Latest Research
DeepFork: Supervised Prediction of Information Diffusion in GitHub
To understand human influence, information spread and evolution of transmitted information among assorted users in GitHub, we developed a deep neural network model: DeepFork, a supervised machine learning based approach that aims to predict information diffusion in complex social networks; considering node as well as topological features.
Convolutional Character Networks
We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e. g., with improvements of 65. 33%->71. 08% (with generic lexicon) on ICDAR 2015, and 54. 0%->69. 23% on Total-Text, on end-to-end text recognition.
Dropping forward-backward algorithms for feature selection
In this era of big data, feature selection techniques, which have long been proven to simplify the model, makes the model more comprehensible, speed up the process of learning, have become more and more important.
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
• sorooshafiee/Optimistic_Likelihoods
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions.
Using a KG-Copy Network for Non-Goal Oriented Dialogues
• SmartDataAnalytics/KG-Copy_Network
Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts.
Electro-Magnetic Side-Channel Attack Through Learned Denoising and Classification
• opendenoising/interception_dataset
This paper proposes an upgraded electro-magnetic side-channel attack that automatically reconstructs the intercepted data.
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights.
Root Mean Square Layer Normalization
• bzhangGo/rmsnorm
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RMSNorm regularizes the summed inputs to a neuron in one layer according to root mean square (RMS), giving the model re-scaling invariance property and implicit learning rate adaptation ability.
LOST: A flexible framework for semi-automatic image annotation
In this paper we present a framework that allows for a quick and flexible design of semi-automatic annotation pipelines.
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
• s-huu/TurningWeaknessIntoStrength
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial images.