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Found 75 papers, 46 papers with code
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended.
FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image Restoration
In this study, we reveal that the interaction between haze degradation and JPEG compression introduces complex joint loss effects, which significantly complicate image restoration.
GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation
Multi-organ segmentation is a critical yet challenging task due to complex anatomical backgrounds, blurred boundaries, and diverse morphologies.
WaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on Wavelet Transform in the Spatial-Frequency Domain
Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment.
ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer
Additionally, we conduct comprehensive experiments on both the generator and classifier, demonstrating the clinical relevance and effectiveness of ProjectedEx in enhancing interpretability and supporting the adoption of AI in medical settings.
SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies
Hepatic vessels in computed tomography scans often suffer from image fragmentation and noise interference, making it difficult to maintain vessel integrity and posing significant challenges for vessel segmentation.
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System
In this paper, we propose TrendSim, an LLM-based multi-agent system to simulate trending topics in social media under poisoning attacks.
Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based Approach
Moreover, the results also demonstrate that our method can obtain promising results even with $5\%$ known values left in the datasets.
KMM: Key Frame Mask Mamba for Extended Motion Generation
The recent Mamba architecture shows promising results in efficiently modeling long and complex sequences, yet two significant challenges remain: Firstly, directly applying Mamba to extended motion generation is ineffective, as the limited capacity of the implicit memory leads to memory decay.
Medical AI for Early Detection of Lung Cancer: A Survey
The recent emergence of deep learning has revolutionized medical image analysis, driving substantial advancements in this field.
CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms.
M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes
We propose M^3Bench, a new benchmark of whole-body motion generation for mobile manipulation tasks.
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
This limits the model to understand the rich semantic information in the data and its reasoning ability for complex downstream tasks, while also lacking interpretability.
MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries.
DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
The paper discusses signed graphs, which model friendly or antagonistic relationships using edges marked with positive or negative signs, focusing on the task of link sign prediction.
CSPS: A Communication-Efficient Sequence-Parallelism based Serving System for Transformer based Models with Long Prompts
However, SP introduces two challenges: 1) network communication and computation become performance bottlenecks; 2) the latter two issues above are mitigated but not resolved, and SP's resultant KV value distribution across GPUs still requires communication for decode, increasing TBT.
MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule
Pulmonary nodules are critical indicators for the early diagnosis of lung cancer, making their detection essential for timely treatment.
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language Model
Furthermore, our approach exhibits major cost benefits: the average prediction quality of AnyMatch is within 4. 4% of the state-of-the-art method MatchGPT with the proprietary trillion-parameter model GPT-4, yet AnyMatch requires four orders of magnitude less parameters and incurs a 3, 899 times lower inference cost (in dollars per 1, 000 tokens).
MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection
Additionally, we customize the second-order nmODE to improve the model's resistance to noise in MRI.
ESA: Annotation-Efficient Active Learning for Semantic Segmentation
Active learning enhances annotation efficiency by selecting the most revealing samples for labeling, thereby reducing reliance on extensive human input.
Self-Explainable Graph Transformer for Link Sign Prediction
Our goal is to address the explainability of decision-making for the downstream task of link sign prediction specific to signed graph neural networks.
Mitigating Degree Bias in Signed Graph Neural Networks
To handle the confronted bias issue, inspired by previous work on degree bias, a new Model-Agnostic method is consequently proposed to enhance representation of nodes with different degrees, which named as Degree Debiased Signed Graph Neural Network (DD-SGNN) .
Zero-Delay QKV Compression for Mitigating KV Cache and Network Bottlenecks in LLM Inference
To tackle these issues, based on our insightful observations from experimental analysis, we propose ZeroC, a Zero-delay QKV Compression system that eliminates time overhead and even reduces computation and communication time of the model operations.
SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation
Medical imaging segmentation plays a significant role in the automatic recognition and analysis of lesions.
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
The rapid evolution of multimedia and computer vision technologies requires adaptive visual model deployment strategies to effectively handle diverse tasks and varying environments.
XLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training
To this end, this paper proposes a XLIP (Masked modelling for medical Language-Image Pre-training) framework to enhance pathological learning and feature learning via unpaired data.
InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation
Text-to-motion generation holds potential for film, gaming, and robotics, yet current methods often prioritize short motion generation, making it challenging to produce long motion sequences effectively: (1) Current methods struggle to handle long motion sequences as a single input due to prohibitively high computational cost; (2) Breaking down the generation of long motion sequences into shorter segments can result in inconsistent transitions and requires interpolation or inpainting, which lacks entire sequence modeling.
Motion Avatar: Generate Human and Animal Avatars with Arbitrary Motion
Firstly, we proposed a novel agent-based approach named Motion Avatar, which allows for the automatic generation of high-quality customizable human and animal avatars with motions through text queries.
Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures.
A Survey on the Memory Mechanism of Large Language Model based Agents
Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions.
JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA
The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders.
Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V
Autonomous robot navigation and manipulation in open environments require reasoning and replanning with closed-loop feedback.
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph.
Sine Activated Low-Rank Matrices for Parameter Efficient Learning
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning.
