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Found 21 papers, 5 papers with code
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion.
Enhance the performance of navigation: A two-stage machine learning approach
We model the traffic flow as a time series and apply XGBoost algorithm to get accurate predictions on future traffic conditions(1st stage).
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares.
Socially-Optimal Mechanism Design for Incentivized Online Learning
Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment.
Graph Neural Networks in IoT: A Survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on.
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning
Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be generalized to different environments during deployment.
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning
Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data.
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work.
Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline
To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety.
Diversifying the High-level Features for better Adversarial Transferability
Incorporated into the input transformation-based attacks, DHF generates more transferable adversarial examples and outperforms the baselines with a clear margin when attacking several defense models, showing its generalization to various attacks and high effectiveness for boosting transferability.
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation
Then, we build a pure-MLP architecture called Caterpillar by replacing the convolutional layer with the SPC module in a hybrid model of sMLPNet.
LPN: Language-guided Prototypical Network for few-shot classification
Few-shot classification aims to adapt to new tasks with limited labeled examples.
DiffuseGAE: Controllable and High-fidelity Image Manipulation from Disentangled Representation
To further explore the latent space of Diff-AE and achieve a generic editing pipeline, we proposed a module called Group-supervised AutoEncoder(dubbed GAE) for Diff-AE to achieve better disentanglement on the latent code.
Efficient Semi-Supervised Federated Learning for Heterogeneous Participants
Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data.
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction
Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making.
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
However, the application of tensor decomposition is largely hindered by the exponential increment of the computational complexity and storage consumption with the size of tensors.
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs.
Ranked #1 on
Fine-Grained Urban Flow Inference
on TaxiBJ-P4
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses
In this paper, we investigate the retrieval-augmented generation (RAG) based on Knowledge Graphs (KGs) to improve the accuracy and reliability of Large Language Models (LLMs).
P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer
Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance degradation when these agents face new tasks.
INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning
This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting.
Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond
Uncertainty estimation plays a pivotal role in ensuring the reliability of safety-critical human-AI interaction systems, particularly in the medical domain.
