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Found 276 papers, 98 papers with code
Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification
Convolutional neural networks (CNNs) are one of the most effective deep learning methods to solve image classification problems, but the best architecture of a CNN to solve a specific problem can be extremely complicated and hard to design.
Electric Vehicle Driver Clustering using Statistical Model and Machine Learning
Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances.
Learning neural trans-dimensional random field language models with noise-contrastive estimation
However, the training efficiency of neural TRF LMs is not satisfactory, which limits the scalability of TRF LMs on large training corpus.
Language modeling with Neural trans-dimensional random fields
The idea is to use nonlinear potentials with continuous features, implemented by neural networks (NNs), in the TRF framework.
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue.
Traffic-Aware Transmission Mode Selection in D2D-enabled Cellular Networks with Token System
We consider a D2D-enabled cellular network where user equipments (UEs) owned by rational users are incentivized to form D2D pairs using tokens.
Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition
The dominant language models (LMs) such as n-gram and neural network (NN) models represent sentence probabilities in terms of conditionals.
Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform
This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014.
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
Click prediction is one of the fundamental problems in sponsored search.
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification
In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN.
Graph-based Deep-Tree Recursive Neural Network (DTRNN) for Text Classification
A novel graph-to-tree conversion mechanism called the deep-tree generation (DTG) algorithm is first proposed to predict text data represented by graphs.
Neural Material: Learning Elastic Constitutive Material and Damping Models from Sparse Data
The challenge is that such data is sparse as it is consistently given only on part of the surface.
Graphics
Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition
As RGB view and depth view lie in different spaces, a new distance metric bag of neighbors (BON) used in MvLE can get the similar distributions of the two views.
Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach
This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes.
Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks
In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE.
Evaluating Word Embedding Models: Methods and Experimental Results
Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work.
Can Walking and Measuring Along Chord Bunches Better Describe Leaf Shapes?
A novel chord bunch walks (CBW) descriptor is developed through the chord walking that effectively integrates the shape image function over the walked chord to reflect the contour features and the inner properties of the shape.
Automatic Thumbnail Generation Based on Visual Representativeness and Foreground Recognizability
We present an automatic thumbnail generation technique based on two essential considerations: how well they visually represent the original photograph, and how well the foreground can be recognized after the cropping and downsizing steps of thumbnailing.
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks
Three major contributions of this work are: Firstly, a new encoding strategy is proposed to encode a CNN, where the architecture and the shortcut connections are encoded separately; Secondly, a hybrid two-level EC method, which combines particle swarm optimisation and genetic algorithms, is developed to search for the optimal CNNs; Lastly, an adjustable learning rate is introduced for the fitness evaluations, which provides a better learning rate for the training process given a fixed number of epochs.
Graph Attention Memory for Visual Navigation
To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.
Adaptive Convolution for Multi-Relational Learning
We consider the problem of learning distributed representations for entities and relations of multi-relational data so as to predict missing links therein.
Ranked #10 on
Link Prediction
on WN18
YNU NLP at SemEval-2019 Task 5: Attention and Capsule Ensemble for Identifying Hate Speech
We participate in both subtask A and subtask B for English.
YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language
This paper describes the system submitted to SemEval 2019 Task 6: OffensEval 2019.
Efficiently Indexing Large Sparse Graphs for Similarity Search
Abstract—The graph structure is a very important means to model schemaless data with complicated structures, such as protein- protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks.
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks
Deep Convolutional Neural Networks (CNNs) have been widely used in image classification tasks, but the process of designing CNN architectures is very complex, so Neural Architecture Search (NAS), automatically searching for optimal CNN architectures, has attracted more and more research interests.
On the Strong Equivalences of LPMLN Programs
Firstly, we present the notions of p-strong and w-strong equivalences between LPMLN programs.
On the Strong Equivalences for LPMLN Programs
In this paper, we study the strong equivalence for LPMLN programs, which is an important tool for program rewriting and theoretical investigations in the field of logic programming.
Logic in Computer Science D.1.6
From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images
Encouraging experimental results of the proposed method in comparison to the state-of-the-art leaf species recognition methods demonstrate the availability of cultivar information in soybean leaves and effectiveness of the proposed MSCM for soybean cultivar identification, which may advance the research in leaf recognition from species to cultivar.
