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Found 20 papers, 7 papers with code
A Parallel Attention Network for Cattle Face Recognition
Thus, we create the first large-scale cattle face recognition dataset, ICRWE, for wild environments.
H2RSVLM: Towards Helpful and Honest Remote Sensing Large Vision Language Model
The generic large Vision-Language Models (VLMs) is rapidly developing, but still perform poorly in Remote Sensing (RS) domain, which is due to the unique and specialized nature of RS imagery and the comparatively limited spatial perception of current VLMs.
Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
In CUT, user similarity in the target domain is adopted as a constraint for user transformation learning to filter the user collaborative information from the source domain.
A Situation-aware Enhancer for Personalized Recommendation
In this paper, we provide a new perspective that takes situations as the preconditions for users' interactions.
Recommender for Its Purpose: Repeat and Exploration in Food Delivery Recommendations
This paper emphasizes the importance of dedicated analyses and methods for domain-specific characteristics for the recommender system studies.
IRG: Generating Synthetic Relational Databases using GANs
The key idea is to construct a context, based on the tables generated so far, when using a GAN to generate the next table.
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
The surge in prompt-based learning within Natural Language Processing (NLP) suggests the potential of adapting a "pre-train, prompt" paradigm to graphs as an alternative.
LEFormer: A Hybrid CNN-Transformer Architecture for Accurate Lake Extraction from Remote Sensing Imagery
LEFormer contains three main modules: CNN encoder, Transformer encoder, and cross-encoder fusion.
CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis
We have shared our cloud platform at MRSHub, providing free access and service for two years.
Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal
Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification.
Intent-aware Ranking Ensemble for Personalized Recommendation
To address such a task, we propose an Intent-aware ranking Ensemble Learning~(IntEL) model to fuse multiple single-objective item lists with various user intents, in which item-level personalized weights are learned.
Linguistic-style-aware Neural Networks for Fake News Detection
We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal.
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).
Rank Position Forecasting in Car Racing
Forecasting is challenging since uncertainty resulted from exogenous factors exists.
Secure Multigroup Multicast Communication Systems via Intelligent Reflecting Surface
This paper considers a secure multigroup multicast multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS).
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers
The first part of ADMM-NN is a systematic, joint framework of DNN weight pruning and quantization using ADMM.
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM
Both DNN weight pruning and clustering/quantization, as well as their combinations, can be solved in a unified manner.
Progressive Weight Pruning of Deep Neural Networks using ADMM
Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates.
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
For FPGA implementations on deep convolutional neural networks (DCNNs), we achieve at least 152X and 72X improvement in performance and energy efficiency, respectively using the SWM-based framework, compared with the baseline of IBM TrueNorth processor under same accuracy constraints using the data set of MNIST, SVHN, and CIFAR-10.
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks
Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity.
