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Found 19 papers, 11 papers with code
CBNN: 3-Party Secure Framework for Customized Binary Neural Networks Inference
Binarized Neural Networks (BNN) offer efficient implementations for machine learning tasks and facilitate Privacy-Preserving Machine Learning (PPML) by simplifying operations with binary values.
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving
In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to represent driving-specific scenarios accurately, particularly in complex interactions and long-tail cases.
Single-cell Curriculum Learning-based Deep Graph Embedding Clustering
The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity.
Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection
The Bird's-Eye-View (BEV) representation is a critical factor that directly impacts the 3D object detection performance, but the traditional BEV grid representation induces quadratic computational cost as the spatial resolution grows.
Differentially Private Federated Learning: A Systematic Review
Our work presents a systematic overview of the differentially private federated learning.
Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving
We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving.
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release
Motivated by this, this paper proposes DPSUR, a Differentially Private training framework based on Selective Updates and Release, where the gradient from each iteration is evaluated based on a validation test, and only those updates leading to convergence are applied to the model.
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
Unlike existing end-to-end autonomous driving frameworks, PPAD models the interactions among ego, agents, and the dynamic environment in an autoregressive manner by interleaving the Prediction and Planning processes at every timestep, instead of a single sequential process of prediction followed by planning.
DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression.
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data.
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise
The heterogeneity and convergence of training parameters were simply not considered.
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing
Differentially private stochastic gradient descent (DPSGD) is the most popular training method with differential privacy in image recognition.
Point Cloud Compression with Sibling Context and Surface Priors
We present a novel octree-based multi-level framework for large-scale point cloud compression, which can organize sparse and unstructured point clouds in a memory-efficient way.
HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures
We present HoliCity, a city-scale 3D dataset with rich structural information.
Intuitive, Interactive Beard and Hair Synthesis with Generative Models
We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects.
Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition
In this work, we propose a new solution to 3D human pose estimation in videos.
Ranked #13 on
Monocular 3D Human Pose Estimation
on Human3.6M
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image
In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities.
Probabilistic Matrix Factorization with Personalized Differential Privacy
The results show that the PDP-PMF scheme performs well on protecting the privacy of each user and its recommendation quality is much better than the DP-PMF scheme.
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Doodling is a useful and common intelligent skill that people can learn and master.
