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Found 72 papers, 20 papers with code
Acknowledgement Entity Recognition in CORD-19 Papers
We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to 50–60% of named entities are actually acknowledged.
Data-free Distillation with Degradation-prompt Diffusion for Multi-weather Image Restoration
Multi-weather image restoration has witnessed incredible progress, while the increasing model capacity and expensive data acquisition impair its applications in memory-limited devices.
Impact of ChatGPT on the writing style of condensed matter physicists
We apply a state-of-the-art difference-in-differences approach to estimate the impact of ChatGPT's release on the writing style of condensed matter papers on arXiv.
WLC-Net: a robust and fast deep-learning wood-leaf classification method
Wood-leaf classification is an essential and fundamental prerequisite in the analysis and estimation of forest attributes from terrestrial laser scanning (TLS) point clouds, including critical measurements such as diameter at breast height(DBH), above-ground biomass(AGB), wood volume. To address this, we introduce the Wood-Leaf Classification Network(WLC-Net), a deep learning model derived from PointNet++, designed to differentiate between wood and leaf points within tree point clouds. WLC-Net enhances classification accuracy, completeness, and speed by incorporating linearity as an inherent feature, refining the input-output framework, and optimizing the centroid sampling technique. WLC-Net was trained and assessed using three distinct tree species datasets, comprising a total of 102 individual tree point clouds:21 Chinese ash trees, 21 willow trees, and 60 tropical trees. For comparative evaluation, five alternative methods, including PointNet++, DGCNN, Krishna Moorthy's method, LeWoS, and Sun's method, were also applied to these datasets. The classification accuracy of all six methods was quantified using three metrics:overall accuracy(OA), mean Intersection over Union(mIoU), and F1-score. Across all three datasets, WLC-Net demonstrated superior performance, achieving OA scores of 0. 9778, 0. 9712, and 0. 9508;mIoU scores of 0. 9761, 0. 9693, and 0. 9141;and F1-scores of 0. 8628, 0. 7938, and 0. 9019, respectively. The time costs of WLC-Net were also recorded to evaluate the efficiency. The average processing time was 102. 74s per million points for WLC-Net. In terms of visual inspect, accuracy evaluation and efficiency evaluation, the results suggest that WLC-Net presents a promising approach for wood-leaf classification, distinguished by its high accuracy.
Mixed-Query Transformer: A Unified Image Segmentation Architecture
Existing unified image segmentation models either employ a unified architecture across multiple tasks but use separate weights tailored to each dataset, or apply a single set of weights to multiple datasets but are limited to a single task.
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection
Out-of-domain (OOD) intent detection aims to examine whether the user's query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems.
Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models
In this work, we focus on improving the confidence estimation of large language models.
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability.
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
Further, we design a novel Color and Intensity Decoupling Network (CIDNet) with two branches dedicated to processing the decoupled image brightness and color in the HVI space.
Ranked #1 on
Low-Light Image Enhancement
on VV
Low-light Image Deblurring and Enhancement
Low-Light Image Enhancement
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system.
Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT
The tasks of out-of-domain (OOD) intent discovery and generalized intent discovery (GID) aim to extend a closed intent classifier to open-world intent sets, which is crucial to task-oriented dialogue (TOD) systems.
Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition
In a practical dialogue system, users may input out-of-domain (OOD) queries.
Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity
Traditional computer vision models often necessitate extensive data acquisition, annotation, and validation.
Confidence Ranking for CTR Prediction
Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e. g. ads and recommendation systems.
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery
Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn.
Learning from Multi-Perception Features for Real-Word Image Super-resolution
Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods.
GRAN: Ghost Residual Attention Network for Single Image Super Resolution
GRAB consists of the Ghost Module and Channel and Spatial Attention Module (CSAM) to alleviate the generation of redundant features.
