Search Results for author: Abhishek Panigrahi

Found 6 papers, 0 papers with code

Learning and Generalization in RNNs

no code implementations NeurIPS 2021 Abhishek Panigrahi, Navin Goyal

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

no code implementations21 Oct 2019 Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli

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

no code implementations ICLR 2020 Abhishek Panigrahi, Abhishek Shetty, Navin Goyal

In the present paper, we provide theoretical results about the effect of activation function on the training of highly overparametrized 2-layer neural networks.

Small Data Image Classification

Word2Sense: Sparse Interpretable Word Embeddings

no code implementations ACL 2019 Abhishek Panigrahi, Harsha Vardhan Simhadri, Chiranjib Bhattacharyya

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.

Word Embeddings Word Similarity

DeepTagRec: A Content-cum-User based Tag Recommendation Framework for Stack Overflow

no code implementations10 Mar 2019 Suman Kalyan Maity, Abhishek Panigrahi, Sayan Ghosh, Arundhati Banerjee, Pawan Goyal, Animesh Mukherjee

In this paper, we develop a content-cum-user based deep learning framework DeepTagRec to recommend appropriate question tags on Stack Overflow.

TAG

Analysis on Gradient Propagation in Batch Normalized Residual Networks

no code implementations ICLR 2018 Abhishek Panigrahi, Yueru Chen, C. -C. Jay Kuo

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.

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