Ankush Garg | DeepAI

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∙ 08/14/2023

The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation

Automatic evaluation of machine translation (MT) is a critical tool driv...

0 Patrick Fernandes, et al. ∙

∙ 06/12/2023

Benchmarking Neural Network Training Algorithms

Training algorithms, broadly construed, are an essential part of every d...

6 George E. Dahl, et al. ∙

∙ 02/09/2023

Binarized Neural Machine Translation

The rapid scaling of language models is motivating research using low-bi...

0 Yichi Zhang, et al. ∙

∙ 09/23/2022

Do Current Multi-Task Optimization Methods in Deep Learning Even Help?

Recent research has proposed a series of specialized optimization algori...

36 Derrick Xin, et al. ∙

∙ 02/04/2022

Data Scaling Laws in NMT: The Effect of Noise and Architecture

In this work, we study the effect of varying the architecture and traini...

4 Yamini Bansal, et al. ∙

∙ 10/08/2021

A Loss Curvature Perspective on Training Instability in Deep Learning

In this work, we study the evolution of the loss Hessian across many cla...

47 Justin Gilmer, et al. ∙

∙ 10/06/2021

Efficient and Private Federated Learning with Partially Trainable Networks

Federated learning is used for decentralized training of machine learnin...

8 Hakim Sidahmed, et al. ∙

∙ 02/18/2021

Echo State Speech Recognition

We propose automatic speech recognition (ASR) models inspired by echo st...

11 Harsh Shrivastava, et al. ∙

∙ 10/28/2020

The geometry of integration in text classification RNNs

Despite the widespread application of recurrent neural networks (RNNs) a...

13 Kyle Aitken, et al. ∙

∙ 02/27/2020

Echo State Neural Machine Translation

We present neural machine translation (NMT) models inspired by echo stat...

0 Ankush Garg, et al. ∙

∙ 12/28/2018

Machine Translation: A Literature Review

Machine translation (MT) plays an important role in benefiting linguists...

0 Ankush Garg, et al. ∙

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