A game method for improving the interpretability of convolution neural network
The reason is, we believe that: the network is essentially a perceptual model.
YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction
We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task.
Global Adaptive Generative Adjustment
Many traditional signal recovery approaches can behave well basing on the penalized likelihood.
Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks
In this paper, we propose an evasion attack on CNN classifiers in the context of License Plate Recognition (LPR), which adds predetermined perturbations to specific regions of license plate images, simulating some sort of naturally formed spots (such as sludge, etc.).
Towards cross-platform automated rotation electron diffraction
A DigitalMicrograph script InsteaDMatic has been developed to facilitate rapid automated continuous rotation electron diffraction (cRED) data acquisition.
Materials Science
An Improved Coarse-to-Fine Method for Solving Generation Tasks
The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage.
TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples
Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples. Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes perturbation. Therefore, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point problem. The method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples.
Feature-Driven Super-Resolution for Object Detection
Comparing with some state-of-the-art SR algorithms with 4$\times$ scale factor, FDSR outperforms the detection performance mAP on MS COCO validation, VOC2007 databases with good generalization to other detection networks.
Unified Multi-Criteria Chinese Word Segmentation with BERT
Besides, the pre-trained BERT language model has been also introduced into the MCCWS task in a multi-task learning framework.
Attention-based Transducer for Online Speech Recognition
We propose attention-based transducer with modification over RNN-T in two aspects.
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning.
Modeling Discourse Structure for Document-level Neural Machine Translation
Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder.
Xiaomi's Submissions for IWSLT 2020 Open Domain Translation Task
This paper describes the Xiaomi{'}s submissions to the IWSLT20 shared open domain translation task for Chinese{\textless}-{\textgreater}Japanese language pair.
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification
A new effective and efficient surrogate-assisted particle swarm optimisation algorithm is proposed to automatically evolve convolutional neural networks.
A Novel Line Integral Transform for 2D Affine-Invariant Shape Retrieval
While conducting the trace transform once only generates a single feature and multiple trace transforms of different functionals are needed to derive more to make the descriptors informative.
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
This dynamic defense can prohibit the adversary from selecting an optimal substitute model for black-box attacks.
Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach
To realize effective TRUST-TECH searches to train deep neural networks on large datasets, a dynamic search paths (DSP) method is proposed to provide an improved search guidance in TRUST-TECH method.
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning
Thirdly, the agent’s learning process is regarded as a black-box, and the comprehensive metric we proposed is computed after each episode of training, then a Bayesian optimization (BO) algorithm is adopted to guide the agent to evolve towards improving the quality of the approximated Pareto frontier.
Bayesian Optimization
Multi-Objective Reinforcement Learning
+1
An Adversarial Attack via Feature Contributive Regions
Recently, to deal with the vulnerability to generate examples of CNNs, there are many advanced algorithms that have been proposed.
Robust Memory Augmentation by Constrained Latent Imagination
The latent dynamics model summarizes an agent’s high dimensional experiences in a compact way.
Ranking Cost: One-Stage Circuit Routing by Directly Optimizing Global Objective Function
In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object.
A novel combination of theoretical analysis and data-driven method for reconstruction of structural defects
To address this issue, a novel approach to quantitative reconstruction of defects using the integration of data-driven method with the guided wave scattering analysis has been proposed in this paper.
Computational Engineering, Finance, and Science J.2
Pre-training with Meta Learning for Chinese Word Segmentation
Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS).
Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework
The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid.
Visually Imperceptible Adversarial Patch Attacks on Digital Images
With this soft mask, we develop a new loss function with inverse temperature to search for optimal perturbations in CFR.
Modeling Homophone Noise for Robust Neural Machine Translation
In this paper, we propose a robust neural machine translation (NMT) framework.
Horizon curvature and spacetime structure influences on black hole scalarization
Comparing with negative and zero cases, the scalar field near AdS black hole with positive curvature could be much easier to emerge.
General Relativity and Quantum Cosmology
Phase transitions in four-dimensional AdS black holes with a nonlinear electrodynamics source
In this work we consider black hole solutions to Einstein theory coupled to a nonlinear power-law electromagnetic field with a fixed exponent value.
