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Found 227 papers, 47 papers with code
Discriminative Partial Domain Adversarial Network
Previous methods typically match the whole source domain to target domain, which causes negative transfer due to the source-negative classes in source domain that does not exist in target domain.
Knowledge-aware Evolutionary Graph Neural Architecture Search
According to the predicted metrics, non-dominated candidate transfer architectures are selected to warm-start the multi-objective evolutionary algorithm for optimizing the #Acc and #Params on a new dataset.
Autoencoder Enhanced Realised GARCH on Volatility Forecasting
In this thesis, aiming to synthesise the impact of various realised volatility measures on volatility forecasting, we propose an extension of the Realised GARCH model that incorporates an autoencoder-generated synthetic realised measure, combining the information from multiple realised measures in a nonlinear manner.
Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data
Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery.
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
With the development of large language models (LLMs), the ability to handle longer contexts has become a key capability for Web applications such as cross-document understanding and LLM-powered search systems.
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
However, the emergence of open-world SSL (OwSSL) introduces a more practical challenge, wherein unlabeled data may encompass samples from unseen classes.
Graph Signal Processing for Global Stock Market Volatility Forecasting
The interconnectedness of global financial markets has brought increasing attention to modeling volatility spillover effects.
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated.
PMM-Net: Single-stage Multi-agent Trajectory Prediction with Patching-based Embedding and Explicit Modal Modulation
Analyzing and forecasting trajectories of agents like pedestrians plays a pivotal role for embodied intelligent applications.
PiLocNet: Physics-informed neural network on 3D localization with rotating point spread function
Our PiLocNet combines the unique strengths of both approaches by incorporating forward-model-based information into the network via a data-fitting loss term that constrains the neural network to yield results that are physically sensible.
A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension
In this work, we have developed a variational Bayesian inference theory of elasticity, which is accomplished by using a mixed Variational Bayesian inference Finite Element Method (VBI-FEM) that can be used to solve the inverse deformation problems of continua.
A Lightweight Target-Driven Network of Stereo Matching for Inland Waterways
Stereo matching for inland waterways is one of the key technologies for the autonomous navigation of Unmanned Surface Vehicles (USVs), which involves dividing the stereo images into reference images and target images for pixel-level matching.
SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning
However, applying the highly capable SSMs to SNNs for long sequences learning poses three major challenges: (1) The membrane potential is determined by the past spiking history of the neuron, leading to reduced efficiency for sequence modeling in parallel computing scenarios.
Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding
Recent development of protein language models (pLMs) with supervised fine tuning provides a promising solution to this problem.
GraphGI:A GNN Explanation Method using Game Interaction
In this work, we propose a novel explanatory method GraphGI, which identifies the coalition with the highest interaction strength and presents it as an explanatory subgraph.
Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Days
By calculating the volatility spillover index to depict the volatility network as graphs, the model effectively mirrors the volatility dynamics for the chosen stock market indices.
RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs
Existing pretrained LLMs may generate summaries that are concise but lack the necessary context for downstream tasks, hindering their utility in personalization systems.
FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks
We propose a method for formally certifying and quantifying individual fairness of deep neural networks (DNN).
UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches
Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data.
Multi-modal Adversarial Training for Zero-Shot Voice Cloning
The discriminator is used in a training pipeline that improves both the acoustic and prosodic features of a TTS model.
SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
Known as low energy consumption networks, spiking neural networks (SNNs) have gained a lot of attention within the past decades.
Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model
A long memory and non-linear realized volatility model class is proposed for direct Value at Risk (VaR) forecasting.
First Activations Matter: Training-Free Methods for Dynamic Activation in Large Language Models
Dynamic activation (DA) techniques, such as DejaVu and MoEfication, have demonstrated their potential to significantly enhance the inference efficiency of large language models (LLMs).
MambaEVT: Event Stream based Visual Object Tracking using State Space Model
More importantly, we consider introducing a dynamic template update strategy into the tracking framework using the Memory Mamba network.
