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Found 58 papers, 9 papers with code
Causal Mean Field Multi-Agent Reinforcement Learning
Causality contains relatively invariant mechanisms behind interactions, though environments are nonstationary.
Large Language Models for Bioinformatics
With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications.
QueEn: A Large Language Model for Quechua-English Translation
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics.
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity.
OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion
Oracle bone script (OBS), as China's earliest mature writing system, present significant challenges in automatic recognition due to their complex pictographic structures and divergence from modern Chinese characters.
Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation
Our comparison with the zero-shot performance of GPT-4o and LLaMA 3. 1 405B, highlights the significant challenges these models face when translating into low-resource languages.
Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios
Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making.
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization
Experimental results on RoBERTa-large and OPT-1. 3B across multiple tasks show that HELENE achieves up to a 20x speedup compared to MeZO, with average accuracy improvements of 1. 5%.
Large Language Models for Manufacturing
The rapid advances in Large Language Models (LLMs) have the potential to transform manufacturing industry, offering new opportunities to optimize processes, improve efficiency, and drive innovation.
A Survey of Hallucination in Large Visual Language Models
The Large Visual Language Models (LVLMs) enhances user interaction and enriches user experience by integrating visual modality on the basis of the Large Language Models (LLMs).
Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images
To address these issues, we propose a novel framework called RA-SE-ASPP-Net, which incorporates Residual Blocks, Attention Mechanism, Squeeze-and-Excitation connection, and Atrous Spatial Pyramid Pooling to achieve precise and robust cell segmentation.
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware.
ECHOPulse: ECG controlled echocardio-grams video generation
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices.
Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity
Moreover, our framework incorporates a generation decoder that employs two proxy tasks, responsible for generating the impression from (1) images, via a captioning branch, and (2) findings, through a summarization branch.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration
DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective.
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation
Machine-based brain tumor segmentation can help doctors make better diagnoses.
Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports
Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization.
Accurate Explanation Model for Image Classifiers using Class Association Embedding
Recombining the individual code of a given sample with altered class-associated code leads to a synthetic real-looking sample with preserved individual characters but modified class-associated features and possibly flipped class assignments.
SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks
Robust and imperceptible adversarial video attack is challenging due to the spatial and temporal characteristics of videos.
LLMs for Coding and Robotics Education
To teach young children how to code and compete in robot challenges, large language models are being utilized for robot code explanation, generation, and modification.
Revolutionizing Finance with LLMs: An Overview of Applications and Insights
Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions.
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking
In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
Ophtha-LLaMA2: A Large Language Model for Ophthalmology
In recent years, pre-trained large language models (LLMs) have achieved tremendous success in the field of Natural Language Processing (NLP).
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations
After training the model on the two datasets, with labels that comprise 10% cell markers, the DET improved from 0. 130 to 0. 903 and 0. 116 to 0. 877.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder
Few studies conduct machine classification of ASD for participants below 5-year-old, but, with mediocre predictive accuracy.
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation
Class association embedding distills the global class-related features of all instances into a unified domain with well separation between classes.
Intelligent gradient amplification for deep neural networks
Deep learning models offer superior performance compared to other machine learning techniques for a variety of tasks and domains, but pose their own challenges.
Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI
Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically.
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning).
Meta-data Study in Autism Spectrum Disorder Classification Based on Structural MRI
Accurate diagnosis of autism spectrum disorder (ASD) based on neuroimaging data has significant implications, as extracting useful information from neuroimaging data for ASD detection is challenging.
Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder
This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI.
Generative Adversarial Networks: A Survey Towards Private and Secure Applications
Generative Adversarial Networks (GAN) have promoted a variety of applications in computer vision, natural language processing, etc.
Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks
The novel corona virus (Covid-19) has introduced significant challenges due to its rapid spreading nature through respiratory transmission.
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster.
Infant Vocal Tract Development Analysis and Diagnosis by Cry Signals with CNN Age Classification
In this paper, we propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of the vocal tract development as early as 4-month age.
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.
Infant Cry Classification with Graph Convolutional Networks
We propose an approach of graph convolutional networks for robust infant cry classification.
PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks
Deep learning has gained great success in various classification tasks.
Deep Neural Networks with Short Circuits for Improved Gradient Learning
Deep neural networks have achieved great success both in computer vision and natural language processing tasks.
Graph Convolution Networks Using Message Passing and Multi-Source Similarity Features for Predicting circRNA-Disease Association
In this paper, we propose a novel graph convolution network framework to learn features from a graph built with multi-source similarity information to predict circRNA-disease associations.
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News
This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement.
Improved Detection of Adversarial Images Using Deep Neural Networks
Machine learning techniques are immensely deployed in both industry and academy.
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm
However, traditional genetic algorithms with fixed-length chromosomes may not be a good fit for optimizing deep learning hyperparameters, because deep learning models have variable number of hyperparameters depending on the model depth.
Gradient Amplification: An efficient way to train deep neural networks
Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time.
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses.
Conversational AI: The Science Behind the Alexa Prize
This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational
Three-Stream Convolutional Networks for Video-based Person Re-Identification
This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks.
Reconstruction of Hidden Representation for Robust Feature Extraction
We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation.
Parallel Large-Scale Attribute Reduction on Cloud Systems
The rapid growth of emerging information technologies and application patterns in modern society, e. g., Internet, Internet of Things, Cloud Computing and Tri-network Convergence, has caused the advent of the era of big data.
Effective-one-body model for black-hole binaries with generic mass ratios and spins
Gravitational waves emitted by black-hole binary systems have the highest signal-to-noise ratio in LIGO and Virgo detectors when black-hole spins are aligned with the orbital angular momentum and extremal.
General Relativity and Quantum Cosmology
