Search Results for author:
Found 332 papers, 89 papers with code
Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach
In this paper, we address unsupervised chunking as a new task of syntactic structure induction, which is helpful for understanding the linguistic structures of human languages as well as processing low-resource languages.
Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering
Our method reaches state-of-the-art performance on 5 benchmark QA datasets, with up to 10% improvement in top-100 accuracy compared to a joint-training multi-task DPR on SQuAD.
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.
Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
This paper also provides criteria about when DIA data could be used for de novo peptide sequencing and when not to by providing a comparison between DDA and DIA, in both de novo and database search mode.
Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation
1) Speaker Detection: The proposed approach can utilize incompletely given speaker embeddings to discover the unknown speaker and predict the target voice activities in the audio signal.
MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity
Considering the above differences with LLM, we can draw a conclusion that: for a RecSys model, compared to model parameters, the computational complexity FLOPs is a more expensive factor that requires careful control.
Script-centric behavior understanding for assisted autism spectrum disorder diagnosis
Our pipeline converts video content into scripts that describe the behavior of characters, leveraging the generalizability of large language models to detect ASD in a zero-shot or few-shot manner.
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning
In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity.
Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout
In this paper, how to efficiently find the optimal path in complex warehouse layout and make real-time decision is a key problem.
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.
Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A Review of Characteristics, Approaches and Datasets
Besides, we evaluated the Technology Readiness Levels (TRLs) of urban flood mapping techniques to identify challenges and future research areas.
Multi-Reward as Condition for Instruction-based Image Editing
1) we first design a quantitative metric system based on best-in-class LVLM (Large Vision Language Model), i. e., GPT-4o in our case, to evaluate the generation quality from 3 perspectives, namely, instruction following, detail preserving, and generation quality.
EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark
In summary, we believe EEE-Bench not only reveals some noteworthy limitations of LMMs but also provides a valuable resource for advancing research on their application in practical engineering tasks, driving future improvements in their capability to handle complex, real-world scenarios.
Inference in a Stationary/Nonstationary Autoregressive Time-Varying-Parameter Model
This paper considers nonparametric estimation and inference in first-order autoregressive (AR(1)) models with deterministically time-varying parameters.
A Survey on Bundle Recommendation: Methods, Applications, and Challenges
In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items.
Estimation and Inference in Dyadic Network Formation Models with Nontransferable Utilities
This paper studies estimation and inference in a dyadic network formation model with observed covariates, unobserved heterogeneity, and nontransferable utilities.
How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?
In this work, we provide theoretical insights into this challenge by leveraging Flow Matching models, which transform a simple prior into a complex target distribution via a learned velocity field.
What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective
We are specifically interested in how fast vs. slow thinking affects the layer-wise gradients, given the recent popularity of training LLMs on reasoning paths such as chain-of-thoughts (CoT) and process rewards.
CRB Optimization using a Parametric Scattering Model for Extended Targets in ISAC Systems
This paper presents a novel parametric scattering model (PSM) for sensing extended targets in integrated sensing and communication (ISAC) systems.
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.
Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations
The fundamental idea of label filling is to supervise the segmentation model by a subset of pixels with trustworthy labels, meanwhile filling labels of other pixels by mixed supervision.
Joint Space-Time Adaptive Processing and Beamforming Design for Cell-Free ISAC Systems
In this paper, we explore cooperative sensing and communication within cell-free integrated sensing and communication (ISAC) systems.
BenTo: Benchmark Task Reduction with In-Context Transferability
Evaluating large language models (LLMs) is costly: it requires the generation and examination of LLM outputs on a large-scale benchmark of various tasks.
DOA Estimation-Oriented Joint Array Partitioning and Beamforming Designs for ISAC Systems
Integrated sensing and communication has been identified as an enabling technology for forthcoming wireless networks.
A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks
As reconfigurable intelligent surfaces (RIS) emerge as a pivotal technology in the upcoming sixth-generation (6G) networks, their deployment within practical multiple operator (OP) networks presents significant challenges, including the coordination of RIS configurations among OPs, interference management, and privacy maintenance.