52B to 1T: Lessons Learned via Tele-FLM Series
Large Language Models (LLMs) represent a significant stride toward Artificial General Intelligence.
A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs
By providing insights and guidance, this tutorial paper serves to inspire researchers to explore new horizons and fully unleash the potential of FAS.
An Outline of Prognostics and Health Management Large Model: Concepts, Paradigms, and Challenges
To this end, based on a systematic analysis of the current challenges and bottlenecks in PHM, as well as the research status and advantages of Large Model, we propose a novel concept and three progressive paradigms of Prognosis and Health Management Large Model (PHM-LM) through the integration of the Large Model with PHM.
Explainable AI Security: Exploring Robustness of Graph Neural Networks to Adversarial Attacks
Graph neural networks (GNNs) have achieved tremendous success, but recent studies have shown that GNNs are vulnerable to adversarial attacks, which significantly hinders their use in safety-critical scenarios.
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
Numerous adversarial defense methods on GNNs are proposed to address the problem of adversarial attacks.
Evolutionary Spiking Neural Networks: A Survey
Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural networks(ANNs).
MOYU: A Theoretical Study on Massive Over-activation Yielded Uplifts in LLMs
Massive Over-activation Yielded Uplifts(MOYU) is an inherent property of large language models, and dynamic activation(DA) based on the MOYU property is a clever yet under-explored strategy designed to accelerate inference in these models.
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research.
QQQ: Quality Quattuor-Bit Quantization for Large Language Models
Quantization is a proven effective method for compressing large language models.
Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field
Addressing these limitations, we present a lightweight method based on 3D Gaussian Splatting that utilizes multi-view LDR images of a scene with varying exposure times, apertures, and focus distances as input to reconstruct a high-dynamic-range (HDR) radiance field.
Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance
Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance.
Information Theoretic Text-to-Image Alignment
In a nutshell, our method uses self-supervised fine-tuning and relies on point-wise mutual information between prompts and images to define a synthetic training set to induce model alignment.
Automatic Graph Topology-Aware Transformer
Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities.
Yuan 2.0-M32: Mixture of Experts with Attention Router
Yuan 2. 0-M32, with a similar base architecture as Yuan-2. 0 2B, uses a mixture-of-experts architecture with 32 experts of which 2 experts are active.
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representations
This approach enables us to harness both the low rankness from the matrix factorization and the continuity from neural representation in a self-supervised manner. Theoretically, we prove the low-rank property and Lipschitz continuity in the proposed continuous low-rank factorization.
Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study
In this work, we systematically investigate the efficacy of dynamic activation mechanisms within the LLaMA family of language models.
Shifting the ISAC Trade-Off with Fluid Antenna Systems
As an emerging antenna technology, a fluid antenna system (FAS) enhances spatial diversity to improve both sensing and communication performance by shifting the active antennas among available ports.
Tele-FLM Technical Report
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.
PriorNet: A Novel Lightweight Network with Multidimensional Interactive Attention for Efficient Image Dehazing
Hazy images degrade visual quality, and dehazing is a crucial prerequisite for subsequent processing tasks.
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
In this paper, we propose a FLexible And Generalizable RL framework for VNE, named FlagVNE.
Prompt Learning for Oriented Power Transmission Tower Detection in High-Resolution SAR Images
The interaction of image-level and prompt-level features is utilized to address the clutter interference.
Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections
In Taiwan, although the average annual precipitation is up to 2, 500 millimeter (mm), the water allocation for each person is lower than the global average due to drastically geographical elevation changes and uneven distribution through the year.
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms.
X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention
We propose X-Portrait, an innovative conditional diffusion model tailored for generating expressive and temporally coherent portrait animation.
To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions
In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain silent to not disturb the group.
Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization
In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch.
DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node information and topology.
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales
Developing robust and interpretable vision systems is a crucial step towards trustworthy artificial intelligence.