General Relativity and Quantum Cosmology High Energy Physics - Theory
Black holes of $(2+1)$-dimensional $f(R)$ gravity coupled to a scalar field
We consider $f(R)$ gravity theories in the presence of a scalar field minimally coupled to gravity with a self-interacting potential in $(2+1)$-dimensions.
General Relativity and Quantum Cosmology
Towards Speeding up Adversarial Training in Latent Spaces
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples.
Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning
In this paper, we propose a novel semi-supervised learning (SSL) framework for Mandarin Chinese polyphone disambiguation that can potentially leverage unlimited unlabeled text data.
Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning
However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy.
The collider tests of a leptophilic scalar for the anomalous magnetic moments
Our results show that the leptophilic scalar in the mass range of $\mathcal{O}(10)- \mathcal{O}(1000 )\,\rm GeV$ can be fully probed by the future experimental searches at the HL-LHC and the lepton colliders at their early stages.
High Energy Physics - Phenomenology
Progressive Depth Learning for Single Image Dehazing
However, we note that the guidance of the depth information for transmission estimation could remedy the decreased visibility as distances increase.
Studies of form factors and branching ratios for $D\rightarrow S,Al\bar{ν_{l}}$ with light-cone sum rules
We systematically study the semileptonic decay process of $ D\rightarrow S, A l\bar{\nu_{l}}(l=e,\mu)$ by light-cone sum rules (LCSR) with chiral currents, calculate the form factors containing only the contribution of the leading twist light-cone distribution amplitudes (LCDAs).
High Energy Physics - Phenomenology
Forecasts on Interacting Dark Energy from 21-cm Angular Power Spectrum with BINGO and SKA observations
Then we estimate the noise level of three upcoming HI intensity mapping surveys, BINGO, SKA1-MID Band$\,$1 and Band$\,$2, respectively, and employ a Fisher matrix approach to forecast their constraints on the IDE model.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology
BLOCKEYE: Hunting For DeFi Attacks on Blockchain
We applied BLOCKEYE in several popular DeFi projects and managed to discover potential security attacks that are unreported before.
Cryptography and Security Computers and Society
Progressive residual learning for single image dehazing
The recent physical model-free dehazing methods have achieved state-of-the-art performances.
Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN
GAN-based neural vocoders, such as Parallel WaveGAN and MelGAN have attracted great interest due to their lightweight and parallel structures, enabling them to generate high fidelity waveform in a real-time manner.
Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations
The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos.
Recent Advances on Sub-Nyquist Sampling-Based Wideband Spectrum Sensing
Cognitive radio (CR) is a promising technology enabling efficient utilization of the spectrum resource for future wireless systems.
Integrated Communication and Navigation for Ultra-Dense LEO Satellite Networks: Vision, Challenges and Solutions
Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner.
An Efficient Network Solver for Dynamic Simulation of Power Systems Based on Hierarchical Inverse Computation and Modification
In power system dynamic simulation, up to 90% of the computational time is devoted to solve the network equations, i. e., a set of linear equations.
Towards an Online Empathetic Chatbot with Emotion Causes
Existing emotion-aware conversational models usually focus on controlling the response contents to align with a specific emotion class, whereas empathy is the ability to understand and concern the feelings and experience of others.
Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning
However, such a decoder only exploits the preorder traversal based preceding actions, which are insufficient to ensure correct action predictions.
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios.
Lijunyi at SemEval-2020 Task 4: An ALBERT Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation
This article describes the system submitted to SemEval 2020 Task 4: Commonsense Validation and Explanation.
Lee at SemEval-2020 Task 5: ALBERT Model Based on the Maximum Ensemble Strategy and Different Data Sampling Methods for Detecting Counterfactual Statements
Our methods achieved a F1 score of 0. 85 in subtask A.
Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training
In current studies, we observe that class imbalance in SSOD severely impedes the effectiveness of self-training.
Multi-Scale Local-Temporal Similarity Fusion for Continuous Sign Language Recognition
Continuous sign language recognition (cSLR) is a public significant task that transcribes a sign language video into an ordered gloss sequence.
