Search Results for author:
Found 240 papers, 84 papers with code
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
Attention Guided Graph Convolutional Networks for Relation Extraction
Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text.
Ranked #25 on
Relation Extraction
on TACRED
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries
Experimental results show that these two improvements enable to attain higher accuracy than the current state-of-the-arts, which is given by finer resolution reconstruction, for example, with small objects and object boundaries.
Ranked #61 on
Monocular Depth Estimation
on NYU-Depth V2
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on
Reading Comprehension (One-Shot)
on DuReader
Cloze (multi-choices) (Few-Shot)
Cloze (multi-choices) (One-Shot)
+19
Learning to Count Objects in Natural Images for Visual Question Answering
Visual Question Answering (VQA) models have struggled with counting objects in natural images so far.
Ranked #32 on
Visual Question Answering (VQA)
on VQA v2 test-std
Learning Motion Priors for 4D Human Body Capture in 3D Scenes
To prove the effectiveness of the proposed motion priors, we combine them into a novel pipeline for 4D human body capture in 3D scenes.
Visualization of Convolutional Neural Networks for Monocular Depth Estimation
We formulate it as an optimization problem of identifying the smallest number of image pixels from which the CNN can estimate a depth map with the minimum difference from the estimate from the entire image.
Generating 3D People in Scenes without People
However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e. g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions.
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models
In this paper, we present a novel approach, Joint Visual and Text Prompting (VTPrompt), that employs fine-grained visual information to enhance the capability of MLLMs in VQA, especially for object-oriented perception.
Grasping Field: Learning Implicit Representations for Human Grasps
Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud.
Deep Set Prediction Networks
Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result.
EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices
Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view.
LEAD: Liberal Feature-based Distillation for Dense Retrieval
Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model.
Allies: Prompting Large Language Model with Beam Search
With the advance of large language models (LLMs), the research field of LLM applications becomes more and more popular and the idea of constructing pipelines to accomplish complex tasks by stacking LLM API calls come true.
Compositional Human-Scene Interaction Synthesis with Semantic Control
Furthermore, inspired by the compositional nature of interactions that humans can simultaneously interact with multiple objects, we define interaction semantics as the composition of varying numbers of atomic action-object pairs.
Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation.
LEAP: Learning Articulated Occupancy of People
Substantial progress has been made on modeling rigid 3D objects using deep implicit representations.
SAGA: Stochastic Whole-Body Grasping with Contact
Given an initial pose and the generated whole-body grasping pose as the start and end of the motion respectively, we design a novel contact-aware generative motion infilling module to generate a diverse set of grasp-oriented motions.
Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
In contrast, the second-stage is the local diffusion, which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules.
Ranked #1 on
Motion Synthesis
on FineDance
Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views
One of the biggest challenges of this task is severe body truncation due to close social distances in egocentric scenarios, which brings large pose ambiguities for unseen body parts.
An Unsupervised Sentence Embedding Method by Mutual Information Maximization
However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce.
Ranked #20 on
Semantic Textual Similarity
on STS16
Learning Multi-granularity User Intent Unit for Session-based Recommendation
Specifically, we creatively propose Multi-granularity Intent Heterogeneous Session Graph which captures the interactions between different granularity intent units and relieves the burden of long-dependency.
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors.
PLACE: Proximity Learning of Articulation and Contact in 3D Environments
To synthesize realistic human-scene interactions, it is essential to effectively represent the physical contact and proximity between the body and the world.
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description.
Ranked #1 on
KG-to-Text Generation
on ENT-DESC
FSPool: Learning Set Representations with Featurewise Sort Pooling
Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem.
Learning Representations of Sets through Optimized Permutations
Representations of sets are challenging to learn because operations on sets should be permutation-invariant.
Is ChatGPT a Good Recommender? A Preliminary Study
We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models.
Bootstrapped Unsupervised Sentence Representation Learning
As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention.
