Search Results for author:
Found 23 papers, 3 papers with code
Transferring SLU Models in Novel Domains
When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter whether the data is annotated or not.
Fusion of Infrared and Visible Images based on Spatial-Channel Attentional Mechanism
In the study, we present AMFusionNet, an innovative approach to infrared and visible image fusion (IVIF), harnessing the power of multiple kernel sizes and attention mechanisms.
Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving
Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception.
Neural Architecture Search for Intel Movidius VPU
Hardware-aware Neural Architecture Search (NAS) technologies have been proposed to automate and speed up model design to meet both quality and inference efficiency requirements on a given hardware.
Hardware Aware Neural Architecture Search
Neural Architecture Search
+1
A Survey on Vertical Federated Learning: From a Layered Perspective
From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL.
Spatio-Temporal Point Process for Multiple Object Tracking
As such, we propose a novel framework that can effectively predict and mask-out the noisy and confusing detection results before associating the objects into trajectories.
WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning
The experimental results demonstrate that WrapperFL can be successfully applied to a wide range of applications under practical settings and improves the local model with federated learning at a low cost.
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network connection of things, leading to an advanced AI-driven ecology.
Uncertainty-based Network for Few-shot Image Classification
Specifically, we first data augment and classify the query instance and calculate the mutual information of these classification scores.
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning
Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry.
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN
However, it is challenging for GANs to model distributions of separate non-i. i. d.
Learning Class-level Prototypes for Few-shot Learning
Few-shot learning aims to recognize new categories using very few labeled samples.
Deep Generative Learning via Schrödinger Bridge
At the sample level, we derive our Schr\"{o}dinger Bridge algorithm by plugging the drift term estimated by a deep score estimator and a deep density ratio estimator into the Euler-Maruyama method.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning
This motivates us to use a multiplex structure in a diverse way and utilize a priori properties of graphs to guide the learning.
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech
With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition
Modern Automatic Speech Recognition (ASR) systems primarily rely on scores from an Acoustic Model (AM) and a Language Model (LM) to rescore the N-best lists.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images
Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD.
Federated Deep Reinforcement Learning
In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited.
Neural network state estimation for full quantum state tomography
An efficient state estimation model, neural network estimation (NNE), empowered by machine learning techniques, is presented for full quantum state tomography (FQST).
Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volume
We apply a Long Short-Term Memory neural network to forecast CSI300 volatility using those search volume data.
Probabilistic Multi-Task Feature Selection
In this paper, we unify the $l_{1, 2}$ and $l_{1,\infty}$ norms by considering a family of $l_{1, q}$ norms for $1 < q\le\infty$ and study the problem of determining the most appropriate sparsity enforcing norm to use in the context of multi-task feature selection.
