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Found 302 papers, 111 papers with code
Message Passing Stein Variational Gradient Descent
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography
Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells.
Fooling Vision and Language Models Despite Localization and Attention Mechanism
Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks.
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination
All existing image enhancement methods, such as HDR tone mapping, cannot recover A/D quantization losses due to insufficient or excessive lighting, (underflow and overflow problems).
Towards Synthesizing Complex Programs from Input-Output Examples
In our evaluation, we show that using our novel approach, neural parsing programs can be learned to achieve 100% test accuracy on test inputs that are 500x longer than the training samples.
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models---a common type of machine-learning model.
Attentive Tensor Product Learning
This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models.
Generating Plans that Predict Themselves
We introduce $t$-\ACty{}: a measure that quantifies the accuracy and confidence with which human observers can predict the remaining robot plan from the overall task goal and the observed initial $t$ actions in the plan.
Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms
Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution.
Tree-to-tree Neural Networks for Program Translation
We observe that program translation is a modular procedure, in which a sub-tree of the source tree is translated into the corresponding target sub-tree at each step.
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications.
Pragmatic-Pedagogic Value Alignment
In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users' objectives as they go.
Demonstration of Topological Data Analysis on a Quantum Processor
Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure.
Anomaly Detection in Hierarchical Data Streams under Unknown Models
We consider the problem of detecting a few targets among a large number of hierarchical data streams.
MAT: A Multimodal Attentive Translator for Image Captioning
In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model.
How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior
For projects that cost millions of dollars, it is critical to determine the right amount of data needed.
Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approach
We collect a large set of data using The Open Racing Car Simulator (TORCS) and classify the image features into three categories (sky-related, roadside-related, and road-related features). We then design two experimental frameworks to investigate the importance of each single feature for training a CNN controller. The first framework uses the training data with all three features included to train a controller, which is then tested with data that has one feature removed to evaluate the feature's effects.
Automated Inference on Sociopsychological Impressions of Attractive Female Faces
This article is a sequel to our earlier work [25].
Latent Attention For If-Then Program Synthesis
Automatic translation from natural language descriptions into programs is a longstanding challenging problem.
Robust High-Dimensional Linear Regression
The effectiveness of supervised learning techniques has made them ubiquitous in research and practice.
Feature Selection Based on Confidence Machine
We propose a filter method for unsupervised feature selection which is based on the Confidence Machine.
Latent Feature Based FM Model For Rating Prediction
Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM).
Linear Span Network for Object Skeleton Detection
Hinted by this, we formalize a Linear Span framework, and propose Linear Span Network (LSN) modified by Linear Span Units (LSUs), which minimize the reconstruction error of convolutional network.
Boosting Model Performance through Differentially Private Model Aggregation
In this work, two approaches for private model aggregation are proposed that enable the transfer of knowledge from existing models trained on other companies' datasets to a new company with limited labeled data while protecting each client company's underlying individual sensitive information.
A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network
To address this issue, here we propose a novel deep residual learning model that combines the dilated residual convolution and multi-scale convolution groups.
Stochastic Gradient Geodesic MCMC Methods
We propose two stochastic gradient MCMC methods for sampling from Bayesian posterior distributions defined on Riemann manifolds with a known geodesic flow, e. g. hyperspheres.
Execution-Guided Neural Program Synthesis
Most existing neural program synthesis approaches employ an encoder-decoder architecture, which uses an encoder to compute the embedding of the given input-output examples, as well as a decoder to generate the program from the embedding following a given syntax.
A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network
Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays.
Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic MRI Using Mask-RCNN
Prostate cancer (PCa) is the most common cancer in men in the United States.
Orthogonal Decomposition Network for Pixel-Wise Binary Classification
The weight sharing scheme and spatial pooling operations in Convolutional Neural Networks (CNNs) introduce semantic correlation to neighboring pixels on feature maps and therefore deteriorate their pixel-wise classification performance.
