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Found 18 papers, 5 papers with code
Multi Sentence Description of Complex Manipulation Action Videos
Automatic video description requires the generation of natural language statements about the actions, events, and objects in the video.
A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos
To achieve this, we encode, first, the spatio-temporal inter dependencies between objects and actions with scene graphs and we combine this, in a second step, with a novel 3-level architecture creating a hierarchical attention mechanism using Graph Attention Networks (GATs).
On Continuity of Robust and Accurate Classifiers
To this end, we will introduce a framework for a rigorous study of harmonic and holomorphic hypothesis in learning theory terms and provide empirical evidence that continuous hypotheses does not perform as well as discontinuous hypotheses in some common machine learning tasks.
Identity-preserving Editing of Multiple Facial Attributes by Learning Global Edit Directions and Local Adjustments
Semantic facial attribute editing using pre-trained Generative Adversarial Networks (GANs) has attracted a great deal of attention and effort from researchers in recent years.
X-CapsNet For Fake News Detection
This model includes a CapsNet with dynamic routing algorithm paralyzed with a size-based classifier for detecting short and long fake news statements.
An Analytic Framework for Robust Training of Artificial Neural Networks
The reliability of a learning model is key to the successful deployment of machine learning in various industries.
Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME
On the other hand, in some tasks such as medical, economic, and self-driving cars, users want the model to be interpretable to decide if they can trust these results or not.
Modeling and Eliminating Adversarial Examples using Function Theory of Several Complex Variables
This paper presents a model and a solution for the existence and transfer of adversarial examples in analytic hypotheses.
Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks
Also, we extend and unify some of the other proposals in the literature and provide alternative explanations on the observations made in those proposals.
Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
We evaluate our method, referred to as Maximum Entropy Weighted Independent Set Pooling (MEWISPool), on graph classification tasks and the combinatorial optimization problem of the maximum independent set.
Ranked #2 on
Graph Classification
on FRANKENSTEIN
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules
The proposed model consists of an encoder which is a neural structure responsible for learning informative features from the input sequence, and a decoder which is a DRL model responsible for learning profitable strategies based on the features extracted by the encoder.
Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning
The effect of different input representations on the performance of the models is investigated and the performance of DRL-based models in different markets and asset situations is studied.
Detecting Fake News with Capsule Neural Networks
This paper aims to use capsule neural networks in the fake news detection task.
A Deep Decoder Structure Based on WordEmbedding Regression for An Encoder-Decoder Based Model for Image Captioning
The existing approaches are based on neural encoder-decoder structures equipped with the attention mechanism.
Novel Adaptive Genetic Algorithm Sample Consensus
In this paper, a new paradigm is proposed based on genetic algorithm with an adaptive strategy.
Robust Stereo Feature Descriptor for Visual Odometry
We use the 3D feature information to estimate the scale of each feature.
Exponential Natural Particle Filter
Particle Filter algorithm (PF) suffers from some problems such as the loss of particle diversity, the need for large number of particles, and the costly selection of the importance density functions.