Better Set Representations For Relational Reasoning
Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope.
Empirical Study of Zero-Shot NER with ChatGPT
Large language models (LLMs) exhibited powerful capability in various natural language processing tasks.
PopulAtion Parameter Averaging (PAPA)
Based on this idea, we propose PopulAtion Parameter Averaging (PAPA): a method that combines the generality of ensembling with the efficiency of weight averaging.
Data-Efficient Image Quality Assessment with Attention-Panel Decoder
Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents.
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals.
Temporal Human Action Segmentation via Dynamic Clustering
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring.
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.
EndHiC: assemble large contigs into chromosomal-level scaffolds using the Hi-C links from contig ends
As the Hi-C links of two adjacent contigs concentrate only at the neighbor ends of the contigs, larger contig size will reduce the power to differentiate adjacent (signal) and non-adjacent (noise) contig linkages, leading to a higher rate of mis-assembly.
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc.
Claim-Evidence Pair Extraction (CEPE)
Claim Extraction with Stance Classification (CESC)
+1
Exploring Correlations in Multiple Facial Attributes through Graph Attention Network
Estimating multiple attributes from a single facial image gives comprehensive descriptions on the high level semantics of the face.
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation
Recently, the embedding-based recommendation models (e. g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and flexibility.
Revisiting Self-Training for Few-Shot Learning of Language Model
In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM.
Slate-Aware Ranking for Recommendation
Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage.
Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period.
Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image
In this paper, we construct an urban topological map network using OpenStreetMap data in Wuhan, China, and compute a semantic representation of the scene as a whole at the street scale using a large-scale pre-trained model.
Estimation of genome size using k-mer frequencies from corrected long reads
We show that corrected third-generation data can be used to count k-mer frequencies and estimate genome size reliably, in replacement of using second-generation data.
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation
Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets.
Analyzing and Evaluating Faithfulness in Dialogue Summarization
The factual correctness of summaries has the highest priority before practical applications.
On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
Graph CF has attracted more and more attention in recent years due to its effectiveness in leveraging high-order information in the user-item bipartite graph for better recommendations.
Learning To Teach Large Language Models Logical Reasoning
Large language models (LLMs) have gained enormous attention from both academia and industry, due to their exceptional ability in language generation and extremely powerful generalization.
USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval
(2) It limits the scale of negative sample pairs by employing the mini-batch based end-to-end training mechanism.
Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models
Exploring the application of powerful large language models (LLMs) on the named entity recognition (NER) task has drawn much attention recently.
DiffAIL: Diffusion Adversarial Imitation Learning
To address this issue, we propose a method named diffusion adversarial imitation learning (DiffAIL), which introduces the diffusion model into the AIL framework.
DynaEval: Unifying Turn and Dialogue Level Evaluation
Effective evaluation metrics should reflect the dynamics of such interaction.
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity.
Improving LLM-based Machine Translation with Systematic Self-Correction
Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT).
Unlocking Slot Attention by Changing Optimal Transport Costs
Slot attention is a powerful method for object-centric modeling in images and videos.
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
While various forms of models are proposed for the link prediction task, most of them are designed based on a few known relation patterns in several well-known datasets.
Disentangle-based Continual Graph Representation Learning
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data.
MoE-TinyMed: Mixture of Experts for Tiny Medical Large Vision-Language Models
Mixture of Expert Tuning (MoE-Tuning) has effectively enhanced the performance of general MLLMs with fewer parameters, yet its application in resource-limited medical settings has not been fully explored.
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation
Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection.
DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs
Subsequently, we propose Filter Choices based Reasoning (FCR) to improve model performance on MCQs with low ($\mathcal{CS}$).
Frontal Low-rank Random Tensors for Fine-grained Action Segmentation
In this paper, we propose an approach to representing high-order information for temporal action segmentation via a simple yet effective bilinear form.
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
Graphics
Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL
Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL (Structured Query Language) representations.