Take a Prior from Other Tasks for Severe Blur Removal
For general scene deblurring, the feature space of the blurry image and corresponding sharp image under the high-level vision task is closer, which inspires us to rely on other tasks (e. g. classification) to learn a comprehensive prior in severe blur removal cases.
Towards Professional Level Crowd Annotation of Expert Domain Data
A new approach, based on semi-supervised learning (SSL) and denoted as SSL with human filtering (SSL-HF) is proposed.
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning
Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNN-based scoring function for OOD detection.
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery
For OOD clustering stage, we propose a KCC method to form compact clusters by mining true hard negative samples, which bridges the gap between clustering and representation learning.
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning
In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples.
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems
Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals.
Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation
Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside the predefined supported intent set.
Generalized Intent Discovery: Learning from Open World Dialogue System
Traditional intent classification models are based on a pre-defined intent set and only recognize limited in-domain (IND) intent classes.
SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision
Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications.
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving
In this work, we propose a novel framework PCDF(Parallel-Computing Distributed Framework), allowing to split the computation cost into three parts and to deploy them in the pre-module in parallel with the retrieval stage, the middle-module for ranking ads, and the post-module for re-ranking ads with external items.
Evolution of beliefs in social networks
Evolution of beliefs of a society are a product of interactions between people (horizontal transmission) in the society over generations (vertical transmission).
IA-GCN: Interactive Graph Convolutional Network for Recommendation
In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN).
Omni-DETR: Omni-Supervised Object Detection with Transformers
This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection.
Ranked #14 on
Semi-Supervised Object Detection
on COCO 2% labeled data
Neurosymbolic hybrid approach to driver collision warning
(2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature.
Learning Distinctive Margin toward Active Domain Adaptation
Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in transferring model in a more practical way with limited annotation resource on target data.
Discrete Probabilistic Inverse Optimal Transport
Optimal transport (OT) formalizes the problem of finding an optimal coupling between probability measures given a cost matrix.
Neurosymbolic Systems of Perception & Cognition: The Role of Attention
A cognitive architecture aimed at cumulative learning must provide the necessary information and control structures to allow agents to learn incrementally and autonomously from their experience.
Dynamic Parameterized Network for CTR Prediction
Learning to capture feature relations effectively and efficiently is essential in click-through rate (CTR) prediction of modern recommendation systems.
DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy
Quantitative metrics that measure the global economy's equilibrium have strong and interdependent relationships with the agricultural supply chain and international trade flows.
Fast tree skeleton extraction using voxel thinning based on tree point cloud
In this paper, an automatic and fast tree skeleton extraction method (FTSEM) based on voxel thinning is proposed.
SDTP: Semantic-aware Decoupled Transformer Pyramid for Dense Image Prediction
CDI builds the global attention and interaction among different levels in decoupled space which also solves the problem of heavy computation.
Wood-leaf classification of tree point cloud based on intensity and geometrical information
Tree point cloud was classified into wood points and leaf points by using intensity threshold, neighborhood density and voxelization successively.
Gradient-Based Algorithms for Machine Teaching
This is formulated as a problem of functional optimization where, at each teaching iteration, the teacher seeks to align the steepest descent directions of the risk of (1) the teaching set and (2) entire example population.
Lightweight Cross-Lingual Sentence Representation Learning
Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks.
IMAGINE: Image Synthesis by Image-Guided Model Inversion
We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample.
Rethinking and Improving the Robustness of Image Style Transfer
Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image.
Dynamic Transfer for Multi-Source Domain Adaptation
However, such a static model is difficult to handle conflicts across multiple domains, and suffers from a performance degradation in both source domains and target domain.
The first evidence for three-dimensional spin-velocity alignment in pulsars
Observations have shown a strong tendency for alignment of the pulsar space velocity and spin axis in young pulsars but, up to now, these comparisons have been restricted to two dimensions.
Astrophysics of Galaxies
Efficient Discretizations of Optimal Transport
Obtaining solutions to Optimal Transportation (OT) problems is typically intractable when the marginal spaces are continuous.
