Search Results for author:
Found 47 papers, 17 papers with code
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning
To address this challenge, we propose ABG-NAS, a novel framework for automated graph neural network architecture search tailored for efficient graph representation learning.
StableGS: A Floater-Free Framework for 3D Gaussian Splatting
Recent years have witnessed remarkable success of 3D Gaussian Splatting (3DGS) in novel view synthesis, surpassing prior differentiable rendering methods in both quality and efficiency.
DeskVision: Large Scale Desktop Region Captioning for Advanced GUI Agents
Using AutoCaptioner, we created a novel large-scale desktop GUI dataset, DeskVision, along with the largest desktop test benchmark, DeskVision-Eval, which reflects daily usage and covers diverse systems and UI elements, each with rich descriptions.
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models
Inspired by the chain-of-thought (CoT) paradigm in modern LLMs, SEKI operates in two key stages: self-evolution and knowledge distillation.
Transfer Learning Assisted Fast Design Migration Over Technology Nodes: A Study on Transformer Matching Network
With the source data only at 30GHz, the experiments span target data from two metal options in GF 22FDX+ at frequencies of 30 and 39 GHz.
Round Attention: A Novel Round-Level Attention Mechanism to Accelerate LLM Inference
The increasing context window size in large language models (LLMs) has improved their ability to handle complex, long-text tasks.
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
We investigate the construction of gradient-guided conditional diffusion models for reconstructing private images, focusing on the adversarial interplay between differential privacy noise and the denoising capabilities of diffusion models.
TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text
To reduce the computation overhead as well as TTFT, we introduce TurboRAG, a novel RAG system that redesigns the inference paradigm of the current RAG system by first pre-computing and storing the key-value (KV) caches of documents offline, and then directly retrieving the saved KV cache for prefill.
MooER: LLM-based Speech Recognition and Translation Models from Moore Threads
We achieve performance comparable to other open source models trained with up to hundreds of thousands of hours of labeled speech data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation
This paper presents a comprehensive survey of the most prevalent distance-based vessel trajectory clustering methods, which encompass two main steps: trajectory similarity measurement and clustering.
Improving Large Models with Small models: Lower Costs and Better Performance
On the other hand, certain tasks can be broken down into multiple subtasks, some of which can be completed without powerful capabilities.
Exploring Key Factors for Long-Term Vessel Incident Risk Prediction
Most previous studies conduct factor analysis within the framework of incident-related label prediction, where the developed models can be categorized into short-term and long-term prediction models.
A Survey on the Real Power of ChatGPT
ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT.
GPT, Ontology, and CAABAC: A Tripartite Personalized Access Control Model Anchored by Compliance, Context and Attribute
As digital healthcare evolves, the security of electronic health records (EHR) becomes increasingly crucial.
Uncertainty and Explainable Analysis of Machine Learning Model for Reconstruction of Sonic Slowness Logs
Using the SHAP explainable machine learning model, we calculate the importance of each input log to the predicted results as well as the coupling relationship among input logs.
DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework
In our paper, we introduce DP-HyPO, a pioneering framework for ``adaptive'' private hyperparameter optimization, aiming to bridge the gap between private and non-private hyperparameter optimization.
A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection
The "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model.
Deep-Learning-Based Computer Vision Approach For The Segmentation Of Ball Deliveries And Tracking In Cricket
Our research tries to solve one of these problems by segmenting ball deliveries in a cricket broadcast using deep learning models, MobileNet and YOLO, thus enabling researchers to use our work as a dataset for their research.
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction
Specifically, we first exploit network schema as the prior constraint to sample negatives and pre-train our model by employing a multi-level contrastive learning method to yield both prior schema and contextual information.
Analytical Composition of Differential Privacy via the Edgeworth Accountant
Many modern machine learning algorithms are composed of simple private algorithms; thus, an increasingly important problem is to efficiently compute the overall privacy loss under composition.
Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network
This paper presents a deep learning assisted synthesis approach for direct end-to-end generation of RF/mm-wave passive matching network with 3D EM structures.
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
Our main finding uncovers a sharp phase transition phenomenon regarding the {intra-class impact: if the SDEs are locally elastic in the sense that the impact is more significant on samples from the same class as the input, the features of the training data become linearly separable, meaning vanishing training loss; otherwise, the features are not separable, regardless of how long the training time is.
On the Convergence and Calibration of Deep Learning with Differential Privacy
Differentially private (DP) training preserves the data privacy usually at the cost of slower convergence (and thus lower accuracy), as well as more severe mis-calibration than its non-private counterpart.
Exact Sparse Orthogonal Dictionary Learning
Over the past decade, learning a dictionary from input images for sparse modeling has been one of the topics which receive most research attention in image processing and compressed sensing.
Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks
According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint.
Information Theory Signal Processing Information Theory
Local-Global Fusion Network for Video Super-Resolution
The results on benchmark datasets of our LGFN are presented on https://github. com/BIOINSu/LGFN and the source code will be released as soon as the paper is accepted.
Ranked #4 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers
We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits.
The Complete Lasso Tradeoff Diagram
A fundamental problem in the high-dimensional regression is to understand the tradeoff between type I and type II errors or, equivalently, false discovery rate (FDR) and power in variable selection.
Statistics Theory Information Theory Information Theory Statistics Theory
Exploiting Review Neighbors for Contextualized Helpfulness Prediction
Four weighting schemes are designed to learn context clues from the selected neighbors.
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.
Reversible Adversarial Attack based on Reversible Image Transformation
In order to prevent illegal or unauthorized access of image data such as human faces and ensure legitimate users can use authorization-protected data, reversible adversarial attack technique is rise.
Longitudinal Enrichment of Imaging Biomarker Representations for Improved Alzheimer's Disease Diagnosis
Longitudinal data is often available inconsistently across individuals resulting in ignoring of additionally available data.
Deformable Non-local Network for Video Super-Resolution
The video super-resolution (VSR) task aims to restore a high-resolution (HR) video frame by using its corresponding low-resolution (LR) frame and multiple neighboring frames.
Robust Linear Discriminant Analysis Using Ratio Minimization of L1,2-Norms
In this paper, we propose a novel robust linear discriminant analysis method based on the L1, 2-norm ratio minimization.
An Efficient Pre-processing Method to Eliminate Adversarial Effects
Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated by imposing subtle perturbations to inputs that lead a model to predict incorrect outputs.
Spherical Principal Component Analysis
However, most of the studies on PCA aim to minimize the loss after projection, which usually measures the Euclidean distance, though in some fields, angle distance is known to be more important and critical for analysis.
Deep attention-guided fusion network for lesion segmentation
We participated the Task 1: Lesion Segmentation.
Neural Sparse Topical Coding
Topic models with sparsity enhancement have been proven to be effective at learning discriminative and coherent latent topics of short texts, which is critical to many scientific and engineering applications.
Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l1-Norm Distances
To tackle these two challenges, in this paper we propose a novel image representation learning method that can integrate the local patches (the instances) of an input image (the bag) and its holistic representation into one single-vector representation.
Deep Residual Text Detection Network for Scene Text
Scene text detection is a challenging problem in computer vision.
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images.
Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks
Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a potential application in digital entertainment.
Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams
Uncertain data streams have been widely generated in many Web applications.
Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking
Sparse representation has been widely studied in visual tracking, which has shown promising tracking performance.
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine
We applied our SMML method to five broadly used object categorization and scene understanding image data sets for both singlelabel and multi-label image classification tasks.
High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction
Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions.