Kingsoft's Neural Machine Translation System for WMT19
This paper describes the Kingsoft AI Lab{'}s submission to the WMT2019 news translation shared task.
Automated Fashion Size Normalization
The ability to accurately predict the fit of fashion items and recommend the correct size is key to reducing merchandise returns in e-commerce.
Generating Persuasive Visual Storylines for Promotional Videos
Video contents have become a critical tool for promoting products in E-commerce.
Distributed representation of patients and its use for medical cost prediction
Efficient representation of patients is very important in the healthcare domain and can help with many tasks such as medical risk prediction.
ALCNN: Attention-based Model for Fine-grained Demand Inference of Dock-less Shared Bike in New Cities
In this paper, we study the problem of inferring fine-grained bike demands anywhere in a new city before the deployment of bikes.
Straight-Through Estimator as Projected Wasserstein Gradient Flow
The Straight-Through (ST) estimator is a widely used technique for back-propagating gradients through discrete random variables.
RGB-D Individual Segmentation
Fine-grained recognition task deals with sub-category classification problem, which is important for real-world applications.
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.
Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints
As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject.
Cryptography and Security Hardware Architecture
Recreation of the Periodic Table with an Unsupervised Machine Learning Algorithm
In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev.
DWM: A Decomposable Winograd Method for Convolution Acceleration
In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed.
Variational Policy Propagation for Multi-agent Reinforcement Learning
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
It is a challenging task to have real-time, efficient, and accurate hardware RNN implementations because of the high sensitivity to imprecision accumulation and the requirement of special activation function implementations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
HyperSTAR: Task-Aware Hyperparameters for Deep Networks
To reduce HPO time, we present HyperSTAR (System for Task Aware Hyperparameter Recommendation), a task-aware method to warm-start HPO for deep neural networks.
Towards Fine-grained Human Pose Transfer with Detail Replenishing Network
Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality.
Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network
By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS.
Solar and Stellar Astrophysics
LRNNet: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation
Many of these applications need to perform a real-time and efficient prediction for semantic segmentation with a light-weighted network.
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.
Modeling Lost Information in Lossy Image Compression
Specifically, ILC introduces an invertible encoding module to replace the encoder-decoder structure to produce the low dimensional informative latent representation, meanwhile, transform the lost information into an auxiliary latent variable that won't be further coded or stored.
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks.
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Particle-based Variational Inference methods (ParVIs), like Stein Variational Gradient Descent, are nonparametric variational inference methods that optimize a set of particles to best approximate a target distribution.
Attribute-conditioned Layout GAN for Automatic Graphic Design
In this paper, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions.
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
The heavy traffic congestion problem has always been a concern for modern cities.
Neighbor Class Consistency on Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data in a target domain with labeled data from source domain available.
Latent Causal Invariant Model
To avoid spurious correlation, we propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
Computational Design and Fabrication of Corrugated Mechanisms from Behavioral Specifications
Based on an equivalent plate model, we develop and validate analytical formulas for the behavioral specifications of OADLC mechanisms; the analytical formulas can be described as expressions of design parameters.
Robotics
Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering
With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which hinders the development of video text-related applications.
Strain-Rate-Dependent Deformation Behavior of Ti29Zr24Nb23Hf24 High Entropy Alloys at Elevated and Room Temperature
Such a softening behavior can be related to the interaction of dislocations with short-range clustering.
Materials Science
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols.
Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications.
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications
Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI).
Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization
For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction.
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference.
Non-equilibrium Flux Rope Formation by Confined Flares Preceding a Solar Coronal Mass Ejection
We present evidence that a magnetic flux rope was formed before a coronal mass ejection (CME) and its associated long-duration flare during a pair of preceding confined eruptions and associated impulsive flares in a compound event in NOAA Active Region 12371.
Solar and Stellar Astrophysics
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details
With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.