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport
We consider constrained policy optimization in Reinforcement Learning, where the constraints are in form of marginals on state visitations and global action executions.
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice
This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation.
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree.
A Machine Teaching Framework for Scalable Recognition
Preliminary studies show that the accuracy of classifiers trained on the final dataset is a function of the accuracy of the student annotators.
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation
Based on this framework, we enhance the local feature representation by sampling and feeding the point-based features that locate on the semantic edges to an individual Semantic-guided Edge Enhancement module (SEEM), which is specifically designed for promoting depth estimation on the challenging semantic borders.
Automatic sampling and training method for wood-leaf classification based on tree terrestrial point cloud
The point cloud data of ten trees were tested by using the proposed method and a manual selection method.
Search-based Kinodynamic Motion Planning for Omnidirectional Quadruped Robots
Autonomous navigation has played an increasingly significant role in quadruped robot system.
Autonomous Navigation
Motion Planning
Robotics
Learning a manifold from a teacher’s demonstrations
We consider the problem of learning a manifold from a teacher's demonstration.
Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier
Motivated by this, a deep realistic taxonomic classifier (Deep-RTC) is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions.
Estimating the Number of Infected Cases in COVID-19 Pandemic
We propose a structured approach for the estimation of the number of unreported cases, where we distinguish cases that arrive late in the reported numbers and those who had mild or no symptoms and thus were not captured by any medical system at all.
SCOUT: Self-aware Discriminant Counterfactual Explanations
It is argued that self-awareness, namely the ability to produce classification confidence scores, is important for the computation of discriminant explanations, which seek to identify regions where it is easy to discriminate between prediction and counter class.
Automated classification of stems and leaves of potted plants based on point cloud data
A classification method was proposed to classify the leaves and stems of potted plants automatically based on the point cloud data of the plants, which is a nondestructive acquisition.
Sequential Cooperative Bayesian Inference
We seek foundational theoretical results for cooperative inference by Bayesian agents through sequential data.
Automatic marker-free registration of tree point-cloud data based on rotating projection
In coarse registration, point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional (2D) images, which are used to estimate the initial positions of multiple scans.
Deliberative Explanations: visualizing network insecurities
Since insecurity detection requires quantifying the difficulty of network predictions, deliberative explanations combine ideas from the literatures on visual explanations and assessment of classification difficulty.
Learning a manifold from a teacher's demonstrations
We consider the problem of learning a manifold from a teacher's demonstration.
A mathematical theory of cooperative communication
Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction.
Variational Bayesian Weighted Complex Network Reconstruction
Complex network reconstruction is a hot topic in many fields.
Generalizing the theory of cooperative inference
Cooperation information sharing is important to theories of human learning and has potential implications for machine learning.
Towards Realistic Predictors
It is argued that this should be a predictor independent of the classifier itself, but tuned to it, and learned without explicit supervision, so as to learn from its mistakes.
Pulsar Candidate Identification with Artificial Intelligence Techniques
To address these problems, we proposed a framework which combines deep convolution generative adversarial network (DCGAN) with support vector machine (SVM) to deal with imbalance class problem and to improve pulsar identification accuracy.
3D Randomized Connection Network with Graph-based Label Inference
In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity.
Weighted Orthogonal Components Regression Analysis
In the multiple linear regression setting, we propose a general framework, termed weighted orthogonal components regression (WOCR), which encompasses many known methods as special cases, including ridge regression and principal components regression.
Optimal Cooperative Inference
Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners.
Automated Low-cost Terrestrial Laser Scanner for Measuring Diameters at Breast Height and Heights of Forest Trees
The new scanner was named as BEE, which can scan the forest trees in three dimension.
Translingual Obfuscation
We introduce translingual obfuscation, a new software obfuscation scheme which makes programs obscure by "misusing" the unique features of certain programming languages.
Cryptography and Security Software Engineering
