Search Results for author:
Found 121 papers, 35 papers with code
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
Recovering realistic textures from a largely down-sampled low resolution (LR) image with complicated patterns is a challenging problem in image super-resolution.
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding
Building on this, we design a visual program that consists of three types of modules, i. e., view-independent, view-dependent, and functional modules.
Knowledge Base Enabled Semantic Communication: A Generative Perspective
Semantic communication is widely touted as a key technology for propelling the sixth-generation (6G) wireless networks.
Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems.
Efficient View Synthesis with Neural Radiance Distribution Field
Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF.
ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration
The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to resource-rich edge/cloud servers for DNN inference.
LATR: 3D Lane Detection from Monocular Images with Transformer
On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance lane information.
Ranked #1 on
3D Lane Detection
on OpenLane
WeakPolyp: You Only Look Bounding Box for Polyp Segmentation
In contrast, polyp bounding box annotations are much cheaper and more accessible.
Universal Semi-supervised Model Adaptation via Collaborative Consistency Training
In this paper, we introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA), which i) requires only a pre-trained source model, ii) allows the source and target domain to have different label sets, i. e., they share a common label set and hold their own private label set, and iii) requires only a few labeled samples in each class of the target domain.
Over-the-Air Computation in OFDM Systems with Imperfect Channel State Information
In particular, we consider two scenarios with best-effort and error-constrained computation tasks, with the objectives of minimizing the average computation mean squared error (MSE) and the computation outage probability over the multiple subcarriers, respectively.
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets
High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design.
YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp Detection
YONA fully exploits the information of one previous adjacent frame and conducts polyp detection on the current frame without multi-frame collaborations.
Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning
Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
Heterogeneous Value Evaluation for Large Language Models
In this paper, we propose A2EHV, an Automated Alignment Evaluation with a Heterogeneous Value system that (1) is automated to minimize individual human biases, and (2) allows assessments against various target values to foster heterogeneous agents.
Hierarchical Weight Averaging for Deep Neural Networks
Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs).
SCoDA: Domain Adaptive Shape Completion for Real Scans
Thus, we propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data.
Crossword: A Semantic Approach to Data Compression via Masking
The traditional methods for data compression are typically based on the symbol-level statistics, with the information source modeled as a long sequence of i. i. d.
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Due to the motion-centric nature, our method shows its impressive generalizability with limited training labels and provides good differentiability for end-to-end cycle training.
MVImgNet: A Large-scale Dataset of Multi-view Images
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
Efficient Large-scale Scene Representation with a Hybrid of High-resolution Grid and Plane Features
Based on this hybrid representation, we propose a fast optimization NeRF variant, called GP-NeRF, that achieves better rendering results while maintaining a compact model size.
HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling
We firmly think an intermediate representation is essential, but we argue that orientation map using the dominant filtering-based methods is sensitive to uncertain noise and far from a competent representation.
Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors
Specifically, we bridge the latent space of Get3DHuman with that of StyleGAN-Human via a specially-designed prior network, where the input latent code is mapped to the shape and texture feature volumes spanned by the pixel-aligned 3D reconstructor.
Adaptive Context Selection for Polyp Segmentation
To tackle these issues, we propose an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM).
Benchmarking the Robustness of LiDAR Semantic Segmentation Models
In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions.
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion
In practice, the proposed BEV@DC model comprehensively takes advantage of LiDARs with rich geometric details in training, employing an enhanced depth completion manner in inference, which takes only images (RGB and depth) as input.
Output-Dependent Gaussian Process State-Space Model
Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade.
MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction
The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner.
BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations
In practice, box annotations are applied to alleviate the over-fitting issue of previous polyp segmentation models, which generate fine-grained polyp area through the iterative boosted segmentation model.
Geometry-Aware Network for Domain Adaptive Semantic Segmentation
Moreover, to improve the 2D classifier in the target domain, we perform domain-invariant geometric adaptation from source to target and unify the 2D semantic and 3D geometric segmentation results in two domains.
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking
Then, the noised previous state is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values.
MetaLoc: Learning to Learn Wireless Localization
The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately.
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking
The unlabeled data of the DST task is incorporated into the self-training iterations, where the pseudo labels are predicted by a DST model trained on limited labeled data in advance.
Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
Specifically, this paper introduces a simple but effective point cloud cross-modality training (PointCMT) strategy, which utilizes view-images, i. e., rendered or projected 2D images of the 3D object, to boost point cloud analysis.