Manifold Regularization Classification Model Based On Improved Diffusion Map
Manifold regularization model is a semi-supervised learning model that leverages the geometric structure of a dataset, comprising a small number of labeled samples and a large number of unlabeled samples, to generate classifiers.
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging.
LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
Through a series of simulations in a box-packing domain, we quantitatively demonstrate the effectiveness of LLM^3 in solving TAMP problems and the efficiency in selecting action parameters.
Strong and Controllable Blind Image Decomposition
To address this need, we add controllability to the blind image decomposition process, allowing users to enter which types of degradation to remove or retain.
Motion Mamba: Efficient and Long Sequence Motion Generation
Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging.
Ranked #23 on
Motion Synthesis
on HumanML3D
DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis
Diabetes, resulting from inadequate insulin production or utilization, causes extensive harm to the body.
SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models
To bridge the gap with the real-world requirements, we introduce $\textbf{SheetRM}$, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges.
Ranked #1 on
Robot Task Planning
on SheetCopilot
AgentScope: A Flexible yet Robust Multi-Agent Platform
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
On the Emergence of Symmetrical Reality
Artificial intelligence (AI) has revolutionized human cognitive abilities and facilitated the development of new AI entities capable of interacting with humans in both physical and virtual environments.
Device-Wise Federated Network Pruning
Neural network pruning particularly channel pruning is a widely used technique for compressing deep learning models to enable their deployment on edge devices with limited resources.
PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images
Self-supervised learning (SSL) for RGB images has achieved significant success, yet there is still limited research on SSL for infrared images, primarily due to three prominent challenges: 1) the lack of a suitable large-scale infrared pre-training dataset, 2) the distinctiveness of non-iconic infrared images rendering common pre-training tasks like masked image modeling (MIM) less effective, and 3) the scarcity of fine-grained textures making it particularly challenging to learn general image features.
S-T CRF: Spatial-Temporal Conditional Random Field for Human Trajectory Prediction
Trajectory prediction is of significant importance in computer vision.
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy.
Enhancing Signed Graph Neural Networks through Curriculum-Based Training
The prevailing training approach feeds samples (edges) to models in a random order, resulting in equal contributions from each sample during the training process, but fails to account for varying learning difficulties based on the graph's structure.
SGA: A Graph Augmentation Method for Signed Graph Neural Networks
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links.
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
Learnersourcing offers great potential for scalable education through student content creation.
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost.
A Survey on Large Language Model based Autonomous Agents
In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors.
X-VoE: Measuring eXplanatory Violation of Expectation in Physical Events
Intuitive physics is pivotal for human understanding of the physical world, enabling prediction and interpretation of events even in infancy.
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models.
User Behavior Simulation with Large Language Model based Agents
Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism of human decision process.
Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution
Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics.
Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning
Languages models have been successfully applied to a variety of reasoning tasks in NLP, yet the language models still suffer from compositional generalization.
USER: Unsupervised Structural Entropy-based Robust Graph Neural Network
To this end, we propose USER, an unsupervised robust version of graph neural networks that is based on structural entropy.
A Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps
In this work, we present a reconfigurable data glove design to capture different modes of human hand-object interactions, which are critical in training embodied artificial intelligence (AI) agents for fine manipulation tasks.
Structural Alignment for Network Pruning through Partial Regularization
To mitigate this gap, we first learn a target sub-network during the model training process, and then we use this sub-network to guide the learning of model weights through partial regularization.
Sequential Manipulation Planning on Scene Graph
We devise a 3D scene graph representation, contact graph+ (cg+), for efficient sequential task planning.
Understanding Physical Effects for Effective Tool-use
We present a robot learning and planning framework that produces an effective tool-use strategy with the least joint efforts, capable of handling objects different from training.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
In Situ Answer Sentence Selection at Web-scale
Current answer sentence selection (AS2) applied in open-domain question answering (ODQA) selects answers by ranking a large set of possible candidates, i. e., sentences, extracted from the retrieved text.
Double Retrieval and Ranking for Accurate Question Answering
Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering.
A Comparative Study on Robust Graph Neural Networks to Structural Noises
Although a series of robust GNNs have been proposed, they are evaluated with different structural noises, and it lacks a systematic comparison with consistent settings.
Joint Models for Answer Verification in Question Answering Systems
This paper studies joint models for selecting correct answer sentences among the top $k$ provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems.
A Dashboard for Mitigating the COVID-19 Misinfodemic
This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter.
Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments
In this paper, we rethink the problem of scene reconstruction from an embodied agent's perspective: While the classic view focuses on the reconstruction accuracy, our new perspective emphasizes the underlying functions and constraints such that the reconstructed scenes provide \em{actionable} information for simulating \em{interactions} with agents.
Lineage Evolution Reinforcement Learning
In the process of agent evolution, we refer to the characteristics of natural genetic behavior, add lineage factor to ensure the retention of potential performance of agent, and comprehensively consider the current performance and lineage value when evaluating the performance of agent.
A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization
Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large.
Congestion-aware Evacuation Routing using Augmented Reality Devices
We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees' locations.
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks.
Visualizing How Embeddings Generalize
Deep metric learning is often used to learn an embedding function that captures the semantic differences within a dataset.