ProtChatGPT: Towards Understanding Proteins with Large Language Models
The protein first undergoes protein encoders and PLP-former to produce protein embeddings, which are then projected by the adapter to conform with the LLM.
Semi-parametric financial risk forecasting incorporating multiple realized measures
A semi-parametric joint Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting framework employing multiple realized measures is developed.
Power Transformer Fault Prediction Based on Knowledge Graphs
In this paper, we address the challenge of learning with limited fault data for power transformers.
GeoDecoder: Empowering Multimodal Map Understanding
This paper presents GeoDecoder, a dedicated multimodal model designed for processing geospatial information in maps.
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e. g. SEC filings), where discrete reasoning capabilities are often required.
Data Augmentation for Traffic Classification
Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance.
TeleChat Technical Report
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
Re-evaluating the Memory-balanced Pipeline Parallelism: BPipe
Pipeline parallelism is an essential technique in the training of large-scale Transformer models.
DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation
The generation of emotional talking faces from a single portrait image remains a significant challenge.
Reducing Hallucinations: Enhancing VQA for Flood Disaster Damage Assessment with Visual Contexts
Experimental results show that our method exceeds the performance of state-of-the-art zero-shot VQA models for flood disaster scenarios in total.
GIT-Net: Generalized Integral Transform for Operator Learning
This article introduces GIT-Net, a deep neural network architecture for approximating Partial Differential Equation (PDE) operators, inspired by integral transform operators.
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario
Most importantly, our model uses well-designed chain of thought (CoT) demonstrations to unlock the potential of the large language model, allowing zero-shot VQA to show better performance in disaster scenarios.
YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
In this work, we develop and release Yuan 2. 0, a series of large language models with parameters ranging from 2. 1 billion to 102. 6 billion.
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model
We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code: (i) utilizing LLM-based data splitting and data renovation techniques to improve the semantic representation of embeddings' space; (ii) introducing the Chain of Density for Renovation Credibility (CoDRC), driven by LLMs, and the Adaptive Text Renovation (ATR) algorithm for assessing data renovation reliability; (iii) developing the Implicit Knowledge Expansion and Contemplation (IKEC) Prompt technique; and (iv) effectively refactoring existing scripts to generate new and high-quality scripts with LLMs.
Graph Signal Diffusion Model for Collaborative Filtering
In this paper, we adapt standard diffusion model and propose a novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF).
Toward Generative Data Augmentation for Traffic Classification
Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance.
Minimax Optimal Transfer Learning for Kernel-based Nonparametric Regression
This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert space.
NeuroQuantify -- An Image Analysis Software for Detection and Quantification of Neurons and Neurites using Deep Learning
The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation.
Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition
On the other hand, the tensor nuclear norm (TNN)-based approaches have recently demonstrated to be more efficient on keeping high-dimensional low-rank structures in tensor recovery.
Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift
Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used.
CoPAL: Corrective Planning of Robot Actions with Large Language Models
In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge.
Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers
Aim: The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy.
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation
Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL).
Data Scaling Effect of Deep Learning in Financial Time Series Forecasting
For years, researchers investigated the applications of deep learning in forecasting financial time series.
U-SEANNet: A Simple, Efficient and Applied U-Shaped Network for Diagnosis of Nasal Diseases on Nasal Endoscopic Images
Our findings suggest U-SEANNet is the state-of-the-art model for nasal diseases diagnosis in endoscopic images.
Robust retrieval of material chemical states in X-ray microspectroscopy
X-ray microspectroscopic techniques are essential for studying morphological and chemical changes in materials, providing high-resolution structural and spectroscopic information.
Systematic Testing of the Data-Poisoning Robustness of KNN
In other words, they cannot falsify the truly-non-robust cases.
Certifying the Fairness of KNN in the Presence of Dataset Bias
We propose a method for certifying the fairness of the classification result of a widely used supervised learning algorithm, the k-nearest neighbors (KNN), under the assumption that the training data may have historical bias caused by systematic mislabeling of samples from a protected minority group.