Ranked #2 on
Text-To-SQL
on SParC
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions.
Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images
By stacking various layers of CSA blocks, we propose the Fourier Complex Transformer (FCT) model to learn global contextual information from VHR aerial images following the hierarchical manners.
A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems
However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information.
U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling
Novel data acquisition schemes have been an emerging need for scanning microscopy based imaging techniques to reduce the time in data acquisition and to minimize probing radiation in sample exposure.
Selective Information Passing for MR/CT Image Segmentation
However, not all the feature maps transmitted by those connections contribute positively to the network performance.
Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints
The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening.
Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers
In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation Challenge and Expression (Expr) Classification Challenge.
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking
Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, ignoring unlabelled data in the target domain.
How Well Do Text Embedding Models Understand Syntax?
Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data.
Truncating Wide Networks using Binary Tree Architectures
By employing the proposed architecture on a baseline wide network, we can construct and train a new network with same depth but considerably less number of parameters.
SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints
However, existing point-based methods usually are not adequate to extract the local features and the spatial pattern of a point cloud for further shape understanding.
Benchmarking Micro-action Recognition: Dataset, Methods, and Applications
It offers insights into the feelings and intentions of individuals and is important for human-oriented applications such as emotion recognition and psychological assessment.
Ranked #1 on
Micro-Action Recognition
on MA-52
Progressive Multi-stage Feature Mix for Person Re-Identification
In this work, we propose a Progressive Multi-stage feature Mix network (PMM), which enables the model to find out the more precise and diverse features in a progressive manner.
SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks
From these results we observe that deep neural network based training results in superior performance when the training and testing images are not similar.
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing
Translating natural language queries into SQLs in a seq2seq manner has attracted much attention recently.
Ranked #8 on
Text-To-SQL
on spider
Object-centric architectures enable efficient causal representation learning
Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees (up to some reasonable equivalence class).
How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery
In this work, a spatial-dependent graph neural networks (GNNs) approach is proposed to reveal the relation between spatial structure and restoration quality on an urban scale.
Contrastive Latent Variable Models for Neural Text Generation
Experiments on various text generation benchmarks show the effectiveness of our proposed method.
3D Shape Segmentation via Shape Fully Convolutional Networks
3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images.
A New Finitely Controllable Class of Tuple Generating Dependencies: The Triangularly-Guarded Class
In this paper we introduce a new class of tuple-generating dependencies (TGDs) called triangularly-guarded (TG) TGDs.
High Performance Visual Tracking with Circular and Structural Operators
Based on the proposed circular and structural operators, a set of primal confidence score maps can be obtained by circular correlating feature maps with their corresponding structural correlation filters.
A Novel Low-cost FPGA-based Real-time Object Tracking System
In current visual object tracking system, the CPU or GPU-based visual object tracking systems have high computational cost and consume a prohibitive amount of power.
A Novel Parallel Ray-Casting Algorithm
The Ray-Casting algorithm is an important method for fast real-time surface display from 3D medical images.
Loop Restricted Existential Rules and First-order Rewritability for Query Answering
In particular, we prove that this language satisfies the so-called bounded derivation-depth prop- erty (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in AC0 .
A New Decidable Class of Tuple Generating Dependencies: The Triangularly-Guarded Class
In this paper we introduce a new class of tuple-generating dependencies (TGDs) called triangularly-guarded TGDs, which are TGDs with certain restrictions on the atomic derivation track embedded in the underlying rule set.
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision.
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids
In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i. e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE).
Deep Learning for Secure Mobile Edge Computing
Our proposed model can be used to detect malicious applications at the edge of a cellular network, which is a serious security threat.
When Traffic Flow Prediction Meets Wireless Big Data Analytics
Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns.
Information Potential Auto-Encoders
In order to estimate the entropy of the encoding variables and the mutual information, we propose a non-parametric method.
Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling
Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage.
