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Found 6 papers, 0 papers with code
Learning and Generalization in RNNs
In contrast to the previous work that could only deal with functions of sequences that are sums of functions of individual tokens in the sequence, we allow general functions.
Non-Gaussianity of Stochastic Gradient Noise
What enables Stochastic Gradient Descent (SGD) to achieve better generalization than Gradient Descent (GD) in Neural Network training?
Effect of Activation Functions on the Training of Overparametrized Neural Nets
In the present paper, we provide theoretical results about the effect of activation function on the training of highly overparametrized 2-layer neural networks.
Word2Sense: Sparse Interpretable Word Embeddings
We present an unsupervised method to generate Word2Sense word embeddings that are interpretable {---} each dimension of the embedding space corresponds to a fine-grained sense, and the non-negative value of the embedding along the j-th dimension represents the relevance of the j-th sense to the word.
DeepTagRec: A Content-cum-User based Tag Recommendation Framework for Stack Overflow
In this paper, we develop a content-cum-user based deep learning framework DeepTagRec to recommend appropriate question tags on Stack Overflow.
Analysis on Gradient Propagation in Batch Normalized Residual Networks
We conduct mathematical analysis on the effect of batch normalization (BN) on gradient backpropogation in residual network training, which is believed to play a critical role in addressing the gradient vanishing/explosion problem, in this work.
