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Found 10 papers, 1 papers with code
iPAL: A Machine Learning Based Smart Healthcare Framework For Automatic Diagnosis Of Attention Deficit/Hyperactivity Disorder (ADHD)
In this work, multiclass classification is performed on phenotypic data using an SVM model.
Dropout Inference with Non-Uniform Weight Scaling
Dropout as regularization has been used extensively to prevent overfitting for training neural networks.
Design Rule Checking with a CNN Based Feature Extractor
Using this solution, we can detect multiple DRC violations 32x faster than Boolean checkers with an accuracy of up to 92.
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games.
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
In this work, we focus on adapting the representations learned by segmentation networks across synthetic and real domains.
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
Regularizing deep networks using efficient layerwise adversarial training
In this paper, we present an efficient approach to perform adversarial training by perturbing intermediate layer activations and study the use of such perturbations as a regularizer during training.
Self corrective Perturbations for Semantic Segmentation and Classification
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
Manhattan Junction Catalogue for Spatial Reasoning of Indoor Scenes
In this paper, we consider the problem of detecting junctions and using them for recovering the spatial layout of an indoor scene.
Representing Videos Using Mid-level Discriminative Patches
representation for videos based on mid-level discriminative spatio-temporal patches.