Risk Concentration and the Mean-Expected Shortfall Criterion
Recently, Wang and Zitikis (2021) put forward four economic axioms for portfolio risk assessment and provide the first economic axiomatic foundation for the family of ES.
Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments
The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention.
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification
The goal of few-shot fine-grained image classification is to recognize rarely seen fine-grained objects in the query set, given only a few samples of this class in the support set.
Densely Semantic Enhancement for Domain Adaptive Region-free Detectors
Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data.
Porous Lattice Transformer Encoder for Chinese NER
Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information.
Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction
Dependency trees have been shown to be effective in capturing long-range relations between target entities.
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data
Diabetic retinopathy (DR) is one of the major blindness-causing diseases currently known.
Learning Explicit Credit Assignment for Multi-agent Joint Q-learning
In contrast, we formulate an \emph{explicit} credit assignment problem where each agent gives its suggestion about how to weight individual Q-values to explicitly maximize the joint Q-value, besides guaranteeing the Bellman optimality of the joint Q-value.
Task-Specific Dependency-based Word Embedding Methods
Two task-specific dependency-based word embedding methods are proposed for text classification in this work.
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation
PEDENet contains a patch embedding (PE) network, a density estimation (DE) network, and an auxiliary network called the location prediction (LP) network.
Ranked #71 on
Anomaly Detection
on MVTec AD
Maximal Clique Based Non-Autoregressive Open Information Extraction
However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps.
Model-Based Reinforcement Learning via Imagination with Derived Memory
Model-based reinforcement learning aims to improve the sample efficiency of policy learning by modeling the dynamics of the environment.
Model-based Reinforcement Learning
reinforcement-learning
+1
VECTORIZATION METHODS IN RECOMMENDER SYSTEM
The most used recommendation method is collaborative filtering, and the key part of collaborative filtering is to compute the similarity.
ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors
This paper proposes a fusion model named ScaleVLAD to gather multi-Scale representation from text, video, and audio with shared Vectors of Locally Aggregated Descriptors to improve unaligned multimodal sentiment analysis.
Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game
An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets.
SanMove: Next Location Recommendation via Self-Attention Network
To address the above challenges, we propose a novel method named SanMove, a self-attention network based model, to predict the next location via capturing the long- and short-term mobility patterns of users.
ViR:the Vision Reservoir
To address these problems, a novel method, namely, Vision Reservoir computing (ViR), is proposed here for image classification, as a parallel to ViT.
CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding
A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work.
KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling
Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs).
Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach
In this model, the upper-level aims to seek the optimal location and capacity of DGs and energy storage, while the lower-level optimizes the operation of energy storage devices.
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting
In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations.
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.
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge
For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech.
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark
Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.
Electrified Autonomous Freight Benefit analysis on Fleet, Infrastructure and Grid Leveraging Grid-Electrified Mobility (GEM) Model
This paper analyzes the potential benefit of heavy-duty vehicle (HDV) electrification and automation on fleet cost, infrastructure cost, grid, and environmental impact.
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022
Our system is built based on the Transformer model with novel techniques borrowed from our recent research work.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
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.
Confederated Learning: Federated Learning with Decentralized Edge Servers
Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner.
A Numerical Reasoning Question Answering System with Fine-grained Retriever and the Ensemble of Multiple Generators for FinQA
Here, we propose a numerical reasoning question answering system to answer numerical reasoning questions among financial text and table data sources, consisting of a retriever module, a generator module, and an ensemble module.
Energy Circuit-based Integrated Energy Management System: Theory, Implementation, and Application
Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years.
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Extensive experiments conducted on the MINIST and CIFAR-10 datasets show that our adversarial learning with second-order adversarial examples outperforms other fisrt-order methods, which can improve the model robustness against a wide range of attacks.
BIT-Xiaomi’s System for AutoSimTrans 2022
This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge.
CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations
Since translation, rotation, and scaling operations are cascaded to form a compound one, the new model is named CompoundE.
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training
Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models.
Flux Linkage Based Evaluation Method for Voltage Inertia and Voltage Recovery Capability Under Large Disturbances
The voltage support effect of the two capabilities is analyzed based on the flux linkage and an approximate simulation of the fault impact.
