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no code implementations • 18 Oct 2023 • Yuanzhi Li, Raghu Meka, Rina Panigrahy, Kulin Shah

Deep networks typically learn concepts via classifiers, which involves setting up a model and training it via gradient descent to fit the concept-labeled data.

no code implementations • 31 Jan 2023 • Cenk Baykal, Dylan J Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang

One way of introducing sparsity into deep networks is by attaching an external table of parameters that is sparsely looked up at different layers of the network.

no code implementations • 30 Jan 2023 • Cenk Baykal, Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang

It has been well established that increasing scale in deep transformer networks leads to improved quality and performance.

no code implementations • 8 Aug 2022 • Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang

After representing LSH-based sparse networks with our model, we prove that sparse networks can match the approximation power of dense networks on Lipschitz functions.

no code implementations • 13 Apr 2022 • Shaojin Ding, Weiran Wang, Ding Zhao, Tara N. Sainath, Yanzhang He, Robert David, Rami Botros, Xin Wang, Rina Panigrahy, Qiao Liang, Dongseong Hwang, Ian McGraw, Rohit Prabhavalkar, Trevor Strohman

In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios.

Automatic Speech Recognition
Automatic Speech Recognition (ASR)
**+1**

no code implementations • 29 Sep 2021 • Rina Panigrahy, Brendan Juba, Zihao Deng, Xin Wang, Zee Fryer

We propose a modular architecture for lifelong learning of hierarchically structured tasks.

no code implementations • ICLR 2021 • Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang

Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different?

no code implementations • 11 Mar 2021 • Nishanth Dikkala, Gal Kaplun, Rina Panigrahy

We provide theoretical and empirical evidence that neural representations can be viewed as LSH-like functions that map each input to an embedding that is a function of solely the informative $\gamma$ and invariant to $\theta$, effectively recovering the manifold identifier $\gamma$.

no code implementations • 15 May 2020 • Atish Agarwala, Abhimanyu Das, Rina Panigrahy, Qiuyi Zhang

We present experimental evidence that the many-body gravitational force function is easier to learn with ReLU networks as compared to networks with exponential activations.

no code implementations • 3 Oct 2019 • Rina Panigrahy

How we store information in our mind has been a major intriguing open question.

no code implementations • 29 May 2019 • Badih Ghazi, Rina Panigrahy, Joshua R. Wang

The sketch summarizes essential information about the inputs and outputs of the network and can be used to quickly identify key components and summary statistics of the inputs.

no code implementations • 8 Apr 2019 • Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy

In this paper we study the learnability of deep random networks from both theoretical and practical points of view.

no code implementations • ICLR 2019 • Surbhi Goel, Rina Panigrahy

Giving provable guarantees for learning neural networks is a core challenge of machine learning theory.

no code implementations • ICML 2017 • Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff

We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise $\ell_p$-approximation error, for any $p \geq 1$; the case $p = 2$ is the classical SVD problem.

no code implementations • 1 Feb 2017 • Rina Panigrahy, Sushant Sachdeva, Qiuyi Zhang

Iterating, we show that gradient descent can be used to learn the entire network one node at a time.

no code implementations • 13 Nov 2013 • Behnam Neyshabur, Rina Panigrahy

We investigate the problem of factorizing a matrix into several sparse matrices and propose an algorithm for this under randomness and sparsity assumptions.

no code implementations • 7 May 2013 • Alexandr Andoni, Rina Panigrahy

To obtain our main result, we show that the optimal payoff functions have to satisfy the Hermite differential equation, and hence are given by the solutions to this equation.

no code implementations • 29 Apr 2013 • Rina Panigrahy, Preyas Popat

In this paper we show a randomized algorithm that in an amortized sense gets a regret of $O(\sqrt x)$ for any interval when the sequence is partitioned into intervals arbitrarily.

no code implementations • 29 Apr 2013 • Rina Panigrahy, Preyas Popat

In this work we study how "fractal-like" processes arise in a prediction game where an adversary is generating a sequence of bits and an algorithm is trying to predict them.

no code implementations • NeurIPS 2011 • Michael Kapralov, Rina Panigrahy

Moreover, for {\em any window of size $n$} the regret of our algorithm to any expert never exceeds $O(\sqrt{n(\log N+\log T)})$, where $N$ is the number of experts and $T$ is the time horizon, while maintaining the essentially zero loss property.

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