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Found 78 papers, 21 papers with code
Uni-AIMS: AI-Powered Microscopy Image Analysis
This paper presents a systematic solution for the intelligent recognition and automatic analysis of microscopy images.
Improving Significant Wave Height Prediction Using Chronos Models
Accurate wave height prediction is critical for maritime safety and coastal resilience, yet conventional physics-based models and traditional machine learning methods face challenges in computational efficiency and nonlinear dynamics modeling.
HyRRT-Connect: Bidirectional Motion Planning for Hybrid Dynamical Systems
To address the potential discontinuity along the flow caused by tolerating some distance between the forward and backward partial motion plans, we reconstruct the backward partial motion plan by a forward-in-hybrid-time simulation from the final state of the forward partial motion plan.
ReaderLM-v2: Small Language Model for HTML to Markdown and JSON
We present ReaderLM-v2, a compact 1. 5 billion parameter language model designed for efficient web content extraction.
Physics-Informed Implicit Neural Representations for Joint B0 Estimation and Echo Planar Imaging
Echo Planar Imaging (EPI) is widely used for its rapid acquisition but suffers from severe geometric distortions due to B0 inhomogeneities, particularly along the phase encoding direction.
A$^2$ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization
A$^2$ATS aims to obtain an accurate approximation of attention scores by applying the vector quantization technique to key states, thereby enabling efficient and precise retrieval of the top-K tokens.
PUGS: Zero-shot Physical Understanding with Gaussian Splatting
We propose a new method that reconstructs 3D objects using the Gaussian splatting representation and predicts various physical properties in a zero-shot manner.
XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applications
In terms of feature matching, we introduce a hybrid method that combines optical flow and descriptor-based matching.
StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation
Recent advances in large reconstruction and generative models have significantly improved scene reconstruction and novel view generation.
GenX: Mastering Code and Test Generation with Execution Feedback
Recent advancements in language modeling have enabled the translation of natural language into code, and the use of execution feedback to improve code generation.
AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark
The testing data in AIR-Bench is generated with respect to diverse tasks, domains and languages.
jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images
Contrastive Language-Image Pretraining (CLIP) has been widely used for crossmodal information retrieval and multimodal understanding tasks.
ACE-Net: AutofoCus-Enhanced Convolutional Network for Field Imperfection Estimation with application to high b-value spiral Diffusion MRI
Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts.
XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM
In this paper, we propose a flexible SLAM framework, XRDSLAM.
jina-embeddings-v3: Multilingual Embeddings With Task LoRA
We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens.
Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever
Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval.
LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations
The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality.
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
To address these issues, in this paper, we propose PE-Rank, leveraging the single passage embedding as a good context compression for efficient listwise passage reranking.
Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach
Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning paradigm for the 6G Hyper-Connectivity, harnessing data from the Internet of Things (IoT) devices while upholding data privacy.
PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction
Experiments on indoor and outdoor scenes show that our method achieves fast training and rendering while maintaining high-fidelity rendering and geometric reconstruction, outperforming 3DGS-based and NeRF-based methods.
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs.
Jina CLIP: Your CLIP Model Is Also Your Text Retriever
Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors.
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings
We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language.
Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data
This kind of continuous monitoring, in contrast to the short-term one, collects an extended period of fetal heart data.
Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection
Specifically, following this perspective, we design a Redundant Spectrum Removal module to remove interfering information within each modality coarsely and a Dynamic Feature Selection module to finely select the desired features for feature fusion.
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
To address this challenging problem, we propose a novel Causal OOD Heterogeneous graph Few-shot learning model, namely COHF.
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively.
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority.
Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information.
RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments
In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems.
Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM
In this paper, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone.
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
To address this issue in prompt engineering, we propose a new and effective approach called Prompt Space.
Exploring Global and Local Information for Anomaly Detection with Normal Samples
To address such problem, we propose an anomaly detection method GALDetector which is combined of global and local information based on observed normal samples.
BrainNPT: Pre-training of Transformer networks for brain network classification
In this paper, we focused on pre-training methods with Transformer networks to leverage existing unlabeled data for brain functional network classification.
TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph
APT detection is difficult to detect due to the long-term latency, covert and slow multistage attack patterns of Advanced Persistent Threat (APT).
A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records
We developed three types of NLP models (i. e., conditional random field, bi-directional long short-term memory and CancerBERT) to extract cancer phenotypes from clinical texts.
Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks
Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy.
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
However, the bias inherent in user written text, often used for PTG model training, can inadvertently associate different levels of linguistic quality with users' protected attributes.
VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM
Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures.
MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet
U-Nets have achieved tremendous success in medical image segmentation.
AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
This paper proposed a novel anomaly detection (AD) approach of High-speed Train images based on convolutional neural networks and the Vision Transformer.
GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis
To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE.
IMO$^3$: Interactive Multi-Objective Off-Policy Optimization
This problem has been studied extensively in the setting of known objective functions.
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction
Then we train a global GCN node classifier across institutions using a federated graph learning platform.
Rank4Class: A Ranking Formulation for Multiclass Classification
Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes.
Comparative Explanations of Recommendations
As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i. e., comparative explanations about the recommended items.
Unbiased Graph Embedding with Biased Graph Observations
In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes.
Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank
We further demonstrate that the most popular MCC architecture in deep learning can be mathematically formulated as a LTR pipeline equivalently, with a specific set of choices in terms of ranking model architecture and loss function.
An MRC Framework for Semantic Role Labeling
We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense.
Ranked #1 on
Semantic Role Labeling
on CoNLL 2005
CancerBERT: a BERT model for Extracting Breast Cancer Phenotypes from Electronic Health Records
This data used in the study included 21, 291 breast cancer patients diagnosed from 2010 to 2020, patients' clinical notes and pathology reports were collected from the University of Minnesota Clinical Data Repository (UMN).
The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches from Google Earth images.
CORe: Capitalizing On Rewards in Bandit Exploration
We propose a bandit algorithm that explores purely by randomizing its past observations.
Explanation as a Defense of Recommendation
At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation.
Summarizing Medical Conversations via Identifying Important Utterances
For the particular dataset used in this study, we show that high-quality summaries can be generated by extracting two types of utterances, namely, problem statements and treatment recommendations.
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network
For high-dimensional data, there are huge communication costs for distributed GBDT because the communication volume of GBDT is related to the number of features.
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network
The brain structure in the collected data is complicated, thence, doctors are required to spend plentiful energy when diagnosing brain abnormalities.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Studying Challenges in Medical Conversation with Structured Annotation
Medical conversation is a central part of medical care.
Directional Multivariate Ranking
In this work, we propose a directional multi-aspect ranking criterion to enable a holistic ranking of items with respect to multiple aspects.
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction
Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring (IPS) to eliminate bias in implicit feedback.
Graph Learning Approaches to Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Structural Combinatorial of Network Information System of Systems based on Evolutionary Optimization Method
The network information system is a military information network system with evolution characteristics.
UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing
However, wavelength-dependent light attenuation and back-scattering result in color distortion and haze effect, which degrade the visibility of images.
BPMR: Bayesian Probabilistic Multivariate Ranking
The framework naturally leads to a probabilistic multi-aspect ranking criterion, which generalizes the single-aspect ranking to a multivariate fashion.
The FacT: Taming Latent Factor Models for Explainability with Factorization Trees
In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors.
DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments
When compared to executing applications on the Edge servers without dynamic vertical scaling, static priorities and dynamic priorities reduce SLO violation rates of requests by up to 4% and 12% for the online game, respectively, and in both cases 6% for the face detection workload.
Distributed, Parallel, and Cluster Computing Systems and Control
Coding Structures and Actions with the COSTA Scheme in Medical Conversations
This paper describes the COSTA scheme for coding structures and actions in conversation.
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction.
YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction
This paper describes our approach to SemEval-2018 Task 2, which aims to predict the most likely associated emoji, given a tweet in English or Spanish.
YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English
This paper describes our submission to IJCNLP 2017 shared task 4, for predicting the tags of unseen customer feedback sentences, such as comments, complaints, bugs, requests, and meaningless and undetermined statements.
Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes
Generative Adversarial Networks are proved to be efficient on various kinds of image generation tasks.
Unpaired Photo-to-Caricature Translation on Faces in the Wild
We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks.
Challenges and Opportunities in Edge Computing
Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables.
Distributed, Parallel, and Cluster Computing
A new boosting algorithm based on dual averaging scheme
Better optimization algorithms that minimize the training loss can possibly give very poor generalization performance.
Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics
We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models.
Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines
Our work demonstrates the centered GDBM is a meaningful model approach for basic receptive field properties and the emergence of spontaneous activity patterns in early cortical visual areas.
