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Found 9 papers, 2 papers with code
Decentralized Langevin Dynamics for Bayesian Learning
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting.
A Decentralized Approach to Bayesian Learning
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting.
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
Our results show that the selective relevance method can not only provide insight on the role played by motion in the model's decision -- in effect, revealing and quantifying the model's spatial bias -- but the method also simplifies the resulting explanations for human consumption.
Sanity Checks for Saliency Metrics
Despite a proliferation of such methods, little effort has been made to quantify how good these saliency maps are at capturing the true relevance of the pixels to the classifier output (i. e. their "fidelity").
Wasserstein Distance Based Domain Adaptation for Object Detection
Prior approaches utilize adversarial training based on cross entropy between the source and target domain distributions to learn a shared feature mapping that minimizes the domain gap.
Distributed Deep Learning with Event-Triggered Communication
We develop a Distributed Event-Triggered Stochastic GRAdient Descent (DETSGRAD) algorithm for solving non-convex optimization problems typically encountered in distributed deep learning.
Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning
Numerical results also show that the proposed distributed algorithm allows the individual agents to recognize the digits even though the training data corresponding to all the digits is not locally available to each agent.
Optimization and Control Systems and Control Systems and Control
Human Action Attribute Learning From Video Data Using Low-Rank Representations
Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization.
Optimal Sparse Kernel Learning for Hyperspectral Anomaly Detection
In this paper, a novel framework of sparse kernel learning for Support Vector Data Description (SVDD) based anomaly detection is presented.
