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Found 16 papers, 4 papers with code
EdgeFL: A Lightweight Decentralized Federated Learning Framework
By leveraging EdgeFL, software engineers can harness the benefits of federated learning while overcoming the challenges associated with existing FL platforms/frameworks.
5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul
Fast and reliable wireless communication has become a critical demand in human life.
Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning
A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection.
Context-Aware Legal Citation Recommendation using Deep Learning
Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions.
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments.
Real-time End-to-End Federated Learning: An Automotive Case Study
With the development and the increasing interests in ML/DL fields, companies are eager to apply Machine Learning/Deep Learning approaches to increase service quality and customer experience.
Label Leakage and Protection in Two-party Split Learning
Two-party split learning is a popular technique for learning a model across feature-partitioned data.
End-to-End on-device Federated Learning: A case study
Because of its characteristics such as model-only exchange and parallel training, the technique can not only preserve user data privacy but also accelerate model training speed.
Fixup Initialization: Residual Learning Without Normalization
Normalization layers are a staple in state-of-the-art deep neural network architectures.
Ranked #9 on
Image Classification
on SVHN
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate
We study smooth stochastic optimization problems on Riemannian manifolds.
Towards Riemannian Accelerated Gradient Methods
We propose a Riemannian version of Nesterov's Accelerated Gradient algorithm (RAGD), and show that for geodesically smooth and strongly convex problems, within a neighborhood of the minimizer whose radius depends on the condition number as well as the sectional curvature of the manifold, RAGD converges to the minimizer with acceleration.
An Approximate Shading Model with Detail Decomposition for Object Relighting
We present an object relighting system that allows an artist to select an object from an image and insert it into a target scene.
mixup: Beyond Empirical Risk Minimization
We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Matrix Completion from $O(n)$ Samples in Linear Time
Under a certain incoherence assumption on $M$ and for the case when both the rank and the condition number of $M$ are bounded, it was shown in \cite{CandesRecht2009, CandesTao2010, keshavan2010, Recht2011, Jain2012, Hardt2014} that $M$ can be recovered exactly or approximately (depending on some trade-off between accuracy and computational complexity) using $O(n \, \text{poly}(\log n))$ samples in super-linear time $O(n^{a} \, \text{poly}(\log n))$ for some constant $a \geq 1$.
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds
We study optimization of finite sums of geodesically smooth functions on Riemannian manifolds.
First-order Methods for Geodesically Convex Optimization
Geodesic convexity generalizes the notion of (vector space) convexity to nonlinear metric spaces.
