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Found 21 papers, 11 papers with code
Horizontal and Vertical Ensemble with Deep Representation for Classification
Representation learning, especially which by using deep learning, has been widely applied in classification.
Challenges in Representation Learning: A report on three machine learning contests
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Ranked #12 on
Facial Expression Recognition (FER)
on FER2013
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.
Generative Adversarial Networks
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
Super-Resolution
Time-Series Few-Shot Learning with Heterogeneous Channels
Generative Adversarial Nets
We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
Empirical Evaluation of Rectified Activations in Convolutional Network
In this paper we investigate the performance of different types of rectified activation functions in convolutional neural network: standard rectified linear unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU).
Ranked #190 on
Image Classification
on CIFAR-100
Learning with a Strong Adversary
The robustness of neural networks to intended perturbations has recently attracted significant attention.
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.
Revise Saturated Activation Functions
In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh).
Training Deep Nets with Sublinear Memory Cost
In the extreme case, our analysis also shows that the memory consumption can be reduced to O(log n) with as little as O(n log n) extra cost for forward computation.
MI\&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification
A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented.
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies.
Ranked #147 on
Action Classification
on Kinetics-400
CN-HIT-MI.T at SemEval-2019 Task 6: Offensive Language Identification Based on BiLSTM with Double Attention
Our macro-averaged F1-score in sub-task A is 0. 768, ranking 28/103.
Biometric Blockchain: A Better Solution for the Security and Trust of Food Logistics
The advantage of using BBC in the food logistics is clear: it can not only identify if the data or labels are authentic, but also clearly record who is responsible for the secured data or labels.
Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition
We propose a new building block, IdleBlock, which naturally prunes connections within the block.
CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT
Internet memes emotion recognition is focused by many researchers.
Multi-Grained Knowledge Distillation for Named Entity Recognition
To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning. To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts.
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection
Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data.
Robust Causal Graph Representation Learning against Confounding Effects
This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence.
CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue
In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).
HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL Detection
Respectively, for subtask 1, that is, to judge whether a sentence is PCL, the method of retraining the model with specific task data is adopted, and the method of splicing [CLS] and the keyword representation of the last three layers as the representation of the sentence; for subtask 2, that is, to judge the PCL type of the sentence, in addition to using the same method as task1, the method of selecting a special loss for Multi-label text classification is applied.
Multi Label Text Classification
Multi-Label Text Classification
+2