Towards Enhancing Fine-grained Details for Image Matting
In this paper, we argue that recovering these microscopic details relies on low-level but high-definition texture features.
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
It is still an open question to leverage various types of information under the BERT framework.
Learning Invariant Representations across Domains and Tasks
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledge from the abundant typical pneumonia datasets for COVID-19 image classification.
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management
In this work, we invert the "what-if" scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future.
Ternary Hashing
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).
Learning to Simulate on Sparse Trajectory Data
Simulation of the real-world traffic can be used to help validate the transportation policies.
AET-EFN: A Versatile Design for Static and Dynamic Event-Based Vision
The neuromorphic event cameras, which capture the optical changes of a scene, have drawn increasing attention due to their high speed and low power consumption.
CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography
Chemical Species Tomography (CST) has been widely used for in situ imaging of critical parameters, e. g. species concentration and temperature, in reactive flows.
Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning
By tentatively expanding network nodes, LEC-Net enlarges the representation capacity of features, alleviating feature drift of old network from the perspective of model regularization.
Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework
The latest theoretical advances in the field of unlimited sampling framework (USF) show the potential to avoid clipping problems of analog-to-digital converters (ADC).
Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography
Although a fully parallel data acquisition (DAQ) and signal processing system can achieve these functionalities with maximised temporal response, it leads to a highly complex, expensive and power-consuming instrumentation system with high potential for inconsistency between the sampled beams due to the electronics alone.
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.
Underwater Target Recognition based on Multi-Decision LOFAR Spectrum Enhancement: A Deep Learning Approach
To overcome this issue and to further improve the recognition performance, we adopt a deep learning approach for underwater target recognition and propose a LOFAR spectrum enhancement (LSE)-based underwater target recognition scheme, which consists of preprocessing, offline training, and online testing.
Learning to Route via Theory-Guided Residual Network
Therefore, in this paper, we propose to learn the human routing model, which is one of the most essential part in the traffic simulator.
Target-Dependent Chemical Species Tomography with Hybrid Meshing of Sensing Regions
The proposed scheme was both numerically and experimentally validated using a CST sensor with 32 laser beams using a variety of computational tomographic algorithms.
Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography
As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows.
BLCU-NLP at SemEval-2020 Task 5: Data Augmentation for Efficient Counterfactual Detecting
We demonstrate the effectiveness of our approaches, which achieves 0. 95 of subtask 1 in F1 while using only a subset of giving training set to fine-tune the BERT model, and our official submission achieves F1 0. 802, which ranks us 16th in the competition.
Light Pollution Reduction in Nighttime Photography
Nighttime photographers are often troubled by light pollution of unwanted artificial lights.
Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks
In addition, our framework showed a great generalizability potential to transfer learned knowledge from one institution to another, paving the way for future healthcare model pre-training across institutions.
Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis
The claims data, containing medical codes, services information, and incurred expenditure, can be a good resource for estimating an individual's health condition and medical risk level.
A mechanistic-based data-driven approach to accelerate structural topology optimization through finite element convolutional neural network (FE-CNN)
During offline training, a mapping function is built between high and low resolution representations of a given design domain.
Tracing Halpha Fibrils through Bayesian Deep Learning
Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations.
Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering
Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks.
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE
Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm.
Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance.
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator
As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities.
You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors
Fortunately, most existing adversarial patches can be outwitted, disabled and rejected by a simple classification network called an adversarial patch detector, which distinguishes adversarial patches from original images.
Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning
For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.
Learning to Learn across Diverse Data Biases in Deep Face Recognition
Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data.
Particle Based Stochastic Policy Optimization
Stochastic polic have been widely applied for their good property in exploration and uncertainty quantification.
Ranked #1 on
MuJoCo Games
on Ant-v3
Motivating Effort with Information about Future Rewards
The principal knows the reward of the task and provides information to the agent over time in order to motivate effort.
