Search Results for author:
Found 32 papers, 11 papers with code
Detection of a Westward Hotspot Offset in the Atmosphere of a Hot Gas Giant CoRoT-2b
The peculiar infrared flux map of CoRoT-2b may result from westward winds due to non-synchronous rotation magnetic effects, or partial cloud coverage, that obscures the emergent flux from the planet's eastern hemisphere.
Earth and Planetary Astrophysics
A comprehensive review on convolutional neural network in machine fault diagnosis
To fill in this gap, this work attempts to review and summarize the development of the Convolutional Network based Fault Diagnosis (CNFD) approaches comprehensively.
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Moreover, through extensive experiments across SOTA MTL models, we have observed an interesting seesaw phenomenon that performance of one task is often improved by hurting the performance of some other tasks.
Semi-Federated Learning
Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server.
Weak Radio Frequency Signal Detection Based on Piezo-Opto-Electro-Mechanical System: Architecture Design and Sensitivity Prediction
We propose a novel radio-frequency (RF) receiving architecture based on micro-electro-mechanical system (MEMS) and optical coherent detection module.
Quantumized Microwave Detection Based on $Λ$-Type Three-level Superconducting System: HMM Modeling and Performance Prediction
Based on the state transition principles of the three-level system, we propose a statistical model for microwave signal detection.
Wireless Communication Based on Microwave Photon-Level Detection With Superconducting Devices: Achievable Rate Prediction
Future wireless communication system embraces physical-layer signal detection with high sensitivity, especially in the microwave photon level.
A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus
Identifying a short segment in a long video that semantically matches a text query is a challenging task that has important application potentials in language-based video search, browsing, and navigation.
Knowledge-Driven Machine Learning: Concept, Model and Case Study on Channel Estimation
The power of big data and machine learning has been drastically demonstrated in many fields during the past twenty years which somehow leads to the vague even false understanding that the huge amount of precious human knowledge accumulated to date no longer seems to matter.
On the Evaluation of Vision-and-Language Navigation Instructions
Vision-and-Language Navigation wayfinding agents can be enhanced by exploiting automatically generated navigation instructions.
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model
For the task of conversation emotion recognition, recent works focus on speaker relationship modeling but ignore the role of utterance's emotional tendency. In this paper, we propose a new expression paradigm of sentence-level emotion orientation vector to model the potential correlation of emotions between sentence vectors.
Generative adversarial network based single pixel imaging
Single pixel imaging can reconstruct two-dimensional images of a scene with only a single-pixel detector.
MIC: Model-agnostic Integrated Cross-channel Recommenders
However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions.
Adaptive Activation-based Structured Pruning
Pruning is a promising approach to compress complex deep learning models in order to deploy them on resource-constrained edge devices.
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer
The results show that 1) our model compression method can remove up to 99. 36% parameters of WRN-28-10, while preserving a Top-1 accuracy of over 90% on CIFAR-10; 2) our knowledge transfer method enables the compressed models to achieve more than 90% accuracy on CIFAR-10 and retain good accuracy on old categories; 3) it allows the compressed models to converge within real time (three to six minutes) on the edge for incremental learning tasks; 4) it enables the model to classify unseen categories of data (78. 92% Top-1 accuracy) that it is never trained with.
Iterative Activation-based Structured Pruning
To solve this problem, we propose two activation-based pruning methods, Iterative Activation-based Pruning (IAP) and Adaptive Iterative Activation-based Pruning (AIAP).
LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network
We present that using horizon-depth along with room height can obtain omnidirectional-geometry awareness of room layout in both horizontal and vertical directions.
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition
Emotion recognition in conversation (ERC) aims to analyze the speaker's state and identify their emotion in the conversation.
Ranked #4 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Classification
Emotion Recognition in Conversation
+1
Automatic Registration of Images with Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Removal
This new approach is designed to address the problems associated with the registration of images with affine deformations and inconsistent content, such as remote sensing images with different spectral content or noise interference, or map images with inconsistent annotations.
RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote Sensing Image Registration
In this paper, a robust feature point matching algorithm called Recovery and Filtering Vertex Trichotomy Matching (RFVTM) is proposed to remove outliers and retain sufficient inliers for remote sensing images.
Leveraging Reaction-aware Substructures for Retrosynthesis Analysis
In this paper, we propose a substructure-level decoding model, where the substructures are reaction-aware and can be automatically extracted with a fully data-driven approach.
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods.
RGB-X Classification for Electronics Sorting
Conventional recycling processes rely on shredding and sorting waste streams, but for WEEE, which is comprised of numerous dissimilar materials, we explore targeted disassembly of numerous objects for improved material recovery.
FeSAC: Federated Learning-Based Soft Actor-Critic Traffic Offloading in Space-Air-Ground Integrated Network
To further expand the resource pool of traffic offloading in SAGIN, we extend the single-layer satellite network into a double-layer satellite network composed of low-orbit satellites (LEO) and high-orbit satellites (GEO).
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning
Knowledge Transfer (KT) achieves competitive performance and is widely used for image classification tasks in model compression and transfer learning.
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions
Then, it proposes adaptive pruning policies for automatically meeting the pruning objectives of accuracy-critical, memory-constrained, and latency-sensitive tasks.
Confidence-based federated distillation for vision-based lane-centering
Machine learning-based steering angle prediction needs to consider the vehicle's limitation in uploading large amounts of potentially private data for model training.
UniSA: Unified Generative Framework for Sentiment Analysis
Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Bias of AI-Generated Content: An Examination of News Produced by Large Language Models
We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles.
Poster: Self-Supervised Quantization-Aware Knowledge Distillation
Quantization-aware training (QAT) starts with a pre-trained full-precision model and performs quantization during retraining.
LiDAR-based Outdoor Crowd Management for Smart Campus on the Edge
We deploy LiDAR sensors on light poles to collect data from the crowd on the campus and leverage edge accelerators to process data locally.
Self-Supervised Quantization-Aware Knowledge Distillation
Quantization-aware training (QAT) and Knowledge Distillation (KD) are combined to achieve competitive performance in creating low-bit deep learning models.