A new hazard event classification model via deep learning and multifractal
Then, a new multifractal analysis method termed HmF-DFA is proposed to win HaE fractal series by analyzing HaE time series.
Yes, DLGM! A novel hierarchical model for hazard classification
In this paper, we propose a novel model termed DLGM via deep learning for hazard classification.
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks
The CP Loss supervises the text reconstruction with content semantics by multi-scale text recognition features, which effectively incorporates content awareness into the framework.
RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow
High-quality traffic flow generation is the core module in building simulators for autonomous driving.
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging.
Model-based Reinforcement Learning
reinforcement-learning
+1
Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations
The evolved local explanations on four images, randomly selected from ImageNet, are presented, which show that the evolved local explanations are straightforward to be recognised by humans.
Towards Generalized Open Information Extraction
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts.
Optimal Allocation of Virtual Inertia and Droop Control for Renewable Energy in Stochastic Look-Ahead Power Dispatch
To stabilize the frequency of the renewable energy sources (RESs) dominated power system, frequency supports are required by RESs through virtual inertia emulation or droop control in the newly published grid codes.
An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks
In this paper, inspired by transfer learning, a new evolutionary computation based framework is proposed to efficiently evolve CNNs without compromising the classification accuracy.
AE-Net:Adjoint Enhancement Network for Efficient Action Recognition in Video Understanding
Action recognition in video understanding is a challenging task, largely because of the complexity and difficulty in temporal modeling, making it suffer from motion information loss and misalignment of temporal attention in spatial dimensions.
Ranked #28 on
Action Recognition
on Something-Something V1
Analysis of November 21, 2021, Kaua`i Island Power System 18-20 Hz Oscillations
This letter discusses the 18-20 Hz oscillation event at 05:30 am on November 21, 2021, in Kaua`i's power system following the trip of an oil power plant.
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation
Accordingly, we propose a novel paradigm, i. e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy.
An Overview on Language Models: Recent Developments and Outlook
Language modeling studies the probability distributions over strings of texts.
A Spatio-temporal Decomposition Method for the Coordinated Economic Dispatch of Integrated Transmission and Distribution Grids
In the spatial dimension, a multi-parametric programming projection based spatial decomposition algorithm is developed to coordinate the ED problems of TG and DNs in a distributed manner.
A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples
Result: The result of the 3DVNET model with the augmentation method for pulmonary nodule detection shows that the proposed data augmentation method outperforms the method based on generative adversarial network (GAN) framework, training accuracy improved by 1. 5%, and with embedding mechanism for pulmonary nodules classification shows that the embedding mechanism improves the accuracy and robustness for the classification of pulmonary nodules obviously, the model training accuracy is close to 1 and the model testing F1-score is 0. 90. Conclusion:he proposed data augmentation method and embedding mechanism are beneficial to improve the accuracy and robustness of the model, and can be further applied in other common diagnostic imaging tasks.
Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm
The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading.
V3Det: Vast Vocabulary Visual Detection Dataset
2) Hierarchical Category Organization: The vast vocabulary of V3Det is organized by a hierarchical category tree which annotates the inclusion relationship among categories, encouraging the exploration of category relationships in vast and open vocabulary object detection.
Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8
Real-time implementation of such systems is crucial for traffic surveillance and enforcement, however, most of these systems are not real-time.
Pay More Attention to Relation Exploration for Knowledge Base Question Answering
In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision.
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection
The exploration problem is one of the main challenges in deep reinforcement learning (RL).
Ranked #1 on
Atari Games
on Atari-57
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation
To achieve this, we treat the black-box model as a feature extractor and train a classifier with the augmented text data.
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL).
When Hyperspectral Image Classification Meets Diffusion Models: An Unsupervised Feature Learning Framework
Learning effective spectral-spatial features is important for the hyperspectral image (HSI) classification task, but the majority of existing HSI classification methods still suffer from modeling complex spectral-spatial relations and characterizing low-level details and high-level semantics comprehensively.
Universal Information Extraction with Meta-Pretrained Self-Retrieval
Inspired by the fact that large amount of knowledge are stored in the pretrained language models~(PLM) and can be retrieved explicitly, in this paper, we propose MetaRetriever to retrieve task-specific knowledge from PLMs to enhance universal IE.