Ranked #10 on
3D Point Cloud Classification
on ModelNet40
APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation
In 3D medical image segmentation, small targets segmentation is crucial for diagnosis but still faces challenges.
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead.
M$^2$-3DLaneNet: Exploring Multi-Modal 3D Lane Detection
Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature.
Ranked #5 on
3D Lane Detection
on OpenLane
Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach
Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users.
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives
This dynamically personalized FL technique incentivizes clients to participate in personalizing local models while allowing the adoption of the global model when it performs better.
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning
To accelerate the training process, we propose a truncated vertical federated learning (T-VFL) algorithm, where the training latency is highly reduced by integrating the standard VFL algorithm with a channel-aware user scheduling policy.
Composable Text Controls in Latent Space with ODEs
This paper proposes a new efficient approach for composable text operations in the compact latent space of text.
Ranked #2 on
Unsupervised Text Style Transfer
on Yelp
Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images.
Toward Explainable and Fine-Grained 3D Grounding through Referring Textual Phrases
Recent progress in 3D scene understanding has explored visual grounding (3DVG) to localize a target object through a language description.
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge.
Networked Sensing in 6G Cellular Networks: Opportunities and Challenges
This article will discuss about the possibility of exploiting the future sixth-generation (6G) cellular network to realize ISAC.
Service Delay Minimization for Federated Learning over Mobile Devices
In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices.
Multi-level Consistency Learning for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain.
Ranked #2 on
Semi-supervised Domain Adaptation
on VisDA2017
DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation
Experimental results on $4, 773$ dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving.
Ranked #1 on
Object Tracking
on KITTI
X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning
Thus, a more faithful caption can be generated only using point clouds during the inference.
PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds
Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated.
Representation Learning
Weakly supervised Semantic Segmentation
+1
PVSeRF: Joint Pixel-, Voxel- and Surface-Aligned Radiance Field for Single-Image Novel View Synthesis
We present PVSeRF, a learning framework that reconstructs neural radiance fields from single-view RGB images, for novel view synthesis.
Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication
This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC).
DArch: Dental Arch Prior-Assisted 3D Tooth Instance Segmentation With Weak Annotations
Experimental results on 4, 773 dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.
ETHSeg: An Amodel Instance Segmentation Network and a Real-World Dataset for X-Ray Waste Inspection
Inspired by the fact that X-ray has a strong penetrating power to see through the bag and overlapping objects, we propose to perform waste inspection efficiently using X-ray images without the need to open the bag.
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
To tackle this problem, we propose the CLEVR3D, a large-scale VQA-3D dataset consisting of 171K questions from 8, 771 3D scenes.
Federated Two-stage Learning with Sign-based Voting
In this paper, we design a federated two-stage learning framework that augments prototypical federated learning with a cut layer on devices and uses sign-based stochastic gradient descent with the majority vote method on model updates.
Pixel-level Intra-domain Adaptation for Semantic Segmentation
Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks.
Joint LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems
First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated.
Device-Free Sensing in OFDM Cellular Network
A novel two-phase sensing framework is proposed to localize the passive targets that cannot transmit/receive reference signals to/from the base stations (BSs), where the ranges of the targets are estimated based on their reflected OFDM signals to the BSs in Phase I, and the location of each target is estimated based on its ranges to different BSs in Phase II.
Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19.
Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds
Current 3D single object tracking approaches track the target based on a feature comparison between the target template and the search area.
Ranked #2 on
Object Tracking
on KITTI
Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment
Thus, we propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification (CPC) through directly using white-light (WL) colonoscopy images in the examination.
Shallow Feature Matters for Weakly Supervised Object Localization
In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly.
Shallow Attention Network for Polyp Segmentation
To address the above issues, we propose the Shallow Attention Network (SANet) for polyp segmentation.
Ranked #9 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling
Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces.
Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference
In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset.
Task-Aware Sampling Layer for Point-Wise Analysis
Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds.
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
Towards Heterogeneous Clients with Elastic Federated Learning
Training in heterogeneous and potentially massive networks introduces bias into the system, which is originated from the non-IID data and the low participation rate in reality.
RevCore: Review-augmented Conversational Recommendation
Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items.
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery
In this paper, we address a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarding points in a case-agnostic manner (i. e. without additional storage for point relationship)?
Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems
In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied.
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey
Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, which can confidently involve diversified latent demands and personalized services in the foreseeable future.
Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images
Grounding referring expressions in RGBD image has been an emerging field.
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems
Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance.
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging.