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.
RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining
The remarkable development of text-to-image generation models has raised notable security concerns, such as the infringement of portrait rights and the generation of inappropriate content.
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization
Graph convolutional networks (GCNs) have emerged as powerful models for graph learning tasks, exhibiting promising performance in various domains.
Adversarial Attacks and Robust Defenses in Speaker Embedding based Zero-Shot Text-to-Speech System
Speaker embedding based zero-shot Text-to-Speech (TTS) systems enable high-quality speech synthesis for unseen speakers using minimal data.
Optimal Control in Both Steady State and Transient Process with Unknown Disturbances
The scheme of online optimization as a feedback controller is widely used to steer the states of a physical system to the optimal solution of a predefined optimization problem.
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.
Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement
Despite the impressive advancements made in recent low-light image enhancement techniques, the scarcity of paired data has emerged as a significant obstacle to further advancements.
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.
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification
Recent advances in fine-tuning Vision-Language Models (VLMs) have witnessed the success of prompt tuning and adapter tuning, while the classic model fine-tuning on inherent parameters seems to be overlooked.
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras
We implement two algorithms, in which the two-stage algorithm obtains initial depth maps by pairwise stereo matching of multiple cameras and fuses the multiple depth maps to achieve the final depth estimation; the one-stage algorithm adopts spherical sweeping based on hypothetical depths to construct a uniform spherical matching cost of the multi-camera images and obtain the depth.
Active Reconfigurable Intelligent Surface Empowered Synthetic Aperture Radar Imaging
In this paper, we first present a range-Doppler (RD) imaging algorithm to obtain imaging results for the proposed ARIS-empowered SAR system.
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised.
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.
TSELM: Target Speaker Extraction using Discrete Tokens and Language Models
We propose TSELM, a novel target speaker extraction network that leverages discrete tokens and language models.
Real-time Multi-view Omnidirectional Depth Estimation System for Robots and Autonomous Driving on Real Scenes
Omnidirectional Depth Estimation has broad application prospects in fields such as robotic navigation and autonomous driving.
Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge
The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multimodal Laryngoscopic Video Analysis for Assisted Diagnosis of Vocal Fold Paralysis
The system integrates video-based glottis detection with an audio keyword spotting method to analyze both video and audio data, identifying patient vocalizations and refining video highlights to ensure optimal inspection of vocal fold movements.
A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection
In contrast to human speech, machine-generated sounds of the same type often exhibit consistent frequency characteristics and discernible temporal periodicity.
Dynamic Hybrid Beamforming Designs for ELAA Near-Field Communications
Extremely large-scale antenna array (ELAA) is a key candidate technology for the sixth generation (6G) mobile networks.
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.
USEF-TSE: Universal Speaker Embedding Free Target Speaker Extraction
Traditionally, this process has relied on extracting a speaker embedding from a reference speech, necessitating a speaker recognition model.
One-Index Vector Quantization Based Adversarial Attack on Image Classification
In this paper, we propose a novel one-index attack method in the VQ domain to generate adversarial images by a differential evolution algorithm, successfully resulting in image misclassification in victim models.
A Joint Learning Model with Variational Interaction for Multilingual Program Translation
However, parallel data is difficult to collect for some language pairs, and the distribution of program semantics across languages can shift, posing challenges for pairwise program translation.
Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking
In this paper, we propose Top Pass, a code ranking approach that identifies potential correct solutions from a large number of candidates.
VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark
In this paper, we provide a large audio-visual speaker recognition dataset, VoxBlink2, which includes approximately 10M utterances with videos from 110K+ speakers in the wild.
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks
In our numerical experiments, we analyze several widely applied GCNs and observe the phenomenon of energy decay.
