided to solve these two problems, respectively.
Learning from Students: Online Contrastive Distillation Network for General Continual Learning
The goal of General Continual Learning (GCL) is to preserve learned knowledge and learn new knowledge with constant memory from an infinite data stream where task boundaries are blurry.
Adaptive Domain Interest Network for Multi-domain Recommendation
Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously.
An efficient real-time target tracking algorithm using adaptive feature fusion
To address this issue, an efficient real-time target tracking method based on a low-dimension adaptive feature fusion is proposed to allow us the simultaneous implementation of the high-accuracy and real-time target tracking.
Coarse-to-Fine Sparse Sequential Recommendation
We propose to model user dynamics from shopping intents and interacted items simultaneously.
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
We develop and investigate a personalizable deep metric model that captures both the internal contents of items and how they were interacted with by users.
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization.
AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications
Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused crown-root-bone structures with high fidelity and accurate occlusal and dentition information.
PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling
This paper proposes a point cloud feature extraction network named PointSCNet, to capture the geometrical structure information and local region correlation information of a point cloud.
Ranked #39 on
3D Point Cloud Classification
on ModelNet40
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network
GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality.
ApolloRL: a Reinforcement Learning Platform for Autonomous Driving
We introduce ApolloRL, an open platform for research in reinforcement learning for autonomous driving.
Contrastive Regression for Domain Adaptation on Gaze Estimation
Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance.
Learning Canonical F-Correlation Projection for Compact Multiview Representation
With this framework as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping.
Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications
Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data.
Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling
Different from traditional point cloud representation where each point only represents a position or a local plane in the 3D space, each point in Neural Points represents a local continuous geometric shape via neural fields.
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information
End-to-end learning robotic manipulation with high data efficiency is one of the key challenges in robotics.
Enhanced Memory Network: The novel network structure for Symbolic Music Generation
Symbolic melodies generation is one of the essential tasks for automatic music generation.
Understanding Self-supervised Learning via Information Bottleneck Principle
To address this issue, we introduce the information bottleneck principle and propose the Self-supervised Variational Information Bottleneck (SVIB) learning framework.
Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision
Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes.
Dynamic Selection in Algorithmic Decision-making
This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data.
Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo Multilabel
However, cross-lingual SER remains a challenge in real-world applications due to a great difference between the source and target domain distributions.
FDN: Finite Difference Network with Hierarchical Convolutional Features for Text-independent Speaker Verification
For example, RawNet and RawNet2 extracted speaker's feature embeddings from waveforms automatically for recognizing their voice, which can vastly reduce the front-end computation and obtain state-of-the-art performance.
A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition
The shallow stream is used to acquire traditional shallow features that is beneficial for the classification of phones or words while the deep stream is used to obtain utterance-level speaker-invariant deep features for improving the feature diversity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Wavelet Transform-assisted Adaptive Generative Modeling for Colorization
Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples.
Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge
To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure.
Deep Music Retrieval for Fine-Grained Videos by Exploiting Cross-Modal-Encoded Voice-Overs
In this paper, we also investigate the widely added voice-overs in short videos and propose a novel framework to retrieve BGM for fine-grained short videos.
Causal Reinforcement Learning: An Instrumental Variable Approach
In the standard data analysis framework, data is first collected (once for all), and then data analysis is carried out.
Truncation-Free Matching System for Display Advertising at Alibaba
When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.
Simulation on the Transparency of Electrons and Ion Back Flow for a Time Projection Chamber based on Staggered Multiple THGEMs
The IBF and the transparent rate of electrons are two essential indicators of TPC, which affect the energy resolution and counting rate respectively.
Instrumentation and Detectors High Energy Physics - Experiment
Joint Intensity-Gradient Guided Generative Modeling for Colorization
Furthermore, the joint intensity-gradient constraint in data-fidelity term is proposed to limit the degree of freedom within generative model at the iterative colorization stage, and it is conducive to edge-preserving.
A Reversed and Shift Sparse Array Scheme based on the Difference and Sum Co-array
The reversed and shift (RAS) sparse array scheme, which is based on the difference and sum co-array (DSCA) and remarkably enhances the capability of identifying sources, is proposed.
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
Publicly Verifiable Databases With All Efficient Updating Operations
As a result, it remains an open problem how to construct an efficient (and publicly verifiable) VDB scheme that can support all updating operations regardless of the manner of insertion.
Fast Reinforcement Learning for Anti-jamming Communications
This letter presents a fast reinforcement learning algorithm for anti-jamming communications which chooses previous action with probability $\tau$ and applies $\epsilon$-greedy with probability $(1-\tau)$.
Controllable List-wise Ranking for Universal No-reference Image Quality Assessment
First, to extend the authentically distorted image dataset, we present an imaging-heuristic approach, in which the over-underexposure is formulated as an inverse of Weber-Fechner law, and fusion strategy and probabilistic compression are adopted, to generate the degraded real-world images.
DASGrad: Double Adaptive Stochastic Gradient
Adaptive moment methods have been remarkably successful for optimization under the presence of high dimensional or sparse gradients, in parallel to this, adaptive sampling probabilities for SGD have allowed optimizers to improve convergence rates by prioritizing examples to learn efficiently.
Universal Transforming Geometric Network
The recurrent geometric network (RGN), the first end-to-end differentiable neural architecture for protein structure prediction, is a competitive alternative to existing models.
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification
Based on this intuition, a Product Quantization Zero-Shot Learning (PQZSL) method is proposed to learn embeddings as well as quantizers to compress visual features into compact codes for Approximate NN (ANN) search.
Double Adaptive Stochastic Gradient Optimization
We observe that the benefits of~\textsc{DASGrad} increase with the model complexity and variability of the gradients, and we explore the resulting utility in extensions of distribution-matching multitask learning.
Security Matters: A Survey on Adversarial Machine Learning
Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-trained model to make mistake.
A Real-time Robotic Grasp Approach with Oriented Anchor Box
The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors.
Robotics
Zero-Shot Learning by Generating Pseudo Feature Representations
Given the dataset of seen classes and side information of unseen classes (e. g. attributes), we synthesize feature-level pseudo representations for novel concepts, which allows us access to the formulation of unseen class predictor.
Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm
Object detection when provided image-level labels instead of instance-level labels (i. e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain.
Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization
In this paper we discuss Bayesian nonconvex penalization for sparse learning problems.
