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Found 43 papers, 11 papers with code
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.
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 Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
This book offers an in-depth exploration of object detection and semantic segmentation, combining theoretical foundations with practical applications.
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
This manuscript presents a comprehensive guide to Automated Machine Learning (AutoML), 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
This book serves as an introduction to deep learning and machine learning, focusing on their applications in big data analytics.
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.
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer
This book explores the role of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in driving the progress of big data analytics and management.
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.
Adaptive Frequency Enhancement Network for Single Image Deraining
We employ convolutions of different scales to adaptively decompose image frequency bands, introduce a feature enhancement module to boost the features of different frequency components and present a novel interaction module for interchanging and merging information from various frequency branches.
Infinite Motion: Extended Motion Generation via Long Text Instructions
We further demonstrate the versatility and practical utility of "Infinite Motion" through three specific applications: natural language interactive editing, motion sequence editing within long sequences and splicing of independent motion sequences.
FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality
Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs).
Parameter-Efficient Fine-Tuning With Adapters
In the arena of language model fine-tuning, the traditional approaches, such as Domain-Adaptive Pretraining (DAPT) and Task-Adaptive Pretraining (TAPT), although effective, but computational intensive.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
WanJuan-CC: A Safe and High-Quality Open-sourced English Webtext Dataset
To evaluate the quality and utility of the dataset, we trained 1B-parameter and 3B-parameter models using WanJuan-CC and another dataset, RefinedWeb.
F-Eval: Assessing Fundamental Abilities with Refined Evaluation Methods
However, these evaluation benchmarks are limited to assessing the instruction-following capabilities, overlooking the fundamental abilities that emerge during the pre-training stage.
GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting
In this work, we propose a novel clothed human reconstruction method called GaussianBody, based on 3D Gaussian Splatting.
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way
This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, LOMO and AdaLomo.
Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation
Experiments on English-German and English-Romanian translation show that: (i) Epi-Curriculum improves both model's robustness and adaptability in seen and unseen domains; (ii) Our episodic training framework enhances the encoder and decoder's robustness to domain shift.
DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering
Realistic human-centric rendering plays a key role in both computer vision and computer graphics.
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies
Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations.
NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation
Temporal video segmentation is the get-to-go automatic video analysis, which decomposes a long-form video into smaller components for the following-up understanding tasks.
See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval
To explore the fine-grained alignment, we further propose two implicit semantic alignment paradigms: multi-level alignment (MLA) and bidirectional mask modeling (BMM).
Explainable and High-Performance Hate and Offensive Speech Detection
For unbalanced Twitter data, XGboost outperformed the LSTM, AutoGluon, and ULMFiT models on hate speech detection with an F1 score of 0. 75 compared to 0. 38 and 0. 37, and 0. 38 respectively.
Scene Consistency Representation Learning for Video Scene Segmentation
Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods.
SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification
The results show that our two-stage training strategy effectively addresses the class imbalance classification problem, and significantly improves existing works in terms of F1-score and AUC score, resulting in state-of-the-art performance.
Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competition
Automatic affective recognition has been an important research topic in human computer interaction (HCI) area.
Learning a Facial Expression Embedding Disentangled From Identity
The facial expression analysis requires a compact and identity-ignored expression representation.
AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis
Generating high-fidelity talking head video by fitting with the input audio sequence is a challenging problem that receives considerable attentions recently.
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation
The generalization capability of machine learning models, which refers to generalizing the knowledge for an "unseen" domain via learning from one or multiple seen domain(s), is of great importance to develop and deploy machine learning applications in the real-world conditions.
Ranked #33 on
Domain Generalization
on VLCS
Modeling Caricature Expressions by 3D Blendshape and Dynamic Texture
The problem of deforming an artist-drawn caricature according to a given normal face expression is of interest in applications such as social media, animation and entertainment.
Facial Expression Retargeting from Human to Avatar Made Easy
Then we construct correspondences between the two latent spaces guided by geometric and perceptual constraints.
SAIA: Split Artificial Intelligence Architecture for Mobile Healthcare System
Hence, most of the mobile healthcare systems leverage the cloud computing infrastructure, where the data collected by the mobile and IoT devices would be transmitted to the cloud computing platforms for analysis.
CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification
Since the skin lesion datasets are usually limited and statistically biased, while designing an effective fusion approach, it is important to consider not only the performance of each classifier on the training/validation dataset, but also the relative discriminative power (e. g., confidence) of each classifier regarding an individual sample in the testing phase, which calls for an active fusion approach.
Learning Adaptive Loss for Robust Learning with Noisy Labels
Four kinds of SOTA robust loss functions are attempted to be integrated into our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its accuracy and generalization performance, as compared with conventional hyperparameter tuning strategy, even with carefully tuned hyperparameters.
Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face Recognition
By this means, the discriminativeness of the face representations can be enhanced.
Disentangled Representation Learning for 3D Face Shape
In this paper, we present a novel strategy to design disentangled 3D face shape representation.
Image Matters: Visually modeling user behaviors using Advanced Model Server
So we propose to model user preference jointly with user behavior ID features and behavior images.