Data preprocessing methods for robust Fourier ptychographic microscopy
Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging technique with both high resolution and wide field-of-view.
Social Computing for Mobile Big Data in Wireless Networks
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain.
Expressive Completeness of Existential Rule Languages for Ontology-based Query Answering
In this paper, we prove that disjunctive embedded dependencies exactly capture the class of recursively enumerable ontologies in Ontology-based Conjunctive Query Answering (OCQA).
Integrating Deep Features for Material Recognition
We propose a method for integration of features extracted using deep representations of Convolutional Neural Networks (CNNs) each of which is learned using a different image dataset of objects and materials for material recognition.
Query Answering with Inconsistent Existential Rules under Stable Model Semantics
Surprisingly, for R-acyclic existential rules with R-stratified or guarded existential rules with stratified negations, both the data complexity and combined complexity of query answering under the rule {repair semantics} remain the same as that under the conventional query answering semantics.
Existential Rule Languages with Finite Chase: Complexity and Expressiveness
Finite chase, or alternatively chase termination, is an important condition to ensure the decidability of existential rule languages.
Expressiveness of Logic Programs under General Stable Model Semantics
The stable model semantics had been recently generalized to non-Herbrand structures by several works, which provides a unified framework and solid logical foundations for answer set programming.
A Logical Study of Partial Entailment
We introduce a novel logical notion--partial entailment--to propositional logic.
Variable Forgetting in Reasoning about Knowledge
Given a background knowledge base and a set of observable variables for each agent, we show that the notion of an agent knowing a formula can be defined as a weakest sufficient condition of the formula under background knowledge base.
Suspicious Object Recognition Method in Video Stream Based on Visual Attention
Bottom up and top down attention are applied respectively in the process of acquiring interested object(saliency map) and object recognition.
Disjunctive Logic Programs versus Normal Logic Programs
This paper focuses on the expressive power of disjunctive and normal logic programs under the stable model semantics over finite, infinite, or arbitrary structures.
VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification
Multi-view deep neural network is perhaps the most successful approach in 3D shape classification.
Top-K Influential Nodes in Social Networks: A Game Perspective
In this paper, we study influence maximization from a game perspective.
Automated segmentaiton and classification of arterioles and venules using Cascading Dilated Convolutional Neural Networks
The change of retinal vasculature is an early sign of many vascular and systematic diseases, such as diabetes and hypertension.
An Empirical Study towards Understanding How Deep Convolutional Nets Recognize Falls
Detecting unintended falls is essential for ambient intelligence and healthcare of elderly people living alone.
Feature Quantization for Defending Against Distortion of Images
In this work, we address the problem of improving robustness of convolutional neural networks (CNNs) to image distortion.
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants
With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc.
Fractional spectral graph wavelets and their applications
One of the key challenges in the area of signal processing on graphs is to design transforms and dictionaries methods to identify and exploit structure in signals on weighted graphs.
PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation
Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part.
Ranked #14 on
3D Part Segmentation
on ShapeNet-Part
(Class Average IoU metric)
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus
In this paper, we propose a distributed off-policy actor critic method to solve multi-agent reinforcement learning problems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Compositional Network Embedding
Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem.
Siamese Attentional Keypoint Network for High Performance Visual Tracking
In this paper, we investigate the impacts of three main aspects of visual tracking, i. e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization.
Joint Label Prediction based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation
To enrich prior knowledge to enhance the discrimination, RS2ACF clearly uses class information of labeled data and more importantly propagates it to unlabeled data by jointly learning an explicit label indicator for unlabeled data.
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model.
Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications
To simplify the decision-making process, we first decompose the MDP into a series of per-VUE-pair MDPs.
Neural IR Meets Graph Embedding: A Ranking Model for Product Search
Recently, neural models for information retrieval are becoming increasingly popular.
Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network.