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
Fifth, we systematically present the hybridization of Bayesian inference and RL which is a promising direction to improve the convergence of RL for better motion planning.
DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration
With the two cascaded subtasks, DRM-IR first dynamically models the task-specific degradation based on a reference image pair and further restores the image with the collected degradation statistics.
The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised Framework
Our proposed substage-based distillation approach has the potential to inform future research on label-efficient learning for video data.
Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering
Dictionary learning is an effective tool for pattern recognition and classification of time series data.
Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections
In this paper, we report our exploration of efficient search in sparse data with graph-based ANN algorithms (e. g., HNSW, or SONG which is the GPU version of HNSW), which are popular in industrial practice, e. g., search and ads (advertising).
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification
The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited interest towards Traffic Classification (TC).
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
In this paper, we propose Speech-text dialog Pre-training for spoken dialog understanding with ExpliCiT cRoss-Modal Alignment (SPECTRA), which is the first-ever speech-text dialog pre-training model.
Ranked #2 on
Multimodal Sentiment Analysis
on CMU-MOSI
(Acc-2 metric, using extra
training data)
cross-modal alignment
Emotion Recognition in Conversation
+2
Preference or Intent? Double Disentangled Collaborative Filtering
In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences.
Spatial-Frequency Discriminability for Revealing Adversarial Perturbations
The vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community.
Causal Document-Grounded Dialogue Pre-training
To tackle this issue, we are the first to present a causally-complete dataset construction strategy for building million-level DocGD pre-training corpora.
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs
Discrete reasoning over table-text documents (e. g., financial reports) gains increasing attention in recent two years.
A Diffusion-based Method for Multi-turn Compositional Image Generation
We introduce a conditioning scheme to generate the target image based on the fusion results.
Deep Learning Enhanced Realized GARCH
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures.
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search
It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution.
Representing Noisy Image Without Denoising
In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing.
Image Cropping With Spatial-Aware Feature and Rank Consistency
To address the first issue, we propose spatial-aware feature to encode the spatial relationship between candidate crops and aesthetic elements, by feeding the concatenation of crop mask and selectively aggregated feature maps to a light-weighted encoder.
Context-Aware Pretraining for Efficient Blind Image Decomposition
(2) The conventional paradigm usually focuses on mining the abnormal pattern of a superimposed image to separate the noise, which de facto conflicts with the primary image restoration task.
Generative Graph Neural Networks for Link Prediction
Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated.
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
The key idea of SA-PP is leveraging discounted stationary state distribution ratios between the learning policy and the offline dataset to modulate the degree of behavior regularization in a state-wise manner, so that pessimism can be implemented in a more appropriate way.
GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild
Most in-the-wild images are stored in Low Dynamic Range (LDR) form, serving as a partial observation of the High Dynamic Range (HDR) visual world.
The path integral formula for the stochastic evolutionary game dynamics in the Moran process
In this work, we introduce the formulation of the path integral approach for evolutionary game theory based on the Moran process.
Study of detecting behavioral signatures within DeepFake videos
Our results indicate that there could be a behavioral signature that is detectable from a person's movements that is separate from their visual appearance, and that this behavioral signature could be used to distinguish a deep fake from a properly captured video.
Towards Complex Document Understanding By Discrete Reasoning
Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural Language Processing and Computer Vision.
Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection
In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation.
A semi-parametric marginalized dynamic conditional correlation framework
The performance of the proposed model in risk forecasting and portfolio allocation is evaluated by means of a forecasting study on the components of the Dow Jones index for an out-of-sample period from December 2016 to September 2021.
FL-Tuning: Layer Tuning for Feed-Forward Network in Transformer
Specifically, we focus on layer tuning for feed-forward network in the Transformer, namely FL-tuning.
Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework
We conclude that this improvement in ASC performance comes from the regularization effect of using AET and not from the network's improved ability to discern between acoustic events.
