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Found 66 papers, 10 papers with code
Table-based Fact Verification with Self-labeled Keypoint Alignment
Table-based fact verification aims to verify whether a statement sentence is trusted or fake.
Joint Model Assignment and Resource Allocation for Cost-Effective Mobile Generative Services
Artificial Intelligence Generated Content (AIGC) services can efficiently satisfy user-specified content creation demands, but the high computational requirements pose various challenges to supporting mobile users at scale.
Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks
However, the scarcity of available resources on the edge pose significant challenges in deploying generative AI models.
QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE
Herein, a novel reinforcement learning based on the well-known REINFORCE algorithm is proposed.
Joint Source-Channel Optimization for UAV Video Coding and Transmission
Unlike existing methods of modeling UAV video source coding and channel transmission separately, we investigate the joint source-channel optimization issue for video coding and transmission.
Pilot-Aided Joint Time Synchronization and Channel Estimation for OTFS
This letter proposes a pilot-aided joint time synchronization and channel estimation (JTSCE) algorithm for orthogonal time frequency space (OTFS) systems.
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
The rapid evolution of multimedia and computer vision technologies requires adaptive visual model deployment strategies to effectively handle diverse tasks and varying environments.
Alleviating Non-identifiability: a High-fidelity Calibration Objective for Financial Market Simulation with Multivariate Time Series Data
Hence, this work is expected to provide not only a rigorous understanding of non-identifiability in social simulation, but an off-the-shelf high-fidelity calibration objective function for FMS.
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
The proposed method can automatically search merging configurations for multiple tasks with multi-objective optimization algorithms.
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm, namely CDM-PSL, for expensive MOBO.
Towards Geometry-Aware Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
To address these limitations, we design a Geometry-Aware Pareto set Learning algorithm named GAPL, which provides a novel geometric perspective for neural MOCO via a Pareto attention model based on hypervolume expectation maximization.
Pointer Networks Trained Better via Evolutionary Algorithms
With this advantage, this paper is able to at the first time report the results of solving 1000-dimensional TSPs by training a PtrNet on the same dimensionality, which strongly suggests that scaling up the training instances is in need to improve the performance of PtrNet on solving higher-dimensional COPs.
Diversity from Human Feedback
DivHF learns a behavior descriptor consistent with human preference by querying human feedback.
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions
In this paper, our goal is to design a simple learning paradigm for long-tail visual recognition, which not only improves the robustness of the feature extractor but also alleviates the bias of the classifier towards head classes while reducing the training skills and overhead.
Ranked #1 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer
In recent years, various companies have started to shift their data services from traditional data centers to the cloud.
Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation
Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index.
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence
With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence.
Improving Prosody for Cross-Speaker Style Transfer by Semi-Supervised Style Extractor and Hierarchical Modeling in Speech Synthesis
Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesized speech of a target speaker's timbre.
Extraction of cropland field parcels with high resolution remote sensing using multi-task learning
Parcel-level farmland information contains rich spatial distribution and boundary details, which is crucial for digital agriculture and agricultural resource surveys.
Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding
The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE.
Performance Analysis and Enhancement of Beamforming Training in 802.11ad
Since the derived BF training efficiency is an implicit function, to reveal the relationship between system parameters and BF training performance, we also derive an approximate expression of BF training efficiency.
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks
Since the problem is NP-hard due to coupled network planning and network operation stages, we develop a Two timescAle netWork Slicing (TAWS) algorithm by collaboratively integrating reinforcement learning (RL) and optimization methods, which can jointly make network planning and operation decisions.
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning
Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services.
Style-Label-Free: Cross-Speaker Style Transfer by Quantized VAE and Speaker-wise Normalization in Speech Synthesis
In order to improve the style extraction ability of the reference encoder, a style invariant and contrastive data augmentation method is proposed.
SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE
Therefore, it is still hard to perform common machine learning such as logistic regression and neural networks in high performance.
Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation
In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data.
CU-Net: LiDAR Depth-Only Completion With Coupled U-Net
We find that existing depth-only methods can obtain satisfactory results in the areas where the measurement points are almost accurate and evenly distributed (denoted as normal areas), while the performance is limited in the areas where the foreground and background points are overlapped due to occlusion (denoted as overlap areas) and the areas where there are no measurement points around (denoted as blank areas) since the methods have no reliable input information in these areas.
KeypartX: Graph-based Perception (Text) Representation
However, big data is a double-edged sword which is big in volume but unstructured in format.
DeepAuth: A DNN Authentication Framework by Model-Unique and Fragile Signature Embedding
After embedding, each model will respond distinctively to these key samples, which creates a model-unique signature as a strong tool for authentication and user identity.
Defending Backdoor Attacks on Vision Transformer via Patch Processing
We first examine the vulnerability of ViTs against various backdoor attacks and find that ViTs are also quite vulnerable to existing attacks.