Learning Reinforced Attentional Representation for End-to-End Visual Tracking
Although numerous recent tracking approaches have made tremendous advances in the last decade, achieving high-performance visual tracking remains a challenge.
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates.
A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret
In this paper, we consider the problem of distributed online convex optimization, where a network of local agents aim to jointly optimize a convex function over a period of multiple time steps.
A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy
An image dataset of 10 different size molecules, where each molecule has 2, 000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories.
Towards Universal Languages for Tractable Ontology Mediated Query Answering
On the negative side, we prove that there is, in general, no universal language for each of these families of languages.
Short-term Load Forecasting with Dense Average Network
Therefore, improving the accuracy of power load forecasting has always been the pursuing goals for a power system.
Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features.
Collaborative Generated Hashing for Market Analysis and Fast Cold-start Recommendation
Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems.
Semantic Change Pattern Analysis
Change detection is an important problem in vision field, especially for aerial images.
Transfer Reinforcement Learning under Unobserved Contextual Information
Then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples.
Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets
Moreover, the vagueness of satire and news parody determines that a news tweet can hardly be classified with a binary decision, that is, satirical or legitimate.
Cooperative Multi-Agent Reinforcement Learning with Partial Observations
The advantage of the proposed zeroth-order policy optimization method is that it allows the agents to compute the local policy gradients needed to update their local policy functions using local estimates of the global accumulated rewards that depend on partial state and action information only and can be obtained using consensus.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A New One-Point Residual-Feedback Oracle For Black-Box Learning and Control
When optimizing a deterministic Lipschitz function, we show that the query complexity of ZO with the proposed one-point residual feedback matches that of ZO with the existing two-point schemes.
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids
To cope with this growth, intelligent management of the consumption profile of the households is necessary, such that the households can save the electricity bills, and the stress to the power grid during peak hours can be reduced.
Learning Posterior and Prior for Uncertainty Modeling in Person Re-Identification
This paper proposes to learn the sample posterior and the class prior distribution in the latent space, so that not only representative features but also the uncertainty can be built by the model.
Perpetual Motion: Generating Unbounded Human Motion
To address this problem, we propose a model to generate non-deterministic, \textit{ever-changing}, perpetual human motion, in which the global trajectory and the body pose are cross-conditioned.
A Survey on Concept Factorization: From Shallow to Deep Representation Learning
In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods.
Finding structural hole spanners based on community forest model and diminishing marginal utility in large scale social networks
In this paper, we try to give a more visual and detailed definition of structural hole spanner based on the existing work, and propose a novel algorithm to identify structural hole spanner based on community forest model and diminishing marginal utility.
Finding overlapping community from social networks based on community forest model
Overlapping community detection is the key research work to discover and explore the social networks.
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces.
Time-Based Roofline for Deep Learning Performance Analysis
Deep learning applications are usually very compute-intensive and require a long run time for training and inference.
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback
As a result, our regret bounds are much tighter compared to existing regret bounds for ZO with conventional one-point feedback, which suggests that ZO with residual feedback can better track the optimizer of online optimization problems.
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters.
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things
Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial devices to achieve Industry 4. 0 benefits.
DisenE: Disentangling Knowledge Graph Embeddings
Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently.
Deep Pairwise Hashing for Cold-start Recommendation
Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation.
Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation
Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce.
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
In this paper, we integrate DRL and permissioned blockchain into vehicular networks for intelligent and secure content caching.
Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond
In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption.
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks
Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency.
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks
In this paper, we introduce the Digital Twin Wireless Networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane.
``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
4D Human Body Capture from Egocentric Video via 3D Scene Grounding
We introduce a novel task of reconstructing a time series of second-person 3D human body meshes from monocular egocentric videos.
We are More than Our Joints: Predicting how 3D Bodies Move
We note that motion prediction methods accumulate errors over time, resulting in joints or markers that diverge from true human bodies.