A Comprehensive Survey on 6G Networks:Applications, Core Services, Enabling Technologies, and Future Challenges
Cellular Internet of Things (IoT) is considered as de facto paradigm to improve the communication and computation systems.
Networking and Internet Architecture
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
The problem is formulated as an optimization problem whose goal is to maximize the reliability of the VR network by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs.
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks
To this end, the fundamentals of this framework are first introduced.
LapsCore: Language-Guided Person Search via Color Reasoning
The key point of language-guided person search is to construct the cross-modal association between visual and textual input.
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input.
Ranked #3 on
3D Semantic Scene Completion
on SemanticKITTI
3D Semantic Scene Completion from a single RGB image
3D Semantic Segmentation
+3
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks
Analytical results show that, the proposed VD-RL algorithm is guaranteed to converge to a local optimal solution of the non-convex optimization problem.
On the Adversarial Robustness of LASSO Based Feature Selection
We formulate the modification strategy of the adversary as a bi-level optimization problem.
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points.
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks
Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users.
Delay Minimization for Federated Learning Over Wireless Communication Networks
In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features.
UVeQFed: Universal Vector Quantization for Federated Learning
We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.
Wireless Communications for Collaborative Federated Learning
However, due to resource constraints and privacy challenges, edge IoT devices may not be able to transmit their collected data to a central controller for training machine learning models.
Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks
Meanwhile, the probability that the DBS serves over 50% of user requests increases about 27%, compared to the baseline policy gradient algorithm.
Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs.
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc.
Peeking into occluded joints: A novel framework for crowd pose estimation
Although occlusion widely exists in nature and remains a fundamental challenge for pose estimation, existing heatmap-based approaches suffer serious degradation on occlusions.
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks
This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme.
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise.
Ranked #25 on
Semantic Segmentation
on S3DIS
Scalable Learning Paradigms for Data-Driven Wireless Communication
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy.
Optimal Feature Manipulation Attacks Against Linear Regression
In this paper, we investigate how to manipulate the coefficients obtained via linear regression by adding carefully designed poisoning data points to the dataset or modify the original data points.
FPConv: Learning Local Flattening for Point Convolution
We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis.
Artificial Intelligence Aided Next-Generation Networks Relying on UAVs
Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments.
Convergence Time Optimization for Federated Learning over Wireless Networks
Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step.
Point Cloud Segmentation based on Hypergraph Spectral Clustering
Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis.
Hypergraph Spectral Analysis and Processing in 3D Point Cloud
Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings.
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm.
On the Adversarial Robustness of Subspace Learning
We first characterize the optimal rank-one attack strategy that maximizes the subspace distance between the subspace learned from the original data matrix and that learned from the modified data matrix.
Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications
Signal processing over graphs has recently attracted significant attentions for dealing with structured data.
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT
The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT.
A General $\mathcal{O}(n^2)$ Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM
Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays.
Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach
Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way.
Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image
Given a single depth image, our method first goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view depth, and integrating all depth into the point cloud.
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions.
Learning Mutually Local-global U-nets For High-resolution Retinal Lesion Segmentation in Fundus Images
Due to high-resolution and small-size lesion regions, applying existing methods, such as U-Nets, to perform segmentation on fundus photography is very challenging.
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine
Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms.
A Two-Step Learning and Interpolation Method for Location-Based Channel Database
Timely and accurate knowledge of channel state information (CSI) is necessary to support scheduling operations at both physical and network layers.
Fast Similarity Search via Optimal Sparse Lifting
Similarity search is a fundamental problem in computing science with various applications and has attracted significant research attention, especially in large-scale search with high dimensions.
Multitask Gaussian Process with Hierarchical Latent Interactions
The function interaction is modeled by using cross convolution of latent functions.
CaricatureShop: Personalized and Photorealistic Caricature Sketching
To construct the mapping between 2D sketches and a vertex-wise scaling field, a novel deep learning architecture is developed.
Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint
Submodular maximization problems belong to the family of combinatorial optimization problems and enjoy wide applications.
Detection of Cooperative Interactions in Logistic Regression Models
For the case when the underlying interaction graph is known to be acyclic, it is shown that a simple algorithm that is based on a maximum-weight spanning tree with respect to the plug-in estimates of the influences not only has strong theoretical performance guarantees, but can also outperform generic feature selection algorithms for recovering the interaction graph from i. i. d.
Cognitive Learning of Statistical Primary Patterns via Bayesian Network
How to learn such a BN structure is a long standing issue, not fully understood even in the statistical learning community.
