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Found 101 papers, 40 papers with code
Offline Trajectory Generalization for Offline Reinforcement Learning
Then we propose four strategies to use World Transformers to generate high-rewarded trajectory simulation by perturbing the offline data.
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base.
Generative Retrieval as Multi-Vector Dense Retrieval
Both methods compute relevance as a sum of products of query and document vectors and an alignment matrix.
Oh! We Freeze: Improving Quantized Knowledge Distillation via Signal Propagation Analysis for Large Language Models
Large generative models such as large language models (LLMs) and diffusion models have revolutionized the fields of NLP and computer vision respectively.
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
Attention mechanisms have been widely used to capture long-range dependencies among nodes in Graph Transformers.
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
In particular, local editing methods, which directly update model parameters, are more suitable for updating a small amount of knowledge.
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle long-range dependencies because of inherent locality or computational complexity.
On the Effectiveness of Unlearning in Session-Based Recommendation
On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session.
Improving Factual Error Correction by Learning to Inject Factual Errors
Given the lack of paired data (i. e., false claims and their corresponding correct claims), existing methods typically adopt the mask-then-correct paradigm.
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure
Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples.
GPT-4V(ision) Unsuitable for Clinical Care and Education: A Clinician-Evaluated Assessment
OpenAI's large multimodal model, GPT-4V(ision), was recently developed for general image interpretation.
How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model
The study surveys existing modal alignment methods in MLLMs into four groups: (1) Multimodal Converters that change data into something LLMs can understand; (2) Multimodal Perceivers to improve how LLMs perceive different types of data; (3) Tools Assistance for changing data into one common format, usually text; and (4) Data-Driven methods that teach LLMs to understand specific types of data in a dataset.
Learning Robust Sequential Recommenders through Confident Soft Labels
CSRec contains a teacher module that generates high-quality and confident soft labels and a student module that acts as the target recommender and is trained on the combination of dense, soft labels and sparse, one-hot labels.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs.
PMET: Precise Model Editing in a Transformer
To achieve more precise model editing, we analyze hidden states of MHSA and FFN, finding that MHSA encodes certain general knowledge extraction patterns.
Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge
The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89. 5\%, 90. 9\%, and 88. 3\% on North American, European, and Asian cohorts, respectively.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
A physics-constrained machine learning method for mapping gapless land surface temperature
Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model.
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain.
SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation
Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness.
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
For the second issue, we propose introducing contrastive signals between augmented states and the state randomly sampled from other sessions to improve the state representation learning further.
Point Transformer For Coronary Artery Labeling
Coronary CT angiography (CCTA) scans are widely used for diagnosis of coronary artery diseases.
Segment Anything in Medical Images
Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring.
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Different from conventional knowledge distillation, GRAD jointly optimizes a GNN teacher and a graph-free student over the graph's nodes via a shared LM.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Dynamic Multi-View Fusion Mechanism For Chinese Relation Extraction
Recently, many studies incorporate external knowledge into character-level feature based models to improve the performance of Chinese relation extraction.
Breathing cluster in complex neuron-astrocyte networks
It is revealed that the contents of the cluster are determined by the network symmetry and the breathing activities are due to the interplay between the neural network and the astrocyte.
Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation
We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
A Method to Judge the Style of Classical Poetry Based on Pre-trained Model
One of the important topics in the research field of Chinese classical poetry is to analyze the poetic style.
Span-based joint entity and relation extraction augmented with sequence tagging mechanism
On the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE.
Joint Entity and Relation Extraction
named-entity-recognition
+3
On the User Behavior Leakage from Recommender System Exposure
Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that the user past behavior privacy has a high risk of leakage in recommender systems.
A Two-Phase Paradigm for Joint Entity-Relation Extraction
An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.
Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes
Few-shot named entity recognition (NER) enables us to build a NER system for a new domain using very few labeled examples.
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search
Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness.
SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder
However, the existing evaluation metrics for summary text are only rough proxies for summary quality, suffering from low correlation with human scoring and inhibition of summary diversity.
Topic-Grained Text Representation-based Model for Document Retrieval
Document retrieval enables users to find their required documents accurately and quickly.
Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition
Previous research has demonstrated that the two paradigms have clear complementary advantages, but few models have attempted to leverage these advantages in a single NER model as far as we know.
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation
In this paper, we propose to incorporate the knowledge graph (KG) for CDR, which enables items in different domains to share knowledge.
Efficient and effective training of language and graph neural network models
The effectiveness in our framework is achieved by applying stage-wise fine-tuning of the BERT model first with heterogenous graph information and then with a GNN model.
Incremental Few-Shot Learning via Implanting and Compressing
Specifically, in the \textbf{Implanting} step, we propose to mimic the data distribution of novel classes with the assistance of data-abundant base set, so that a model could learn semantically-rich features that are beneficial for discriminating between the base and other unseen classes.
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data
In this paper, we present an integrated temperature fusion framework for satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m spatial resolution and half-hourly temporal resolution.
Towards Generalized and Incremental Few-Shot Object Detection
We conduct experiments on both Pascal VOC and MS-COCO, which demonstrate that our method can effectively solve the problem of incremental few-shot detection and significantly improve the detection accuracy on both base and novel classes.
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model
Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, by directly mapping perception inputs into robot control commands.
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.
Inference on Individual Treatment Effects in Nonseparable Triangular Models
In nonseparable triangular models with a binary endogenous treatment and a binary instrumental variable, Vuong and Xu (2017) established identification results for individual treatment effects (ITEs) under the rank invariance assumption.
Improving Transformer-based Sequential Recommenders through Preference Editing
By doing so, the SR model is able to learn how to identify common and unique user preferences, and thereby do better user preference extraction and representation.
Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanism
To solve this problem, we pro-pose Sequence Tagging enhanced Span-based Network (STSN), a span-based joint extrac-tion network that is enhanced by token BIO label information derived from sequence tag-ging based NER.
Joint Entity and Relation Extraction
named-entity-recognition
+4
Semi-Supervised Variational Reasoning for Medical Dialogue Generation
We propose an end-to-end variational reasoning approach to medical dialogue generation.
Generalized Iterative Super-Twisting Sliding Mode Control: A Case Study on Flexure-Joint Dual-Drive H-Gantry Stage
Mechatronic systems are commonly used in the industry, where fast and accurate motion performance is always required to guarantee manufacturing precision and efficiency.
Global Iterative Sliding Mode Control of an Industrial Biaxial Gantry System for Contouring Motion Tasks
This paper proposes a global iterative sliding mode control approach for high-precision contouring tasks of a flexure-linked biaxial gantry system.
Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System
Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features.
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
With this growth, however, come new challenges for the community.
Excedance-type polynomials and gamma-positivity
We first give a sufficient condition for a sequence of polynomials to have alternatingly increasing property, and then we present a systematic study of the joint distribution of excedances, fixed points and cycles of permutations and derangements, signed or not, colored or not.
Combinatorics 05A05
Galaxy Clusters from the DESI Legacy Imaging Surveys. I. Cluster Detection
Based on the photometric redshift catalog of Zou H. et al. (2019), we apply a fast clustering algorithm to identify 540, 432 galaxy clusters at $z\lesssim1$ in the DESI legacy imaging surveys, which cover a sky area of about 20, 000 deg$^2$.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?
Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation.
Loss Ensembles for Extremely Imbalanced Segmentation
This short paper briefly presents our methodology details of automatic intracranial aneurysms segmentation from brain MR scans.
Exploring Large Context for Cerebral Aneurysm Segmentation
Automated segmentation of aneurysms from 3D CT is important for the diagnosis, monitoring, and treatment planning of the cerebral aneurysm disease.
Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI
Automatic evaluation of myocardium and pathology plays an important role in the quantitative analysis of patients suffering from myocardial infarction.
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images
In this short paper, we propose an automatic segmentation method for head and neck tumors from PET and CT images based on the combination of convolutional neural networks (CNNs) and hybrid active contours.
Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation
Specifically, our method generates new training cases by using HM to transfer the intensity distribution of testing cases to existing training cases.
Unifying Homophily and Heterophily Network Transformation via Motifs
Because H2NT can sparsify networks with motif structures, it can also improve the computational efficiency of existing network embedding methods when integrated.
Mixed Information Flow for Cross-domain Sequential Recommendations
One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains.
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations
Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction.
Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization
Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models.
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.
Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs
In addition, the coverage accuracy of the RCEL confidence set is automatically robust against slight perturbation to the covariate balance condition, which may happen in cases such as data contamination and misspecified "unaffected" outcomes used as covariates.
Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation
The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence, which we referred it as the confidence-driven mean teacher.
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
Contextualized Emotion Recognition in Conversation as Sequence Tagging
Emotion recognition in conversation (ERC) is an important topic for developing empathetic machines in a variety of areas including social opinion mining, health-care and so on.
Ranked #2 on
Emotion Recognition in Conversation
on DailyDialog
Emotion Classification
Emotion Recognition in Conversation
+1
A Characteristic Function-based Algorithm for Geodesic Active Contours
Active contour models have been widely used in image segmentation, and the level set method (LSM) is the most popular approach for solving the models, via implicitly representing the contour by a level set function.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
Robust Fixed-Order Controller Design for Uncertain Systems with Generalized Common Lyapunov Strictly Positive Realness Characterization
It is also noteworthy that the proposed methodology additionally provides the necessary and sufficient conditions for this robust controller design with the consideration of a prescribed finite frequency range, and therefore significantly less conservatism is attained in the system performance.
Segmentation Loss Odyssey
Loss functions are one of the crucial ingredients in deep learning-based medical image segmentation methods.
Incremental Few-Shot Object Detection for Robotics
Incremental few-shot learning is highly expected for practical robotics applications.
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients.
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories
Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce.
A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation
To address this, we propose a neural topical expansion framework, namely Persona Exploration and Exploitation (PEE), which is able to extend the predefined user persona description with semantically correlated content before utilizing them to generate dialogue responses.
How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study
The \emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.
Meta Matrix Factorization for Federated Rating Predictions
Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module.
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations
We study sequential recommendation in a particularly challenging context, in which multiple individual users share asingle account (i. e., they have a shared account) and in which user behavior is available in multiple domains (i. e., recommendations are cross-domain).
Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation
Given a conversational context and background knowledge, we first learn a topic transition vector to encode the most likely text fragments to be used in the next response, which is then used to guide the local KS at each decoding timestamp.
Improving Outfit Recommendation with Co-supervision of Fashion Generation
FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information.
RefNet: A Reference-aware Network for Background Based Conversation
In this paper, we propose a Reference-aware Network (RefNet) to address the two issues.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation
RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user's history and recommends them at the right time.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
LinkNBed: Multi-Graph Representation Learning with Entity Linkage
Knowledge graphs have emerged as an important model for studying complex multi-relational data.
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation
The generated comments can be regarded as explanations for the recommendation results.
Chemi-net: a graph convolutional network for accurate drug property prediction
Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery.
Neural Attentive Session-based Recommendation
Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later.
Ranked #10 on
Session-Based Recommendations
on yoochoose1/64
Neural Att entive Session-based Recommendation
Specifically, we explore a hybrid encoder with an attention mechanism to model the user’s sequential behavior and capture the user’s main purpose in the current session, which are combined as a unified session representation later.
Prediction of amino acid side chain conformation using a deep neural network
A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy.
A genetic algorithm approach to fitting interferometric data of post-AGB objects: I. the case of the Ant nebula
Our result is consistent with a large dusty disc with similar parameter values to those previously found by Chesneau et al., except for a larger dust mass of $3. 5^{+7. 5}_{-2. 2}\times10^{-5}$ M$_{\odot}$.
Solar and Stellar Astrophysics
A Redundancy-Aware Sentence Regression Framework for Extractive Summarization
Existing sentence regression methods for extractive summarization usually model sentence importance and redundancy in two separate processes.
