Search Results for author:
Found 102 papers, 35 papers with code
On the Doubt about Margin Explanation of Boosting
Margin theory provides one of the most popular explanations to the success of \texttt{AdaBoost}, where the central point lies in the recognition that \textit{margin} is the key for characterizing the performance of \texttt{AdaBoost}.
On the Consistency of AUC Pairwise Optimization
Based on this result, we prove that exponential loss and logistic loss are consistent with AUC, but hinge loss is inconsistent.
One-Pass AUC Optimization
AUC is an important performance measure and many algorithms have been devoted to AUC optimization, mostly by minimizing a surrogate convex loss on a training data set.
Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm
Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data.
Convergence analysis of kernel LMS algorithm with pre-tuned dictionary
In addition to choosing a reproducing kernel and setting filter parameters, designing a KLMS adaptive filter requires to select a so-called dictionary in order to get a finite-order model.
Dropout Rademacher Complexity of Deep Neural Networks
Great successes of deep neural networks have been witnessed in various real applications.
Simple Effective Microblog Named Entity Recognition: Arabic as an Example
Despite many recent papers on Arabic Named Entity Recognition (NER) in the news domain, little work has been done on microblog NER.
Mouse Pose Estimation From Depth Images
We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images.
Modern Physiognomy: An Investigation on Predicting Personality Traits and Intelligence from the Human Face
The classification results show that the personality traits "Rule-consciousness" and "Vigilance" can be predicted reliably, and that the traits of females can be predicted more accurately than those of male.
On the Resistance of Nearest Neighbor to Random Noisy Labels
The theoretical results show that, for asymmetric noises, k-nearest neighbor is robust enough to classify most data correctly, except for a handful of examples, whose labels are totally misled by random noises.
Unorganized Malicious Attacks Detection
This work considers a different attack style: unorganized malicious attacks, where attackers individually utilize a small number of user profiles to attack different items without any organizer.
Effects of the optimisation of the margin distribution on generalisation in deep architectures
Despite being so vital to success of Support Vector Machines, the principle of separating margin maximisation is not used in deep learning.
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
How fake news goes viral via social media?
Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation
How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information?
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks
Automatic rumor detection is technically very challenging.
Generative Adversarial Networks with Decoder-Encoder Output Noise
The deep convolutional generative adversarial networks (DCGANs) were then proposed to leverage the quality of generated images.
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning
Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem.
Structure Learning of Deep Neural Networks with Q-Learning
Based on reinforcement learning and taking advantages of the superiority of these networks, we propose a novel automatic process to design a multi-block neural network, whose architecture contains multiple types of blocks mentioned above, with the purpose to do structure learning of deep neural networks and explore the possibility whether different blocks can be composed together to form a well-behaved neural network.
FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization
Additionally, we present a simple and efficient twist parameterization that generalizes our method to the registration of articulated and deformable objects.
A security steganography scheme based on hdr image
And numerous researches are concentrated on spatial domain and jpeg domain.
kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation
However, representing an object with a parameterized transformation defined on a fixed template cannot capture large intra-category shape variation, and specifying a target pose at a category level can be physically infeasible or fail to accomplish the task -- e. g. knowing the pose and size of a coffee mug relative to some canonical mug is not sufficient to successfully hang it on a rack by its handle.
Robotics
An extended description logic system with knowledge element based on ALC
With the rise of knowledge management and knowledge economy, the knowledge elements that directly link and embody the knowledge system have become the research focus and hotspot in certain areas.
SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction
We contribute a dense SLAM system that takes a live stream of depth images as input and reconstructs non-rigid deforming scenes in real time, without templates or prior models.
PRUNING WITH HINTS: AN EFFICIENT FRAMEWORK FOR MODEL ACCELERATION
Hints can improve the performance of student model by transferring knowledge from teacher model.
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks
Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications.
Weighted Oblique Decision Trees
Decision trees have attracted much attention during the past decades.
kPAM-SC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion
Using the proposed hybrid object representation, we formulate the manipulation task as a motion planning problem which encodes both the object target configuration and physical feasibility for a category of objects.
Subspace Clustering for Panel Data with Interactive Effects
The factor loadings are assumed to be in different subspaces and the subspace clustering for factor loadings are considered.