Spatial-temporal water area monitoring of Miyun Reservoir using remote sensing imagery from 1984 to 2020
Based on the mapping results, we analyzed the changes of Miyun Reservoir from 1984 to 2020 and the driving factors of them.
Improving Location Recommendation with Urban Knowledge Graph
We first conduct information propagation on two sub-graphs to learn the representations of POIs and users.
Decentralized On-Ramp Merging Control of Connected and Automated Vehicles in the Mixed Traffic Using Control Barrier Functions
We first formulate the optimal merging control problem, which includes the constraints of safety and vehicle dynamics, with the objectives of minimizing travel time and energy consumption.
基于跨语言双语预训练及Bi-LSTM的汉-越平行句对抽取方法(Chinese-Vietnamese Parallel Sentence Pair Extraction Method Based on Cross-lingual Bilingual Pre-training and Bi-LSTM)
汉越平行句对抽取是缓解汉越平行语料库数据稀缺的重要方法。平行句对抽取可转换为同一语义空间下的句子相似性分类任务, 其核心在于双语语义空间对齐。传统语义空间对齐方法依赖于大规模的双语平行语料, 越南语作为低资源语言获取大规模平行语料相对困难。针对这个问题本文提出一种利用种子词典进行跨语言双语预训练及Bi-LSTM(Bi-directional Long Short-Term Memory)的汉-越平行句对抽取方法。预训练中仅需要大量的汉越单语和一个汉越种子词典, 通过利用汉越种子词典将汉越双语映射到公共语义空间进行词对齐。再利用Bi-LSTM和CNN(Convolutional Neural Networks)分别提取句子的全局特征和局部特征从而最大化表示汉-越句对之间的语义相关性。实验结果表明, 本文模型在F1得分上提升7. 1%, 优于基线模型。
面向人工智能伦理计算的中文道德词典构建方法研究(Construction of a Chinese Moral Dictionary for Artificial Intelligence Ethical Computing)
道德词典资源的建设是人工智能伦理计算的一个研究重点。由于道德行为复杂多样, 现有的英文道德词典分类体系并不完善, 而中文方面目前尚未有相关的词典资源, 理论体系和构建方法仍待探究。针对以上问题, 该文提出了面向人工智能伦理计算的中文道德词典构建任务, 设计了四类标签和四种类型, 得到包含25, 012个词的中文道德词典资源。实验结果表明, 该词典资源不仅能够使机器学会道德知识, 判断词的道德标签和类型, 而且能够为句子级别的道德文本分析提供数据支持。
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification
To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance.
Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images
These methods mainly focus on capturing either compact or descriptive features, where the information of the samples of a given one class is not sufficiently utilized.
DMF-Net: Dual-Branch Multi-Scale Feature Fusion Network for copy forgery identification of anti-counterfeiting QR code
We first analyze the production principle of anti-counterfeiting QR code, and convert the identification of copy forgery to device category forensics, and then a Dual-Branch Multi-Scale Feature Fusion network is proposed.
Time-Frequency Mask Aware Bi-directional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation
In particular, this method breaks through the limitations of the existing methods, not only achieves good results in multivariate separation, but also effectively separates signals when mixed with 40dB Gaussian noise signals.
Orientation-Discriminative Feature Representation for Decentralized Pedestrian Tracking
This paper focuses on the problem of decentralized pedestrian tracking using a sensor network.
A Survey for Solving Mixed Integer Programming via Machine Learning
This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems.
Towards Open-Set Text Recognition via Label-to-Prototype Learning
Scene text recognition is a popular topic and extensively used in the industry.
An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.
MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator
While a few previous studies tried to automatically generate 3D bounding boxes from weak labels such as 2D boxes, the quality is sub-optimal compared to human annotators.
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
The design variables consist of a set of reactants in a reaction network and its network topology.
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.
Instance-Specific Feature Propagation for Referring Segmentation
In this work, we propose a novel framework that simultaneously detects the target-of-interest via feature propagation and generates a fine-grained segmentation mask.
TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection
In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum.
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation
To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations
This paper proposes a multilingual speech synthesis method which combines unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR) to avoid reliance on the pronunciation dictionaries of target languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Tensor Recovery Based on A Novel Non-convex Function Minimax Logarithmic Concave Penalty Function
The proposed function is generalized to tensor cases, yielding tensor MLCP and weighted tensor $L\gamma$-norm.
An Extendable Maneuver Management Framework with Fault-Tolerant Mechanism for Vehicle Platoon Control System in Highway Scenario
To deal with this problem, this paper introduces a two-dimensional maneuver management framework with a fault-tolerant mechanism on the basis of the proposed hierarchical architecture for the platoon control system.
$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features
In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features.
Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-enabled Vehicular Networks
Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel prediction (CP), i. e., predicting the angular parameters of users.
Robust Bayesian Inference for Moving Horizon Estimation
The accuracy of moving horizon estimation (MHE) suffers significantly in the presence of measurement outliers.
Time Minimization in Hierarchical Federated Learning
Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts.
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations
In this paper, we propose a reciprocal learning approach to jointly optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels.
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e. g., unsupervised semantic segmentation (USS).
NoMorelization: Building Normalizer-Free Models from a Sample's Perspective
The normalizing layer has become one of the basic configurations of deep learning models, but it still suffers from computational inefficiency, interpretability difficulties, and low generality.
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations.
Matching entropy based disparity estimation from light field
A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions.
Completely Heterogeneous Federated Learning
Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i. i. d.
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning
In this paper, we present relation-aware contrastive self-supervised learning (ReCo) to integrate instance relations, i. e., global distribution relation and local interpolation relation, into the CSL framework in a plug-and-play fashion.
Local Manifold Augmentation for Multiview Semantic Consistency
Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation.
Local Magnification for Data and Feature Augmentation
And further combination with our feature augmentation techniques, termed LOMA_IF&FO, can continue to strengthen the model and outperform advanced intensity transformation methods for data augmentation.
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality
The Metaverse has received much attention recently.
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test
While most existing message-passing graph neural networks (MPNNs) are permutation-invariant in graph-level representation learning and permutation-equivariant in node- and edge-level representation learning, their expressive power is commonly limited by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test.
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted.
Optical Character Recognition (OCR)
Weakly supervised Semantic Segmentation
+1
Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach
With the proposed predictive approach, we can avoid full-scale CSI estimation and facilitate low-dimensional CE for transmit beamforming design such that the signaling overhead is reduced by a scale of $\frac{1}{N}$, where $N$ is the number of IRS elements.
Robust Contracts with Exploration
We study a two-period moral hazard problem; there are two agents, with action sets that are unknown to the principal.
DQnet: Cross-Model Detail Querying for Camouflaged Object Detection
Optimizing a convolutional neural network (CNN) for camouflaged object detection (COD) tends to activate local discriminative regions while ignoring complete object extent, causing the partial activation issue which inevitably leads to missing or redundant regions of objects.
Adam: Dense Retrieval Distillation with Adaptive Dark Examples
Different from previous works that only rely on one positive and hard negatives as candidate passages, we create dark examples that all have moderate relevance to the query through mixing-up and masking in discrete space.
THMA: Tencent HD Map AI System for Creating HD Map Annotations
THMA has been deployed by the Tencent Map team to provide services to downstream companies and users, serving over 1, 000 labeling workers and producing more than 30, 000 kilometers of HD map data per day at most.
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC).
Ultra-low Precision Multiplication-free Training for Deep Neural Networks
The energy-efficient works try to decrease the precision of multiplication or replace the multiplication with energy-efficient operations such as addition or bitwise shift, to reduce the energy consumption of FP32 multiplications.