UniMC: A Unified Framework for Long-Term Memory Conversation via Relevance Representation Learning
In this paper, we propose a Unified framework for Long-term Memory Conversations (UniMC), which increases the connection between different stages by learning relevance representation.
Short-Term Voltage Security Constrained UC to Prevent Trip Faults in High Wind Power Penetrated Power Systems
Due to the time delay in WTs' controllers, it is difficult for WTs alone to meet the reactive power demands in different stages of the transient process.
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
To resolve these challenges, we cast the chip placement as an offline RL formulation and present ChiPFormer that enables learning a transferable placement policy from fixed offline data.
Low-Confidence Samples Mining for Semi-supervised Object Detection
Reliable pseudo-labels from unlabeled data play a key role in semi-supervised object detection (SSOD).
CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?
We present the Chinese Elementary School Math Word Problems (CMATH) dataset, comprising 1. 7k elementary school-level math word problems with detailed annotations, source from actual Chinese workbooks and exams.
Impact of UAVs Equipped with ADS-B on the Civil Aviation Monitoring System
However, due to the limited frequency of ADS-B technique, UAVs equipped with ADS-B devices result in the loss of packets to both UAVs and civil aviation.
Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method
In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras.
Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling
In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge.
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations
Previously, KG embedding (KGE) methods tried to solve the KGET task by introducing an auxiliary relation, 'hasType', to model the relationship between entities and their types.
What Makes Good Open-Vocabulary Detector: A Disassembling Perspective
A two-stage object detector includes a visual backbone, a region proposal network (RPN), and a region of interest (RoI) head.
Robust Backdoor Attacks on Object Detection in Real World
However, these models are vulnerable to backdoor attacks.
Domain Generalization with Fourier Transform and Soft Thresholding
However, it neglects background interference in the amplitude spectrum.
Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning
Recommender systems usually leverage multi-task learning methods to simultaneously optimize several objectives because of the multi-faceted user behavior data.
Knowledge Graph Embedding: An Overview
We will also discuss an emerging approach for KG completion which leverages pre-trained language models (PLMs) and textual descriptions of entities and relations and offer insights into the integration of KGE embedding methods with PLMs for KG completion.
Fan-Beam Binarization Difference Projection (FB-BDP): A Novel Local Object Descriptor for Fine-Grained Leaf Image Retrieval
Fine-grained leaf image retrieval (FGLIR) aims to search similar leaf images in subspecies level which involves very high interclass visual similarity and accordingly poses great challenges to leaf image description.
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions
However, most existing ensemble attacks depend on numerous substitute models to cover the vulnerable subspace of a target model.
Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations.
EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process.
F$^2$AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns
We propose a Feature-Focusing Adversarial Training (F$^2$AT), which differs from previous work in that it enforces the model to focus on the core features from natural patterns and reduce the impact of spurious features from perturbed patterns.
Blind Image Super-resolution with Rich Texture-Aware Codebooks
However, we find that a codebook based on HR reconstruction may not effectively capture the complex correlations between low-resolution (LR) and HR images.
CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation with Weighted Prefix-to-Prefix Training
Simultaneous machine translation (SiMT) is a challenging task that requires starting translation before the full source sentence is available.
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought
In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities.
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Naturally, such a paradigm raises three core questions that are not fully answered by prior non-iterative offline RL counterparts like reward-conditioned policy: (q1) What information should we transfer from the inner-level to the outer-level?
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
New categories may be introduced over time, or existing categories may need to be reclassified.
Impact of Flexible and Bidirectional Charging in Medium- and Heavy-Duty Trucks on California's Decarbonization Pathway
In particular, we examine the potential cost savings of electrical generation infrastructure by enabling flexible charging and bidirectional charging for these trucks.
In-Context Learning for Few-Shot Nested Named Entity Recognition
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address.
Active Support of Inverters for Improving Short-Term Voltage Security in 100% IBRsPenetrated Power Systems
Due to the energy crisis and environmental pollution, the installed capacity of inverter-based resources (IBRs) in power grids is rapidly increasing, and grid-following control (GFL) is the most prevalent at present.