RDU: A Region-based Approach to Form-style Document Understanding
Key Information Extraction (KIE) is aimed at extracting structured information (e. g. key-value pairs) from form-style documents (e. g. invoices), which makes an important step towards intelligent document understanding.
Rethinking Reinforcement Learning based Logic Synthesis
Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process.
Attacking and Defending Deep Reinforcement Learning Policies
Recent studies have shown that deep reinforcement learning (DRL) policies are vulnerable to adversarial attacks, which raise concerns about applications of DRL to safety-critical systems.
Using Augmented Face Images to Improve Facial Recognition Tasks
We present a framework that uses GAN-augmented images to complement certain specific attributes, usually underrepresented, for machine learning model training.
A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks
Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes.
Federated Self-Supervised Learning for Acoustic Event Classification
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization.
A Principled Design of Image Representation: Towards Forensic Tasks
Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society.
Rule Mining over Knowledge Graphs via Reinforcement Learning
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.
A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0
Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD.
Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm
Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources and security constraints to ensure the rationality and security of resource allocation in ICPSs.
Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding
Resource allocation in virtual network is essentially a problem of allocating underlying resources for virtual network requests (VNRs).
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning
Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics.
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning
In addition, as the use of intelligent learning algorithm to solve the problem of VNE has become a trend, this method is gradually outdated.
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT
Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment.
IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode
This paper proposes a bandwidth aware multi domain virtual network embedding algorithm (BA-VNE).
Network Resource Allocation Strategy Based on Deep Reinforcement Learning
This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution.
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method
Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN's heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm.
LinSyn: Synthesizing Tight Linear Bounds for Arbitrary Neural Network Activation Functions
The most scalable approaches to certifying neural network robustness depend on computing sound linear lower and upper bounds for the network's activation functions.
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition
We pre-train SER model simultaneously on Automatic Speech Recognition (ASR) and sentiment classification tasks to make the acoustic ASR model more ``emotion aware''.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
MOORe: Model-based Offline-to-Online Reinforcement Learning
With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online.
Opinion Dynamics in Financial Markets via Random Networks
We investigate the financial market dynamics by introducing a heterogeneous agent-based opinion formation model.
Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection
Community detection (CD) from dynamics and network reconstruction (NR) from dynamics are natural synergistic tasks that motivate the proposed evolutionary multitasking NR and CD framework, called network collaborator (NC).
Evolutionary Multitasking AUC Optimization
Inspired by the characteristic of pairwise learning, the cheap AUC optimization task with a small-scale dataset sampled from the large-scale dataset is constructed to promote the AUC accuracy of the original, large-scale, and expensive AUC optimization task.
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training
Moreover, our case study shows that our visual solution can well reflect the characteristics of various training scenarios, showing good potential of DVI as a debugging tool for analyzing deep learning training processes.
Community detection and portfolio optimization
Community detection methods can be used to explore the structure of complex systems.
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective
To this end, in this paper, we revisit the task of topic modeling by transforming each document into a directed graph with word dependency as edges between word nodes, and develop a novel approach, namely Graph Neural Topic Model (GNTM).
Portfolio optimization with idiosyncratic and systemic risks for financial networks
The two risks are measured by the idiosyncratic variance and the network clustering coefficient derived from the asset correlation networks, respectively.
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex \emph{ObtainDiamond} task with sparse rewards.
Learning a self-supervised tone mapping operator via feature contrast masking loss
High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies.
Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs
In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model.
Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding
Experiments show that, compared with the baseline models, our proposed model can significantly improve the naturalness of converted singing voices and the similarity with the target singer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?
The acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic respiratory failure with in-hospital mortality of 35-46%.
Self-Adaptive Partial Domain Adaptation
Partial Domain adaptation (PDA) aims to solve a more practical cross-domain learning problem that assumes target label space is a subset of source label space.
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots
E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention.