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Found 89 papers, 40 papers with code
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
Trading Graph Neural Network
This paper proposes a new algorithm -- Trading Graph Neural Network (TGNN) that can structurally estimate the impact of asset features, dealer features and relationship features on asset prices in trading networks.
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
The era of intelligent agents is upon us, driven by revolutionary advancements in large language models.
A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation
In this work, we introduce a novel conTrastive prEtrain Model with Prompt Tuning (TEMPT) for multi-center medication recommendation, which includes two stages of pretraining and finetuning.
Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation
The Amar framework comprises two key sub-components: 1) a self-alignment module that aligns commonalities among entities, relations, and subgraphs to enhance retrieved text, thereby reducing noise interference; 2) a relevance gating module that employs a soft gate to learn the relevance score between question and multi-aspect retrieved data, to determine which information should be used to enhance LLMs' output, or even filtered altogether.
PrivAgent: Agentic-based Red-teaming for LLM Privacy Leakage
We also show PrivAgent's effectiveness in extracting training data from an open-source LLM with a success rate of 5. 9%.
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding
In this paper, we address these hallucination issues in the context of Medical Information Extraction (MIE) tasks by introducing ALternate Contrastive Decoding (ALCD).
Soft-Label Integration for Robust Toxicity Classification
Toxicity classification in textual content remains a significant problem.
BlockFound: Customized blockchain foundation model for anomaly detection
We propose BlockFound, a customized foundation model for anomaly blockchain transaction detection.
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
In this paper, we introduce LLMEmb, a novel method leveraging LLM to generate item embeddings that enhance SRS performance.
RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework
3D Multi-Object Tracking (MOT) obtains significant performance improvements with the rapid advancements in 3D object detection, particularly in cost-effective multi-camera setups.
RepAct: The Re-parameterizable Adaptive Activation Function
Addressing the imperative need for efficient artificial intelligence in IoT and edge computing, this study presents RepAct, a re-parameterizable adaptive activation function tailored for optimizing lightweight neural networks within the computational limitations of edge devices.
AutoPal: Autonomous Adaptation to Users for Personal AI Companionship
However, imbuing agents with autonomous adaptability presents unique challenges, including identifying optimal adaptations to meet users' expectations and ensuring a smooth transition during the adaptation process.
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
As one of the pioneers in this field, we propose the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR).
RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
In this paper, we propose RICE, an innovative refining scheme for reinforcement learning that incorporates explanation methods to break through the training bottlenecks.
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Specifically, we propose a cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks.
Fast-Poly: A Fast Polyhedral Framework For 3D Multi-Object Tracking
3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception.
Ranked #2 on
3D Multi-Object Tracking
on nuScenes
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.
MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding
With appropriate data selection and training techniques, Large Language Models (LLMs) have demonstrated exceptional success in various medical examinations and multiple-choice questions.
Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds
Clinical reasoning refers to the cognitive process that physicians employ in evaluating and managing patients.
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.
Biomedical Entity Linking as Multiple Choice Question Answering
Although biomedical entity linking (BioEL) has made significant progress with pre-trained language models, challenges still exist for fine-grained and long-tailed entities.
Large Language Model Distilling Medication Recommendation Model
In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).
Large Language Models for Generative Information Extraction: A Survey
Information extraction (IE) aims to extract structural knowledge from plain natural language texts.
COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal
In recent years, there has been a growing interest in exploring dialogues with more complex goals, such as negotiation, persuasion, and emotional support, which go beyond traditional service-focused dialogue systems.
Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Biomedical entity linking (BioEL) has achieved remarkable progress with the help of pre-trained language models.
LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning
Instead, this paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales, with a latent variable called a reasoning skill.
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development.
A Survey of Large Language Models in Medicine: Progress, Application, and Challenge
Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face.
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
To address these two problems, we propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA.
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.
Ranked #1 on
Link Property Prediction
on ogbl-biokg
A Critical Escape Probability Formulation for Enhancing the Transient Stability of Power Systems with System Parameter Design
For the enhancement of the transient stability of power systems, the key is to define a quantitative optimization formulation with system parameters as decision variables.
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning
In addition, JoTR employs reinforcement learning with a reward-shaping mechanism to efficiently finetune the word-level dialogue policy, which allows the model to learn from its interactions, improving its performance over time.
MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning
To deal with the label shortage problem, we present a simple yet effective zero-shot approach MultiCapCLIP that can generate visual captions for different scenarios and languages without any labeled vision-caption pairs of downstream datasets.
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries
We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance.
ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation
We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and believable outputs and significantly outperforms existing zero-shot methods.
Exploring Social Media for Early Detection of Depression in COVID-19 Patients
Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
Explicit formulas for the Variance of the State of a Linearized Power System driven by Gaussian stochastic disturbances
In the linearized system of the power systems, the disturbance is modeled by a Brownian motion process, and the fluctuations are described by the covariance matrix of the associated stochastic process at the invariant probability distribution.
Aligning Source Visual and Target Language Domains for Unpaired Video Captioning
To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language.
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Most video-and-language representation learning approaches employ contrastive learning, e. g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs.
Ranked #2 on
Video Retrieval
on LSMDC
(text-to-video Mean Rank metric)
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention
To enhance the correlation between vision and language in disentangled spaces, we introduce the visual concepts to DiMBERT which represent visual information in textual format.
Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine
We demonstrate the effectiveness of our method in generating patient discharge instructions.
Prophet Attention: Predicting Attention with Future Attention for Image Captioning
Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.
Multi-modal Contrastive Representation Learning for Entity Alignment
Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with entities.
Ranked #2 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
Competence-based Multimodal Curriculum Learning for Medical Report Generation
Medical report generation task, which targets to produce long and coherent descriptions of medical images, has attracted growing research interests recently.
Graph-in-Graph Network for Automatic Gene Ontology Description Generation
This task aims to automatically generate a sentence that describes the function of a GO term belonging to one of the three categories, i. e., molecular function, biological process, and cellular component.
DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches
Researchers have explored various ways to generate realistic images from freehand sketches, e. g., for objects and human faces.
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model
To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.
Ranked #1 on
Spoken Language Understanding
on Spoken-SQuAD
AutoField: Automating Feature Selection in Deep Recommender Systems
Thereby, feature selection is a critical process in developing deep learning-based recommender systems.
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback
On one hand, the user may exit immediately after clicking the news as he dislikes the news content, leaving the noise in his positive implicit feedback; on the other hand, the user may be recommended multiple interesting news at the same time and only click one of them, producing the noise in his negative implicit feedback.
AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation
The acquired disease-grounded visual features can better represent the abnormal regions of the input image, which could alleviate data bias problem; 2) MGT module effectively uses the multi-grained features and Transformer framework to generate the long medical report.
Conditional Generation Net for Medication Recommendation
Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics.
Knowledge Matters: Radiology Report Generation with General and Specific Knowledge
In this paper, we propose a knowledge-enhanced radiology report generation approach introduces two types of medical knowledge: 1) General knowledge, which is input independent and provides the broad knowledge for report generation; 2) Specific knowledge, which is input dependent and provides the fine-grained knowledge for report generation.
Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment
In clinics, a radiology report is crucial for guiding a patient's treatment.
NeRF-SR: High-Quality Neural Radiance Fields using Supersampling
We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly low-resolution (LR) inputs.
EDGE: Explaining Deep Reinforcement Learning Policies
With the rapid development of deep reinforcement learning (DRL) techniques, there is an increasing need to understand and interpret DRL policies.
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
KGAE consists of a pre-constructed knowledge graph, a knowledge-driven encoder and a knowledge-driven decoder.
Towards Joint Intent Detection and Slot Filling via Higher-order Attention
Intent detection (ID) and Slot filling (SF) are two major tasks in spoken language understanding (SLU).
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning
Video captioning combines video understanding and language generation.
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model
As a result, the proposed approach can handle various tasks including: Audio-Oriented Multimodal Machine Comprehension, Machine Reading Comprehension and Machine Listening Comprehension, in a single model, making fair comparisons possible between our model and the existing unimodal MC models.
Contrastive Attention for Automatic Chest X-ray Report Generation
In addition, according to the analysis, the CA model can help existing models better attend to the abnormal regions and provide more accurate descriptions which are crucial for an interpretable diagnosis.
Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation
In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias; PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias.
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning
Recent research has confirmed the feasibility of backdoor attacks in deep reinforcement learning (RL) systems.
Control optimization for parametric hamiltonians by pulse reconstruction
Optimal control techniques provide a means to tailor the control pulses required to generate customized quantum gates, which helps to improve the resilience of quantum simulations to gate errors and device noise.
Quantum Physics
Prophet Attention: Predicting Attention with Future Attention
Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests
To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests.
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records
The goal is to facilitate the learning process in the target segments by leveraging the learned knowledge from data-sufficient source segments.
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Our improved rate serves as one of the first results where an algorithm outperforms SGD-DD on an interesting Markov chain and also provides one of the first theoretical analyses to support the use of experience replay in practice.
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
In this paper, we create a benchmark for predictive analysis of individuals from a perspective that integrates: physical and physiological behavior, psychological states and traits, and job performance.
Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding
While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information.
JMNet: A joint matting network for automatic human matting
We propose a novel end-to-end deep learning framework, the Joint Matting Network (JMNet), to automatically generate alpha mattes for human images.
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series
The prevalence of wearable sensors (e. g., smart wristband) is creating unprecedented opportunities to not only inform health and wellness states of individuals, but also assess and infer personal attributes, including demographic and personality attributes.
Automated Relational Meta-learning
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.
A⋆MCTS: SEARCH WITH THEORETICAL GUARANTEE USING POLICY AND VALUE FUNCTIONS
We apply our theoretical framework to different models for the noise distribution of the policy and value network as well as the distribution of rewards, and show that for these general models, the sample complexity is polynomial in D, where D is the depth of the search tree.
Multi-Grained Named Entity Recognition
This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Ranked #5 on
Nested Mention Recognition
on ACE 2005
Multi-Grained Named Entity Recognition
named-entity-recognition
+5
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model
In this paper we consider the problem of computing an $\epsilon$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time.
Multi-Grained Entity Proposal Network for Named Entity Recognition
In this paper, we focus on a new Named Entity Recognition (NER) task, i. e., the Multi-grained NER task.
Deep Portrait Image Completion and Extrapolation
We evaluate our method on public portrait image datasets, and show that it outperforms other state-of-art general image completion methods.
Graphics
Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model
In this paper we consider the problem of computing an $\epsilon$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time.
Optimization and Control
Interpretable Video Captioning via Trajectory Structured Localization
Automatically describing open-domain videos with natural language are attracting increasing interest in the field of artificial intelligence.
Neural Tensor Factorization
Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data.
Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes
Given a discounted Markov Decision Process (DMDP) with $|S|$ states, $|A|$ actions, discount factor $\gamma\in(0, 1)$, and rewards in the range $[-M, M]$, we show how to compute an $\epsilon$-optimal policy, with probability $1 - \delta$ in time \[ \tilde{O}\left( \left(|S|^2 |A| + \frac{|S| |A|}{(1 - \gamma)^3} \right) \log\left( \frac{M}{\epsilon} \right) \log\left( \frac{1}{\delta} \right) \right) ~ .
Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning
Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement.
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm.
Learning to Segment Object Candidates via Recursive Neural Networks
To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images.
End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning
Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches.