Deceptive Opponent Modeling with Proactive Network Interdiction for Stochastic Goal Recognition Control
Goal recognition based on the observations of the behaviors collected online has been used to model some potential applications.
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning
Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms.
Bidirectional Trajectory Computation for Odometer-Aided Visual-Inertial SLAM
the extrinsic parameters before the first turning, which is a complement of the existing results of observability analyses.
Robotics
Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations
A U-Net is trained to recover acoustic interference striations (AISs) from distorted ones.
PointManifold: Using Manifold Learning for Point Cloud Classification
In this paper, we propose a point cloud classification method based on graph neural network and manifold learning.
Ranked #66 on
3D Point Cloud Classification
on ModelNet40
Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements
Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge.
Robotics
Privacy-preserving Collaborative Learning with Automatic Transformation Search
Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.
Debunking Rumors on Twitter with Tree Transformer
Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by {``}word-of-post{''} through social media conversations.
Towards Convergence Rate Analysis of Random Forests for Classification
We get a convergence rate O(n^{-{1}/(d+2)}(\ln n)^{{1}/(d+2)}) for the variant of random forests, which reaches the minimax rate, except for a factor (\ln n)^{{1}/(d+2)}, of the optimal plug-in classifier under the L-Lipschitz assumption.
Transient Theoretical Analysis of Diffusion RLS Algorithm for Cyclostationary Colored Inputs
Convergence of the diffusion RLS (DRLS) algorithm to steady-state has been extensively studied in the literature, whereas no analysis of its transient convergence behavior has been reported yet.
Automating LC-MS/MS mass chromatogram quantification. Wavelet transform based peak detection and automated estimation of peak boundaries and signal-to-noise ratio using signal processing methods
While there are many different methods for peak detection, no automatic methods for marking peak boundaries to calculate area under the curve (AUC) and signal-to-noise ratio (SNR) estimation exist.
Assessing Individual and Community Vulnerability to Fake News in Social Networks
In this paper we base our idea on Computational Trust in social networks to propose a novel Community Health Assessment model against fake news.
Community Detection
Social and Information Networks
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
TS-CAM finally couples the patch tokens with the semantic-agnostic attention map to achieve semantic-aware localization.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
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.
Towards Understanding Theoretical Advantages of Complex-Reaction Networks
Complex-valued neural networks have attracted increasing attention in recent years, while it remains open on the advantages of complex-valued neural networks in comparison with real-valued networks.
Transient Performance Analysis of the $\ell_1$-RLS
The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems.
Deep Visual Navigation under Partial Observability
Importantly, we integrate the multiple neural network modules into a unified controller that achieves robust performance for visual navigation in complex, partially observable environments.
Exploiting Robust Unsupervised Video Person Re-identification
A local-aware module is employed to explore the poentials of local-level feature for unsupervised learning.
Ranked #1 on
Unsupervised Person Re-Identification
on PRID2011
Unsupervised Person Re-Identification
Video-Based Person Re-Identification
Learning to Disentangle Scenes for Person Re-identification
In this way, the complex scenes in the ReID task are effectively disentangled, and the burden of each branch is relieved.
Ranked #2 on
Person Re-Identification
on P-DukeMTMC-reID
End-to-end lossless compression of high precision depth maps guided by pseudo-residual
The whole process is comprised of two sub-processes, named pre-processing of depth maps and deep lossless compression of processed depth maps.
OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution.
Multi-direction and Multi-scale Pyramid in Transformer for Video-based Pedestrian Retrieval
To fuse multi-scale feature representation, this paper presents a pyramid structure containing global-level information and many pieces of local-level information from different scales.
DialMed: A Dataset for Dialogue-based Medication Recommendation
Therefore, we make the first attempt to recommend medications with the conversations between doctors and patients.
Context-Hierarchy Inverse Reinforcement Learning
To solve these issues, we present Context Hierarchy IRL(CHIRL), a new IRL algorithm that exploits the context to scale up IRL and learn reward functions of complex behaviors.
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling
We propose a generative adversarial network for point cloud upsampling, which can not only make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions.
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning
The diffusion of rumors on microblogs generally follows a propagation tree structure, that provides valuable clues on how an original message is transmitted and responded by users over time.
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information; or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors.
On the Optimization of Margin Distribution
Margin has played an important role on the design and analysis of learning algorithms during the past years, mostly working with the maximization of the minimum margin.