MIMO-OFDM ISAC Waveform Design for Range-Doppler Sidelobe Suppression
In addition, the proposed waveform design achieves target detection and estimation performance close to that achievable by waveforms designed only for radar, which demonstrates the superiority of the proposed SLP-based ISAC approach.
Understanding is Compression
The better a large model understands the data, the better LMCompress compresses.
Feature-prompting GBMSeg: One-Shot Reference Guided Training-Free Prompt Engineering for Glomerular Basement Membrane Segmentation
Experimental results on our collected 2538 TEM images confirm that GBMSeg achieves superior segmentation performance with a Dice similarity coefficient (DSC) of 87. 27% using only one labeled reference image in a training-free manner, outperforming recently proposed one-shot or few-shot methods.
RuleR: Improving LLM Controllability by Rule-based Data Recycling
Despite the remarkable advancement of Large language models (LLMs), they still lack delicate controllability under sophisticated constraints, which is critical to enhancing their response quality and the user experience.
Ranking LLMs by compression
We conceptualize the process of understanding as information compression, and propose a method for ranking large language models (LLMs) based on lossless data compression.
PFID: Privacy First Inference Delegation Framework for LLMs
Our framework was designed to be communication efficient, computation can be delegated to the local client so that the server's computation burden can be lightened.
AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection
The rapid advancements in speech technologies over the past two decades have led to human-level performance in tasks like automatic speech recognition (ASR) for fluent speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The Database and Benchmark for the Source Speaker Tracing Challenge 2024
We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks.
Multipath Exploitation for Fluctuating Target Detection in RIS-Assisted ISAC Systems
Integrated sensing and communication (ISAC) systems are typically deployed in multipath environments, which is usually deemed as a challenging issue for wireless communications.
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
However, given that most of the data in the autonomous driving dataset is collected in daytime scenarios, this leads to poor depth model performance in the face of out-of-distribution(OoD) data.
Scaling Laws for Discriminative Classification in Large Language Models
To address this issue we present a system that allows us to use an LLM to augment our customer support advocates by re-framing the language modeling task as a discriminative classification task.
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs.
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction Tuning
Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions.
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
Our extensive experiments, conducted on a diverse range of real-world and synthetic datasets with varying degrees of heterophily, support the superiority of UniFilter.
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
In this paper, we propose a new hierarchical pooling operation, namely the Edge-Node Attention-based Differentiable Pooling (ENADPool), for GNNs to learn effective graph representations.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
Frame Interpolation with Consecutive Brownian Bridge Diffusion
To address this problem, we propose our unique solution: Frame Interpolation with Consecutive Brownian Bridge Diffusion.
Towards Less Biased Data-driven Scoring with Deep Learning-Based End-to-end Database Search in Tandem Mass Spectrometry
Traditional database search methods, though widely used, rely on heuristic scoring functions and statistical estimations have to be introduced for a higher identification rate.
Semi-Supervised Disease Classification based on Limited Medical Image Data
By addressing the limitations imposed by limited labeled data and harnessing the untapped potential of unlabeled medical images, our novel generative model presents a promising direction for enhancing semi-supervised disease classification in the field of medical image analysis.
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?
So far, most NBR studies have focused on optimizing the accuracy of the recommendation, whereas optimizing for beyond-accuracy metrics, e. g., item fairness and diversity remains largely unexplored.
ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model
In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities.
ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback
Specifically, for an input conditional control, we use a pre-trained discriminative reward model to extract the corresponding condition of the generated images, and then optimize the consistency loss between the input conditional control and extracted condition.
MealRec$^+$: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness
Due to constraints related to user health privacy and meal scenario characteristics, the collection of data that includes both meal-course affiliation and two levels of interactions is impeded.
Scalable Model Editing via Customized Expert Networks
Addressing the issues of hallucinations and outdated knowledge in large language models is critical for their reliable application.
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images.
Voice EHR: Introducing Multimodal Audio Data for Health
Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection.
Joint Training on Multiple Datasets With Inconsistent Labeling Criteria for Facial Expression Recognition
In this study, we propose a joint training method for training an FER model using multiple FER datasets.