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain
To address this issue, we proposed a novel method of Augmenting data with Adversarial examples via a Wavelet module (AdvWavAug), an on-manifold adversarial data augmentation technique that is simple to implement.
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e. g., CNNs, Transformers) and learning algorithms (e. g., normal supervised training, pre-training, adversarial training) under numerous adversarial attacks and out-of-distribution (OOD) datasets.
Particle-based Online Bayesian Sampling
Online optimization has gained increasing interest due to its capability of tracking real-world streaming data.
Parallel Vertex Diffusion for Unified Visual Grounding
An intuitive materialization of our paradigm is Parallel Vertex Diffusion (PVD) to directly set vertex coordinates as the generation target and use a diffusion model to train and infer.
Stock Price Prediction Using Temporal Graph Model with Value Chain Data
Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks.
Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation
Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis.
Context-Aware Transformer for 3D Point Cloud Automatic Annotation
3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious.
Hail Mary Pass: Contests with Stochastic Progress
This paper studies the equilibrium behavior in contests with stochastic progress.
TG-VQA: Ternary Game of Video Question Answering
Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them.
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs.
Multi-Modal Mutual Attention and Iterative Interaction for Referring Image Segmentation
However, the generic attention mechanism in Transformer only uses the language input for attention weight calculation, which does not explicitly fuse language features in its output.
More than Classification: A Unified Framework for Event Temporal Relation Extraction
After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events.
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation
Bottom-up methods are mainly perturbed by Inferior Positive (IP) errors due to the lack of prior object information.
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts
In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts.
UNIDEAL: Curriculum Knowledge Distillation Federated Learning
Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy.
Retinex-guided Channel-grouping based Patch Swap for Arbitrary Style Transfer
The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps.
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
TopoSeg: Topology-Aware Nuclear Instance Segmentation
Experiments on three nuclear instance segmentation datasets justify the superiority of TopoSeg, which achieves state-of-the-art performance.
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research.
Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation
This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments.
YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object Detection
Vehicle perception systems strive to achieve comprehensive and rapid visual interpretation of their surroundings for improved safety and navigation.
Transformer-empowered Multi-modal Item Embedding for Enhanced Image Search in E-Commerce
Since its deployment in March 2023, it has achieved a remarkable 9. 90% increase in terms of clicks per user and a 4. 23% boost in terms of orders per user for the image search feature on the Shopee e-commerce platform.
LLMs Accelerate Annotation for Medical Information Extraction
The results highlight the potential of using LLMs to improve the utilization of unstructured clinical data, allowing for the swift deployment of tailored NLP solutions in healthcare.
Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke
This work focuses on the development of an optimal wildfire escape route planning system specifically designed for drones, considering dynamic fire and smoke models.
Active Wildfires Detection and Dynamic Escape Routes Planning for Humans through Information Fusion between Drones and Satellites
Taking the Chongqing wildfire on August 24, 2022, as a case study, the results demonstrate that the dynamic escape route planning algorithm can provide an optimal real-time navigation path for humans in the presence of fire through the information fusion of UAVs and satellites.
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design
However, there are few specialized models in certain domains, such as interior design, which is attributed to the complex textual descriptions and detailed visual elements inherent in design, alongside the necessity for adaptable resolution.
Offloading and Quality Control for AI Generated Content Services in 6G Mobile Edge Computing Networks
In pursuit of enhancing the accessibility of AIGC services, the deployment of AIGC models (e. g., diffusion models) to edge servers and local devices has become a prevailing trend.
Future-proofing geotechnics workflows: accelerating problem-solving with large language models
The integration of Large Language Models (LLMs) like ChatGPT into the workflows of geotechnical engineering has a high potential to transform how the discipline approaches problem-solving and decision-making.
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation
Existing approaches to Implicit Neural Representation (INR) can be interpreted as a global scene representation via a linear combination of Fourier bases of different frequencies.
VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model
In the realm of household robotics, the Zero-Shot Object Navigation (ZSON) task empowers agents to adeptly traverse unfamiliar environments and locate objects from novel categories without prior explicit training.
PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding
This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task.
Error bounds of constant gain least-mean-squares algorithms
Constant gain least-mean-squares (LMS) algorithms have a wide range of applications in trajectory tracking problems, but the formal convergence of LMS in mean square is not yet fully established.
Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective Risk Minimization
The average organ dice of the proposed method is 0. 71 compared with 0. 63 in baseline model, indicating the enhancement of anatomical structures.
Path Planning based on 2D Object Bounding-box
The implementation of Autonomous Driving (AD) technologies within urban environments presents significant challenges.
Artificial Intelligence for Complex Network: Potential, Methodology and Application
Complex networks pervade various real-world systems, from the natural environment to human societies.
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments
Zero-Shot Object Navigation (ZSON) requires agents to autonomously locate and approach unseen objects in unfamiliar environments and has emerged as a particularly challenging task within the domain of Embodied AI.
AnatoMix: Anatomy-aware Data Augmentation for Multi-organ Segmentation
Multi-organ segmentation in medical images is a widely researched task and can save much manual efforts of clinicians in daily routines.
Self-Consistency Training for Hamiltonian Prediction
This merit addresses the data scarcity difficulty, and distinguishes the task from other property prediction formulations with unique benefits: (1) self-consistency training enables the model to be trained on a large amount of unlabeled data, hence substantially enhances generalization; (2) self-consistency training is more efficient than labeling data with DFT for supervised training, since it is an amortization of DFT calculation over a set of molecular structures.
Ultrafast Adaptive Primary Frequency Tuning and Secondary Frequency Identification for S/S WPT system
The swift response of SCC and two-step perturb-and-observe algorithm mitigate output disturbances, thereby expediting the frequency tuning process.
Convolutional Bayesian Filtering
In this paper, we find that by adding an additional event that stipulates an inequality condition, we can transform the conditional probability into a special integration that is analogous to convolution.
Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields
In spectral CT reconstruction, the basis materials decomposition involves solving a large-scale nonlinear system of integral equations, which is highly ill-posed mathematically.
Large Language Models for Networking: Workflow, Advances and Challenges
The networking field is characterized by its high complexity and rapid iteration, requiring extensive expertise to accomplish network tasks, ranging from network design, configuration, diagnosis and security.
GraCo: Granularity-Controllable Interactive Segmentation
In this work, we introduce Granularity-Controllable Interactive Segmentation (GraCo), a novel approach that allows precise control of prediction granularity by introducing additional parameters to input.
Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.
Progressive Cluster Purification for Unsupervised Feature Learning
In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner.
DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction
Solar flare prediction plays an important role in understanding and forecasting space weather.
Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoods
Our method can be divided into three stages: 1) a neighborhood generation stage, which generates instances based on the given sample; 2) a classification stage, which yields classifications on the generated instances to carve out the local decision boundary and delineate the model behavior; and 3) a human-in-the-loop stage, which involves human to refine and explore the neighborhood of interest.
BIG-bench Machine Learning
Explainable artificial intelligence
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Scribble-Supervised Target Extraction Method Based on Inner Structure-Constraint for Remote Sensing Images
Weakly supervised learning based on scribble annotations in target extraction of remote sensing images has drawn much interest due to scribbles' flexibility in denoting winding objects and low cost of manually labeling.
Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image Analysis
Colorectal cancer (CRC), which frequently originates from initially benign polyps, remains a significant contributor to global cancer-related mortality.
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
We present two recurrent neural networks (RNNs), one based on gated recurrent units and the other based on long short-term memory, for predicting whether an active region (AR) that produces an M- or X-class flare will also produce a coronal mass ejection (CME).
Feature-Gate Coupling for Dynamic Network Pruning
Gating modules have been widely explored in dynamic network pruning to reduce the run-time computational cost of deep neural networks while preserving the representation of features.
