Search Results for author: Nadiia Chepurko

Found 5 papers, 1 papers with code

Learning Program Representations for Food Images and Cooking Recipes

no code implementations CVPR 2022 Dim P. Papadopoulos, Enrique Mora, Nadiia Chepurko, Kuan Wei Huang, Ferda Ofli, Antonio Torralba

To validate our idea, we crowdsource programs for cooking recipes and show that: (a) projecting the image-recipe embeddings into programs leads to better cross-modal retrieval results; (b) generating programs from images leads to better recognition results compared to predicting raw cooking instructions; and (c) we can generate food images by manipulating programs via optimizing the latent code of a GAN.

Cross-Modal Retrieval Retrieval

Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time

no code implementations16 Jul 2021 Nadiia Chepurko, Kenneth L. Clarkson, Praneeth Kacham, David P. Woodruff

This question is regarding the logarithmic factors in the sketching dimension of existing oblivious subspace embeddings that achieve constant-factor approximation.

Open-Ended Question Answering regression

Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra

no code implementations9 Nov 2020 Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, Honghao Lin, David P. Woodruff

We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression that are comparable to their quantum analogues.

Recommendation Systems

ARDA: Automatic Relational Data Augmentation for Machine Learning

1 code implementation21 Mar 2020 Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen, Raul Castro Fernandez, Tim Kraska, David Karger

Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join.

BIG-bench Machine Learning Data Augmentation +2

Robust and Sample Optimal Algorithms for PSD Low-Rank Approximation

no code implementations9 Dec 2019 Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff

Our main result is to resolve this question by obtaining an optimal algorithm that queries $O(nk/\epsilon)$ entries of $A$ and outputs a relative-error low-rank approximation in $O(n(k/\epsilon)^{\omega-1})$ time.

Cannot find the paper you are looking for? You can Submit a new open access paper.