Dense Small Satellite Networks for Modern Terrestrial Communication Systems: Benefits, Infrastructure, and Technologies
Dense small satellite networks (DSSN) in low earth orbits (LEO) can benefit several mobile terrestrial communication systems (MTCS).
Secret Key Generation for Intelligent Reflecting Surface Assisted Wireless Communication Networks
We propose and analyze secret key generation using intelligent reflecting surface (IRS) assisted wireless communication networks.
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
Quantifying the importance of firms by means of reputation and network control
The reputation of firms is largely channeled through their ownership structure.
Physics and Society Systems and Control General Economics Systems and Control Economics
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
Learning to Represent and Predict Sets with Deep Neural Networks
In this thesis, we develop various techniques for working with sets in machine learning.
Formal Verification of Stochastic Systems with ReLU Neural Network Controllers
Using this abstraction, we propose a method to compute tight bounds on the safety probabilities of nodes in this graph, despite possible over-approximations of the transition probabilities between these nodes.
A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
We assume that this context is not accessible to a learner agent who can only observe the expert data.
r-GAT: Relational Graph Attention Network for Multi-Relational Graphs
To tackle this issue, we propose r-GAT, a relational graph attention network to learn multi-channel entity representations.
Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion
To this end, both the marginal distribution and the elements with high-confidence correlations are aligned over the common space of the query and key vectors which are computed from different modalities.
ChicHealth @ MEDIQA 2021: Exploring the limits of pre-trained seq2seq models for medical summarization
In this article, we will describe our system for MEDIQA2021 shared tasks.
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
The sequence representation plays a key role in the learning of matching degree between the dialogue context and the response.
A bi-diffusion based layer-wise sampling method for deep learning in large graphs
The Graph Convolutional Network (GCN) and its variants are powerful models for graph representation learning and have recently achieved great success on many graph-based applications.
Learning Robust Representation through Graph Adversarial Contrastive Learning
Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features.
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction
At its core is a new lighting model (dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph Convolutional Network (GCN) that infers the new lighting representation from a single LDR image of limited field-of-view.
The Wanderings of Odysseus in 3D Scenes
In our solution, we decompose the long-term motion into a time sequence of motion primitives.
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System
Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval.
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations
Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues.
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification
In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework for DECI, which improves existing state-of-the-art (SOTA) methods upon two aspects.
Joint Communication and Sensing: Models and Potential of Using MIMO
In this survey, we discuss joint communication and sensing (JCAS) in the context of MIMO.
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation
For 3D medical image (e. g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly.
Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation
Axie infinity is a complicated card game with a huge-scale action space.
PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening
Most of the available deep learning-based pan-sharpening methods sharpen the multispectral images through a one-step scheme, which strongly depends on the reconstruction ability of the network.
LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation
Recently, many efforts have been devoted to improving Tag-aware recommendation systems (TRS) with Graph Convolutional Networks (GCN), which has become new state-of-the-art for the general recommendation.
A High Fidelity Simulation Framework for Potential Safety Benefits Estimation of Cooperative Pedestrian Perception
This paper proposes a high-fidelity simulation framework that can estimate the potential safety benefits of vehicle-to-infrastructure (V2I) pedestrian safety strategies.
Equivariance with Learned Canonicalization Functions
Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations.
In vivo labeling and quantitative imaging of neurons using MRI
Developing in vivo neuronal labeling and imaging techniques is crucial for studying the structure and function of neural circuits.
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
We approach the problem of improving robustness of deep learning algorithms in the presence of label noise.
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high performance code generation of the deep neural network graph.
Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan
Conclusions: The proposed deep learning algorithms can be an effective tool for early identification of hemorrhage etiologies based on NCCT scans.
Homophily-oriented Heterogeneous Graph Rewiring
To this end, we propose HDHGR, a homophily-oriented deep heterogeneous graph rewiring approach that modifies the HG structure to increase the performance of HGNN.
Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things
In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering.
