Search Results for author:
Found 32 papers, 3 papers with code
All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
Therefore, such a combination of the pruning decisions and the finetuned weights may be suboptimal, leading to non-negligible performance degradation.
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
Our approach conceptualizes domain knowledge as natural language and introduces a specialized multimodal GNN capable of leveraging this uncurated knowledge to guide the learning process of the GNN, such that it can improve the model performance and strengthen the interpretability of the predictions.
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation.
Cross-Dataset Gaze Estimation by Evidential Inter-intra Fusion
To address the problem of ``cross-dataset gaze estimation'', we propose a novel Evidential Inter-intra Fusion EIF framework, for training a cross-dataset model that performs well across all source and unseen domains.
PCIE_LAM Solution for Ego4D Looking At Me Challenge
This report presents our team's 'PCIE_LAM' solution for the Ego4D Looking At Me Challenge at CVPR2024.
PCIE_EgoHandPose Solution for EgoExo4D Hand Pose Challenge
Our approach achieved the 1st position in the Hand Pose challenge with 25. 51 MPJPE and 8. 49 PA-MPJPE.
PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud
Experimental results on simulation and the actual IBM quantum computer both prove the ability of PristiQ to provide high security for the quantum data while maintaining the model performance in QML.
VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection
In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features.
Technical Report for Argoverse Challenges on Unified Sensor-based Detection, Tracking, and Forecasting
We achieved 1st place in Detection, Tracking, and Forecasting on the E2E Forecasting track in Argoverse Challenges at CVPR 2023 WAD.
Technical Report for Argoverse Challenges on 4D Occupancy Forecasting
This report presents our Le3DE2E_Occ solution for 4D Occupancy Forecasting in Argoverse Challenges at CVPR 2023 Workshop on Autonomous Driving (WAD).
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV).
Ranked #1 on
Semantic Segmentation
on LaRS
Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devices
Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring subsurface structures from seismic data.
ReIDTrack: Multi-Object Track and Segmentation Without Motion
Association techniques mainly depend on the combination of motion and appearance information.
A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices
The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers.
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model
We further developed a reinforcement learning-based security engine, which can automatically optimize the model design under the distributed setting, such that a good trade-off between model performance and security can be made.
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
Self-supervised learning (SSL) methods, contrastive learning (CL) and masked autoencoders (MAE), can leverage the unlabeled data to pre-train models, followed by fine-tuning with limited labels.
Distributed Contrastive Learning for Medical Image Segmentation
However, when adopting CL in FL, the limited data diversity on each site makes federated contrastive learning (FCL) ineffective.
Quantum Neural Network Compression
Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices.
Federated Contrastive Learning for Volumetric Medical Image Segmentation
However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective.
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning
To tackle this problem, we propose a data generation framework with two methods to improve CL training by joint sample generation and contrastive learning.
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning
The recently developed self-supervised learning approach, contrastive learning (CL), can leverage the unlabeled data to pre-train a model, after which the model is fine-tuned on limited labeled data for dermatological disease diagnosis.
Decentralized Unsupervised Learning of Visual Representations
To tackle this problem, we propose a collaborative contrastive learning framework consisting of two approaches: feature fusion and neighborhood matching, by which a unified feature space among clients is learned for better data representations.
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching
Federated learning (FL) enables distributed clients to learn a shared model for prediction while keeping the training data local on each client.
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation
In this way, the main model learns to cluster hard positives by pulling the representations of similar yet distinct samples together, by which the representations of similar samples are well-clustered and better representations can be learned.
Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow
Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise.
Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs
Experimental results demonstrate that the identified quantum neural architectures with mixed quantum neurons can achieve 90. 62% of accuracy on the MNIST dataset, compared with 52. 77% and 69. 92% on the VQC and QuantumFlow, respectively.
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast
After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy.
Enabling Efficient On-Device Self-supervised Contrastive Learning by Data Selection
In this paper, we propose a framework to automatically select the most representative data from unlabeled input stream on-the-fly, which only requires the use of a small data buffer for dynamic learning.
Personalized Deep Learning for Ventricular Arrhythmias Detection on Medical IoT Systems
We equip the system with real-time inference on both intracardiac and surface rhythm monitors.
Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning
For example, when training ResNet-110 on CIFAR-10, we achieve 68% computation saving while preserving full accuracy and 75% computation saving with a marginal accuracy loss of 1. 3%.
Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devices
This work aims to enable persistent, event-driven sensing and decision capabilities for energy-harvesting (EH)-powered devices by deploying lightweight DNNs onto EH-powered devices.
Omnidirectional Scene Text Detection with Sequential-free Box Discretization
Scene text in the wild is commonly presented with high variant characteristics.
Ranked #1 on
Scene Text Detection
on IC19-ReCTs
(using extra training data)
