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Found 48 papers, 7 papers with code
From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty.
From Noise to Nuance: Advances in Deep Generative Image Models
We examine how recent developments in Stable Diffusion, DALL-E, and consistency models have redefined the capabilities and performance boundaries of image synthesis, while addressing persistent challenges in efficiency and quality.
From Bench to Bedside: A Review of Clinical Trials in Drug Discovery and Development
Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application.
Deep Learning Model Security: Threats and Defenses
Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage.
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management
This work explores the theoretical foundations, methodological advancements, and practical implementations of these technologies, emphasizing their role in uncovering actionable insights from massive, high-dimensional datasets.
A Comprehensive Guide to Explainable AI: From Classical Models to LLMs
Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes.
A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models.
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models
Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces.
Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application
With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence.
Large Language Model Benchmarks in Medical Tasks
With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial.
Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application
Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related fields.
Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL).
Deep Active Learning with Manifold-preserving Trajectory Sampling
The key question in AL is which unlabeled data should be selected for annotation.
Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
An in-depth exploration of object detection and semantic segmentation is provided, combining theoretical foundations with practical applications.
Jailbreaking and Mitigation of Vulnerabilities in Large Language Models
This review analyzes the state of research on these vulnerabilities and presents available defense strategies.
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
A comprehensive guide to Automated Machine Learning (AutoML) is presented, covering fundamental principles, practical implementations, and future trends.
Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
This book, Design Patterns in Machine Learning and Deep Learning: Advancing Big Data Analytics Management, presents a comprehensive study of essential design patterns tailored for large-scale machine learning and deep learning applications.
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications
Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management, enabling groundbreaking advancements across diverse industries.
LaDTalk: Latent Denoising for Synthesizing Talking Head Videos with High Frequency Details
Our experiments further demonstrate that the high-frequency texture deficiency of the foundation model can be temporally consistently recovered by the Space-Optimised Vector Quantised Auto Encoder (SOVQAE) we introduced, thereby facilitating the creation of realistic talking head videos.
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming
Object-Oriented Programming (OOP) has become a crucial paradigm for managing the growing complexity of modern software systems, particularly in fields like machine learning, deep learning, large language models (LLM), and data analytics.
Multimodal Generalized Category Discovery
Generalized Category Discovery (GCD) aims to classify inputs into both known and novel categories, a task crucial for open-world scientific discoveries.
From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice
This comprehensive review explores the progression of LLMs to Multimodal Large Language Models (MLLMs) and their growing influence in medical practice.
Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges
This comprehensive review explores the intersection of Large Language Models (LLMs) and cognitive science, examining similarities and differences between LLMs and human cognitive processes.
Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring
To address these issues, we propose a diffusion-based weakly-supervised approach for deriving health indicators of rotating machines, enabling early fault detection and continuous monitoring of condition evolution.
Training-free CryoET Tomogram Segmentation
In addition to prompt-based single-particle instance segmentation, our approach can automatically search for similar features, facilitating full tomogram semantic segmentation with only one prompt.
HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction
Drug target binding affinity (DTA) is a key criterion for drug screening.
Cycle-YOLO: A Efficient and Robust Framework for Pavement Damage Detection
Due to a substantial difference between the images generated by CycleGAN and real road images, we proposed a data enhancement method based on an improved Scharr filter, CycleGAN, and Laplacian pyramid.
Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation
MDT-AF incorporates an attention-based feature filtering mechanism into the patch embedding blocks and employs a coarse-to-fine process to mitigate the impact of low signal-to-noise ratio.
Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
This study conducts a comprehensive review and analysis of the existing literature on the attitudes of LLMs towards the 17 SDGs, emphasizing the comparison between their attitudes and support for each goal and those of humans.
Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation
The Segment Anything Model (SAM) gained significant success in natural image segmentation, and many methods have tried to fine-tune it to medical image segmentation.
Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning
3D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning.
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry
In such a way, FL is implemented as a privacy-enhancing collaborative learning technique that addresses the challenges posed by the sensitivity and privacy of data in traditional machine learning solutions.
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content
This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model.
MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug-Target Interactions Prediction
Drug-target interactions (DTIs) is essential for advancing pharmaceuticals.
DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation
Few-shot medical image semantic segmentation is of paramount importance in the domain of medical image analysis.
Temporal Output Discrepancy for Loss Estimation-based Active Learning
Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss.
Deep Active Learning with Noise Stability
We provide theoretical analyses by leveraging the small Gaussian noise theory and demonstrate that our method favors a subset with large and diverse gradients.
Towards Inadequately Pre-trained Models in Transfer Learning
Based on the contradictory phenomenon between FE and FT that better feature extractor fails to be fine-tuned better accordingly, we conduct comprehensive analyses on features before softmax layer to provide insightful explanations.
Boosting Active Learning via Improving Test Performance
In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in better test performance.
Semi-Supervised Active Learning with Temporal Output Discrepancy
To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset.
An Evaluation of novel method of Ill-Posed Problem for the Black-Scholes Equation solution
It was proposed by Klibanov a new empirical mathematical method to work with the Black-Scholes equation.
Numerical Analysis Numerical Analysis 35R30, 65K05, 35R25, 65M30 G.1.8; G.1.6
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy
In this paper, comprehensive experimental studies of implementing state-of-the-art CNNs for the detection and classification of DR are conducted in order to determine the top performing classifiers for the task.
Parameter-Free Style Projection for Arbitrary Style Transfer
This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer, which includes a regularization term for matching the semantics between input contents and stylized outputs.
Instance-based Deep Transfer Learning
It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain.
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
In this paper, we demonstrate that deep learning models such as convolutional neural networks may not favor all training samples, and generalization accuracy can be further improved by dropping those unfavorable samples.
Imbalanced Malware Images Classification: a CNN based Approach
To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs.
Dilated Deep Residual Network for Image Denoising
It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem.
An ELU Network with Total Variation for Image Denoising
In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function.