Ranked #8 on
Facial Expression Recognition (FER)
on RAF-DB
(using extra training data)
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification.
SSHPool: The Separated Subgraph-based Hierarchical Pooling
We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs.
A Comparative Study of Artificial Potential Fields and Safety Filters
In this paper, we have demonstrated that the controllers designed by a classical motion planning tool, namely artificial potential fields (APFs), can be derived from a recently prevalent approach: control barrier function quadratic program (CBF-QP) safety filters.
KunquDB: An Attempt for Speaker Verification in the Chinese Opera Scenario
This work aims to promote Chinese opera research in both musical and speech domains, with a primary focus on overcoming the data limitations.
A Tunable Universal Formula for Safety-Critical Control
Finally, we demonstrate the efficacy of our method through a collision avoidance example, investigating the essential properties including safety, robustness, and smoothness under various tunable scaling terms.
Unifying Controller Design for Stabilizing Nonlinear Systems with Norm-Bounded Control Inputs
This paper revisits a classical challenge in the design of stabilizing controllers for nonlinear systems with a norm-bounded input constraint.
Pyramid Feature Attention Network for Monocular Depth Prediction
Deep convolutional neural networks (DCNNs) have achieved great success in monocular depth estimation (MDE).
Location-guided Head Pose Estimation for Fisheye Image
Camera with a fisheye or ultra-wide lens covers a wide field of view that cannot be modeled by the perspective projection.
A Survey on Knowledge Distillation of Large Language Models
In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities from leading proprietary LLMs, such as GPT-4, to their open-source counterparts like LLaMA and Mistral.
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
To examine DEBATUNE, we curate the largest dataset of debate topics so far, which covers 710 controversial topics and corresponding arguments for each topic.
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
This paper introduces Selective Reflection-Tuning, a novel paradigm that synergizes a teacher LLM's reflection and introspection for improving existing data quality with the data selection capability of the student LLM, to automatically refine existing instruction-tuning data.
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.
Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales
Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data.
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process.
Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning
Large Language Models (LLMs) have shown significant promise in various applications, including zero-shot and few-shot learning.
Multi-Input Multi-Output Target-Speaker Voice Activity Detection For Unified, Flexible, and Robust Audio-Visual Speaker Diarization
The proposed method can take audio-visual input and leverage the speaker's acoustic footprint or lip track to flexibly conduct audio-based, video-based, and audio-visual speaker diarization in a unified sequence-to-sequence framework.
End-to-End Learning for SLP-Based ISAC Systems
Integrated sensing and communication (ISAC) is an encouraging wireless technology which can simultaneously perform both radar and communication functionalities by sharing the same transmit waveform, spectral resource, and hardware platform.
A Practical Beamforming Design for Active RIS-assisted MU-MISO Systems
Motivated by this fact, in this paper we first investigate the amplification principle of typical active RIS and propose a more accurate amplification model based on amplifier hardware characteristics.
Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning
Specifically, a teacher model continually refines pseudo labels through online clustering, providing dynamic supervision signals to train the student model.
IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment
This poses a paradox as the necessary camera pose must be estimated from the entire dataset, even though the data arrives sequentially and future chunks are inaccessible.
Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning
In our previous research, we developed an analytical control strategy, namely the universal formula, that incorporates CLF and CBF conditions for safe stabilization.
ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy
Specifically, to reconstruct the entire colon in a piecewise manner, our ColonNeRF introduces a region division and integration module, effectively reducing shape dissimilarity and ensuring geometric consistency in each segment.
LucidDreaming: Controllable Object-Centric 3D Generation
Specifically, our research demonstrates that Large Language Models (LLMs) possess 3D spatial awareness and can effectively translate textual 3D information into precise 3D bounding boxes.
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation
In medical image analysis, Federated Learning (FL) stands out as a key technology that enables privacy-preserved, decentralized data processing, crucial for handling sensitive medical data.