Search Results for author: Zahra Shakeri

Found 7 papers, 1 papers with code

Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models

no code implementations7 Oct 2023 Song Jiang, Zahra Shakeri, Aaron Chan, Maziar Sanjabi, Hamed Firooz, Yinglong Xia, Bugra Akyildiz, Yizhou Sun, Jinchao Li, Qifan Wang, Asli Celikyilmaz

Breakdown analysis further highlights RESPROMPT particularly excels in complex multi-step reasoning: for questions demanding at least five reasoning steps, RESPROMPT outperforms the best CoT based benchmarks by a remarkable average improvement of 21. 1% on LLaMA-65B and 14. 3% on LLaMA2-70B.

Math

On the Equivalence of Graph Convolution and Mixup

no code implementations29 Sep 2023 Xiaotian Han, Hanqing Zeng, Yu Chen, Shaoliang Nie, Jingzhou Liu, Kanika Narang, Zahra Shakeri, Karthik Abinav Sankararaman, Song Jiang, Madian Khabsa, Qifan Wang, Xia Hu

We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.

Data Augmentation

Prosody Transfer in Neural Text to Speech Using Global Pitch and Loudness Features

no code implementations21 Nov 2019 Siddharth Gururani, Kilol Gupta, Dhaval Shah, Zahra Shakeri, Jervis Pinto

This paper presents a simple yet effective method to achieve prosody transfer from a reference speech signal to synthesized speech.

Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms

1 code implementation22 Mar 2019 Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa

This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning.

Dictionary Learning

Identifiability of Kronecker-structured Dictionaries for Tensor Data

no code implementations10 Dec 2017 Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa

This paper derives sufficient conditions for local recovery of coordinate dictionaries comprising a Kronecker-structured dictionary that is used for representing $K$th-order tensor data.

STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery

no code implementations13 Nov 2017 Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa

In recent years, a class of dictionaries have been proposed for multidimensional (tensor) data representation that exploit the structure of tensor data by imposing a Kronecker structure on the dictionary underlying the data.

Dictionary Learning

Minimax Lower Bounds for Kronecker-Structured Dictionary Learning

no code implementations17 May 2016 Zahra Shakeri, Waheed U. Bajwa, Anand D. Sarwate

This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk.

Dictionary Learning

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