Search Results for author:
Found 9 papers, 3 papers with code
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
This paper presents a framework of successive functional gradient optimization for training nonconvex models such as neural networks, where training is driven by mirror descent in a function space.
Composite Functional Gradient Learning of Generative Adversarial Models
This paper first presents a theory for generative adversarial methods that does not rely on the traditional minimax formulation.
Deep Pyramid Convolutional Neural Networks for Text Categorization
This paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that can efficiently represent long-range associations in text.
Ranked #2 on
Sentiment Analysis
on Amazon Review Full
Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level
This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016).
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings
The best results were obtained by combining region embeddings in the form of LSTM and convolution layers trained on unlabeled data.
Ranked #1 on
Text Classification
on RCV1
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization.
Ranked #1000000000 on
Text Classification
on IMDb
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data.
Ranked #29 on
Sentiment Analysis
on IMDb
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
Stochastic gradient descent is popular for large scale optimization but has slow convergence asymptotically due to the inherent variance.
Learning Nonlinear Functions Using Regularized Greedy Forest
We consider the problem of learning a forest of nonlinear decision rules with general loss functions.
