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Found 7 papers, 1 papers with code
SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture
To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN.
MaxMin-L2-SVC-NCH: A Novel Approach for Support Vector Classifier Training and Parameter Selection
We first formulate the training and parameter selection of SVC as a minimax optimization problem named as MaxMin-L2-SVC-NCH, in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal Gaussian kernel parameters.
Automatic Sparse Connectivity Learning for Neural Networks
In this work, we propose a new automatic pruning method - Sparse Connectivity Learning (SCL).
Training convolutional neural networks with cheap convolutions and online distillation
The large memory and computation consumption in convolutional neural networks (CNNs) has been one of the main barriers for deploying them on resource-limited systems.
EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification
This paper describes our approach to the EmotionX-2019, the shared task of SocialNLP 2019.
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues
The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification.
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogue
The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification.