One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching
However, in the existing deep supervised hashing methods, coding balance and low-quantization error are difficult to achieve and involve several losses.
Energy-Sensitive Trajectory Design and Restoration Areas Allocation for UAV-Enabled Grassland Restoration
As a result, the maximization of restoration areas turns out to be a composite of a trajectory design problem and an areas allocation problem that are highly coupled.
Over-the-Air Federated Learning via Second-Order Optimization
To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation.
Machine Learning Empowered Intelligent Data Center Networking: A Survey
To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization.
Mitigating Forgetting in Online Continual Learning with Neuron Calibration
Though recent studies have achieved remarkable progress in improving the online continual learning performance empowered by the deep neural networks-based models, many of today's approaches still suffer a lot from catastrophic forgetting, a persistent challenge for continual learning.
Networking of Internet of UAVs: Challenges and Intelligent Approaches
Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs.
Approaching the Transient Stability Boundary of a Power System: Theory and Applications
Estimating the stability boundary is a fundamental and challenging problem in transient stability studies.
Active Reinforcement Learning over MDPs
This paper proposes a framework of Active Reinforcement Learning (ARL) over MDPs to improve generalization efficiency in a limited resource by instance selection.
Augmented Synchronization of Power Systems
Here, we formulate the empirical wisdom by the concept of augmented synchronization and aim to bridge such a theory-practice gap.
Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning
This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users.
Robust Dynamic Network Embedding via Ensembles
It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.
Mitigating Forgetting in Online Continual Learning with Neuron Calibration
Though recent studies have achieved remarkable progress in improving the online continual learning performance empowered by the deep neural networks-based models, many of today's approaches still suffer a lot from catastrophic forgetting, a persistent challenge for continual learning.
Measurement methods of radial flow in relativistic heavy-ion collisions
Radial flow can be directly extracted from the azimuthal distribution of mean transverse rapidity.
Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment
Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network
Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique.
Networking and Internet Architecture Signal Processing
Robust Watermarking for Deep Neural Networks via Bi-Level Optimization
Deep neural networks (DNNs) have become state-of-the-art in many application domains.
Power Control for a URLLC-enabled UAV system incorporated with DNN-Based Channel Estimation
This problem is challenging to be solved due to the requirement of analytically tractable channel models and the non-convex characteristic as well.
Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network
Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint.
Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search
Evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability.
Few-shots Parallel Algorithm Portfolio Construction via Co-evolution
In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of training problem instances, which is often referred to as PAP construction.
Distributed Primal-Dual Optimization for Online Multi-Task Learning
Conventional online multi-task learning algorithms suffer from two critical limitations: 1) Heavy communication caused by delivering high velocity of sequential data to a central machine; 2) Expensive runtime complexity for building task relatedness.
Deep RL-based Trajectory Planning for AoI Minimization in UAV-assisted IoT
Due to the flexibility and low deployment cost, unmanned aerial vehicles (UAVs) have been widely used to assist cellular networks in providing extended coverage for Internet of Things (IoT) networks.
Optimal Stochastic and Online Learning with Individual Iterates
In this paper, we propose a theoretically sound strategy to select an individual iterate of the vanilla SCMD, which is able to achieve optimal rates for both convex and strongly convex problems in a non-smooth learning setting.
Parallel Exploration via Negatively Correlated Search
Empirical results show that the significant advantages of NCS over the compared state-of-the-art methods can be highly owed to the effective parallel exploration ability.
Fast mmwave Beam Alignment via Correlated Bandit Learning
Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems.
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas.
Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback
Morever, the proposed algorithm can be scaled up to large-sized datasets after a relaxation.
Robust Online Multi-Task Learning with Correlative and Personalized Structures
Multi-Task Learning (MTL) can enhance a classifier's generalization performance by learning multiple related tasks simultaneously.
High-dimensional Black-box Optimization via Divide and Approximate Conquer
Divide and Conquer (DC) is conceptually well suited to high-dimensional optimization by decomposing a problem into multiple small-scale sub-problems.
Supporting Regularized Logistic Regression Privately and Efficiently
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on.
Negatively Correlated Search
This paper presents a new EA, namely Negatively Correlated Search (NCS), which maintains multiple individual search processes in parallel and models the search behaviors of individual search processes as probability distributions.
Classification and its applications for drug-target interaction identification
Classification is one of the most popular and widely used supervised learning tasks, which categorizes objects into predefined classes based on known knowledge.
Microbial community pattern detection in human body habitats via ensemble clustering framework
To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data.
The NNI Query-by-Example System for MediaEval 2014
For both symbolic and DTW search, partial sequence matching is performed to reduce missing rate, especially for query type 2 and 3.
Ranked #6 on
Keyword Spotting
on QUESST
Recognition of Nonmanual Markers in American Sign Language (ASL) Using Non-Parametric Adaptive 2D-3D Face Tracking
This paper addresses the problem of automatically recognizing linguistically significant nonmanual expressions in American Sign Language from video.
