Search Results for author: Amirali Aghazadeh

Found 7 papers, 5 papers with code

ProtiGeno: a prokaryotic short gene finder using protein language models

1 code implementation19 Jul 2023 Tony Tu, Gautham Krishna, Amirali Aghazadeh

Prokaryotic gene prediction plays an important role in understanding the biology of organisms and their function with applications in medicine and biotechnology.

Protein Language Model

Efficiently Computing Sparse Fourier Transforms of $q$-ary Functions

1 code implementation15 Jan 2023 Yigit Efe Erginbas, Justin Singh Kang, Amirali Aghazadeh, Kannan Ramchandran

Fourier transformations of pseudo-Boolean functions are popular tools for analyzing functions of binary sequences.

Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces

no code implementations5 Oct 2022 Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran

Data-driven machine learning models are being increasingly employed in several important inference problems in biology, chemistry, and physics which require learning over combinatorial spaces.

Group-Structured Adversarial Training

no code implementations18 Jun 2021 Farzan Farnia, Amirali Aghazadeh, James Zou, David Tse

Robust training methods against perturbations to the input data have received great attention in the machine learning literature.

BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory

1 code implementation26 Oct 2020 Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, O. Ozan Koyluoglu, Kannan Ramchandran

We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing machine.

feature selection

Ultra Large-Scale Feature Selection using Count-Sketches

1 code implementation ICML 2018 Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, baraniuk

We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.

BIG-bench Machine Learning feature selection

MISSION: Ultra Large-Scale Feature Selection using Count-Sketches

1 code implementation12 Jun 2018 Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk

We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.

BIG-bench Machine Learning feature selection

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