Search Results for author:
Found 11 papers, 2 papers with code
Body Fat Estimation from Surface Meshes using Graph Neural Networks
Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases.
Autonomic Architecture for Big Data Performance Optimization
This paper presents KERMIT - the autonomic architecture for big data capable of automatically tuning Apache Spark and Hadoop on-line, and achieving performance results 30% faster than rule-of-thumb tuning by a human administrator and up to 92% as fast as the fastest possible tuning established by performing an exhaustive search of the tuning parameter space.
Local Repair of Neural Networks Using Optimization
In this paper, we propose a framework to repair a pre-trained feed-forward neural network (NN) to satisfy a set of properties.
Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest
Due to their long-standing reputation as excellent off-the-shelf predictors, random forests continue remain a go-to model of choice for applied statisticians and data scientists.
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance
As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predictions with minimal a priori model specifications.
Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation
In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner.
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
Random forests remain among the most popular off-the-shelf supervised machine learning tools with a well-established track record of predictive accuracy in both regression and classification settings.
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks
We propose an implementation of GNN that predicts and imitates the motion be- haviors from observed swarm trajectory data.
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance
This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions.
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations.
HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities.