Deep Geometry Post-Processing for Decompressed Point Clouds
Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission.
Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision
Deep learning (DL) shows its prosperity in a wide variety of fields.
Receding Horizon Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations.
Adaptive Random Fourier Features Kernel LMS
We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS).
BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images
The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload.
Salient Object Detection for Point Clouds
This paper researches the unexplored task-point cloud salient object detection (SOD).
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms
In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms.
Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge
In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model.
DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.
AdaNIC: Towards Practical Neural Image Compression via Dynamic Transform Routing
Compressive autoencoders (CAEs) play an important role in deep learning-based image compression, but large-scale CAEs are computationally expensive.
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering
Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies.
Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020
People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well.
Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset
Little attention has been paid on \underline{EA}rly \underline{R}umor \underline{D}etection (EARD), and EARD performance was evaluated inappropriately on a few datasets where the actual early-stage information is largely missing.
Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models
Few-shot or zero-shot fact verification only relies on a few or no labeled training examples.
Screen-based 3D Subjective Experiment Software
To accurately illustrate the perceptual quality differences of 3D stimuli, our software can simultaneously render the source stimulus and impaired stimulus and allows both stimuli to respond synchronously to viewer interactions.
Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
With the fast growth of LLM-enabled AI applications and democratization of open-souced LLMs, fine-tuning has become possible for non-expert individuals, but intensively performed LLM fine-tuning worldwide could result in significantly high energy consumption and carbon footprint, which may bring large environmental impact.
Tackling the Unlimited Staleness in Federated Learning with Intertwined Data and Device Heterogeneities
Device heterogeneity is defined as the clients' variant latencies in uploading their local model updates due to heterogeneous conditions of local hardware resources, and causes the problem of staleness when being addressed by asynchronous FL.
Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method
While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still under-explored in the misinformation domain.
Enhancing Stance Classification with Quantified Moral Foundations
The results highlight the importance of considering deeper psychological attributes in stance analysis and underscores the role of moral foundations in guiding online social behavior.
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences.
OpenDMC: An Open-Source Library and Performance Evaluation for Deep-learning-based Multi-frame Compression
Video streaming has become an essential component of our everyday routines.
A Novel Tree Model-based DNN to Achieve a High-Resolution DOA Estimation via Massive MIMO receive array
To satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity.
Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understanding
Large-scale image-language pretrained models, e. g., CLIP, have demonstrated remarkable proficiency in acquiring general multi-modal knowledge through web-scale image-text data.
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences
To address this issue, multi-frame optical flow methods leverage adjacent frames to mitigate the local ambiguity.
Adaptive Annotation Distribution for Weakly Supervised Point Cloud Semantic Segmentation
Based on our analysis, we propose a label-aware point cloud downsampling strategy to increase the proportion of annotations involved in the training stage.
Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AI
Large Language Models (LLMs) are capable of reasoning over diverse input data modalities through pre-trained encoders.
Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI
With the wide adoption of AI applications, there is a pressing need of enabling real-time neural network (NN) inference on small embedded devices, but deploying NNs and achieving high performance of NN inference on these small devices is challenging due to their extremely weak capabilities.
ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selection
To speed up on-device training, existing schemes select trainable NN portion offline or conduct unrecoverable selection at runtime, but the evolution of trainable NN portion is constrained and cannot adapt to the current need for training.
JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims
Justification is an explanation that supports the veracity assigned to a claim in fact-checking.
Uncertainty-aware No-Reference Point Cloud Quality Assessment
The evolution of compression and enhancement algorithms necessitates an accurate quality assessment for point clouds.
Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance
We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model.
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Then we propose to fine-tune a small language model as the debate judge for harmfulness inference, to facilitate multimodal fusion between the harmfulness rationales and the intrinsic multimodal information within memes.
Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference
We decompose the input data (e. g., some images) into sensitive and insensitive segments according to importance and privacy.
A general framework for rotation invariant point cloud analysis
We propose a general method for deep learning based point cloud analysis, which is invariant to rotation on the inputs.
Bringing Textual Prompt to AI-Generated Image Quality Assessment
To solve this problem, we introduce IP-IQA (AGIs Quality Assessment via Image and Prompt), a multimodal framework for AGIQA via corresponding image and prompt incorporation.