A Comprehensive Evaluation of Quantization Strategies for Large Language Models
Our experimental results indicate that LLMs with 4-bit quantization can retain performance comparable to their non-quantized counterparts, and perplexity can serve as a proxy metric for quantized LLMs on most benchmarks.
DRSI-Net: Dual-Residual Spatial Interaction Network for Multi-Person Pose Estimation
Multi-person pose estimation (MPPE), which aims to locate keypoints for all persons in the frames, is an active research branch of computer vision.
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach
Due to the potentially massive number of users involved, it is crucial to reduce the communication overhead of the CFL system.
ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval
Specifically, our proposed ToolRerank includes Adaptive Truncation, which truncates the retrieval results related to seen and unseen tools at different positions, and Hierarchy-Aware Reranking, which makes retrieval results more concentrated for single-tool queries and more diverse for multi-tool queries.
Resilience of Large Language Models for Noisy Instructions
As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating text across various tasks.
Automatic Speech Recognition
Optical Character Recognition
+3
PhyRecon: Physically Plausible Neural Scene Reconstruction
In this paper, we introduce PhyRecon, which stands as the first approach to harness both differentiable rendering and differentiable physics simulation to learn implicit surface representations.
An Off-Policy Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems
Recently, to optimize long-term user satisfaction within a recommendation session, Reinforcement Learning (RL) is used for MTF in the industry.
Deep Structural Point Process for Learning Temporal Interaction Networks
A temporal interaction network consists of a series of chronological interactions between users and items.
Geometry Contrastive Learning on Heterogeneous Graphs
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.
Robust Network Architecture Search via Feature Distortion Restraining
This process is modeled as a multi-objective bilevel optimization problem and a novel algorithm is proposed to solve this optimization.
Transformer based Generative Adversarial Network for Liver Segmentation
The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.
Dilation-Erosion for Single-Frame Supervised Temporal Action Localization
To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization.
Domain Generalization with Correlated Style Uncertainty
In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains.
Knowledge Graph Embedding with 3D Compound Geometric Transformations
The cascade of 2D geometric transformations were exploited to model relations between entities in a knowledge graph (KG), leading to an effective KG embedding (KGE) model, CompoundE.
Ranked #7 on
Link Property Prediction
on ogbl-wikikg2
TEAM: We Need More Powerful Adversarial Examples for DNNs
In this paper, we propose a novel method (TEAM, Taylor Expansion-Based Adversarial Methods) to generate more powerful adversarial examples than previous methods.
Focus-Constrained Attention Mechanism for CVAE-based Response Generation
To model diverse responses for a given post, one promising way is to introduce a latent variable into Seq2Seq models.
Exploring the Interactive Guidance for Unified and Effective Image Matting
Although avoiding the extensive labors of trimap annotation, existing methods still suffer from two limitations: (1) For the single image with multiple objects, it is essential to provide extra interaction information to help determining the matting target; (2) For transparent objects, the accurate regression of alpha matte from RGB image is much more difficult compared with the opaque ones.
A Focused Study on Sequence Length for Dialogue Summarization
Output length is critical to dialogue summarization systems.
Point normal orientation and surface reconstruction by incorporating isovalue constraints to Poisson equation
In this work, we bridge orientation and reconstruction in the implicit space and propose a novel approach to orient point cloud normals by incorporating isovalue constraints to the Poisson equation.
Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension
Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.
Learning Modified Indicator Functions for Surface Reconstruction
We design a novel deep neural network to perform surface integral and learn the modified indicator functions from un-oriented and noisy point clouds.
Structure-Unified M-Tree Coding Solver for MathWord Problem
As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years.
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions
In this paper, we propose a framework, MoralDial to train and evaluate moral dialogue systems.
Improved training of neural trans-dimensional random field language models with dynamic noise-contrastive estimation
First, a dynamic noise distribution is introduced and trained simultaneously to converge to the data distribution.
Graph Representation Learning: A Survey
Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs.
Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism
Concretely, we insert a left-to-right mask to the same decoder of CMTM, and then induce it to autoregressively review whether each generated word from CMTM is supposed to be replaced or kept.
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
