Anna Choromanska

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NYU college ∙ Columbia University

Assistant Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering.

A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs

This paper focuses on understanding how the generalization error scales ...
05/05/2021 ∙ by Devansh Bisla, et al. ∙ 2 ∙ share

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Continual learning with direction-constrained optimization

This paper studies a new design of the optimization algorithm for traini...
11/25/2020 ∙ by Yunfei Teng, et al. ∙ 6 ∙ share

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Backdoor Attacks on the DNN Interpretation System

Interpretability is crucial to understand the inner workings of deep neu...
11/21/2020 ∙ by Shihong Fang, et al. ∙ 1 ∙ share

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SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions

This paper addresses the problem of optimizing partition functions in a ...
11/03/2020 ∙ by Jing Wang, et al. ∙ 0 ∙ share

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Multi-modal Experts Network for Autonomous Driving

End-to-end learning from sensory data has shown promising results in aut...
09/18/2020 ∙ by Shihong Fang, et al. ∙ 32 ∙ share

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Wasserstein Reinforcement Learning

We propose behavior-driven optimization via Wasserstein distances (WDs) ...
06/11/2019 ∙ by Aldo Pacchiano, et al. ∙ 1 ∙ share

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LdSM: Logarithm-depth Streaming Multi-label Decision Trees

We consider multi-label classification where the goal is to annotate eac...
05/24/2019 ∙ by Maryam Majzoubi, et al. ∙ 0 ∙ share

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Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

We consider distributed optimization under communication constraints for...
05/24/2019 ∙ by Yunfei Teng, et al. ∙ 2 ∙ share

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Skin Lesion Segmentation and Classification with Deep Learning System

Melanoma is one of the ten most common cancers in the US. Early detectio...
02/16/2019 ∙ by Devansh Bisla, et al. ∙ 20 ∙ share

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Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy

The paper proposes an on-line monitoring framework for continuous real-t...
11/12/2018 ∙ by Naman Patel, et al. ∙ 0 ∙ share

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Beyond Backprop: Alternating Minimization with co-Activation Memory

We propose a novel online algorithm for training deep feedforward neural...
06/24/2018 ∙ by Anna Choromanska, et al. ∙ 0 ∙ share

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VisualBackProp for learning using privileged information with CNNs

In many machine learning applications, from medical diagnostics to auton...
05/24/2018 ∙ by Devansh Bisla, et al. ∙ 0 ∙ share

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LSALSA: efficient sparse coding in single and multiple dictionary settings

We propose an efficient sparse coding (SC) framework for obtaining spars...
02/13/2018 ∙ by Benjamin Cowen, et al. ∙ 0 ∙ share

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Invertible Autoencoder for domain adaptation

The unsupervised image-to-image translation aims at finding a mapping be...
02/10/2018 ∙ by Yunfei Teng, et al. ∙ 0 ∙ share

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A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI

Mild traumatic brain injury (mTBI) is a growing public health problem wi...
02/08/2018 ∙ by Shervin Minaee, et al. ∙ 0 ∙ share

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Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car

As part of a complete software stack for autonomous driving, NVIDIA has ...
04/25/2017 ∙ by Mariusz Bojarski, et al. ∙ 0 ∙ share

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VisualBackProp: efficient visualization of CNNs

This paper proposes a new method, that we call VisualBackProp, for visua...
11/16/2016 ∙ by Mariusz Bojarski, et al. ∙ 0 ∙ share

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Entropy-SGD: Biasing Gradient Descent Into Wide Valleys

This paper proposes a new optimization algorithm called Entropy-SGD for ...
11/06/2016 ∙ by Pratik Chaudhari, et al. ∙ 0 ∙ share

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Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation

We consider multi-class classification where the predictor has a hierarc...
10/14/2016 ∙ by Yacine Jernite, et al. ∙ 0 ∙ share

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Binary embeddings with structured hashed projections

We consider the hashing mechanism for constructing binary embeddings, th...
11/16/2015 ∙ by Anna Choromanska, et al. ∙ 0 ∙ share

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Deep learning with Elastic Averaging SGD

We study the problem of stochastic optimization for deep learning in the...
12/20/2014 ∙ by Sixin Zhang, et al. ∙ 0 ∙ share

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The Loss Surfaces of Multilayer Networks

We study the connection between the highly non-convex loss function of a...
11/30/2014 ∙ by Anna Choromanska, et al. ∙ 0 ∙ share

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Differentially- and non-differentially-private random decision trees

We consider supervised learning with random decision trees, where the tr...
10/26/2014 ∙ by Mariusz Bojarski, et al. ∙ 0 ∙ share

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Semistochastic Quadratic Bound Methods

Partition functions arise in a variety of settings, including conditiona...
09/05/2013 ∙ by Aleksandr Y. Aravkin, et al. ∙ 0 ∙ share

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Anna Choromanska

Featured Co-authors

Stefano Soatto

Yann LeCun

Yao Wang

Jing Wang

David Sontag

Sanjiv Kumar

Adrian Weller

Shervin Minaee

Krzysztof Choromanski

Aleksandr Y. Aravkin

Aldo Pacchiano

A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs

Continual learning with direction-constrained optimization

Backdoor Attacks on the DNN Interpretation System

SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions

Multi-modal Experts Network for Autonomous Driving

Wasserstein Reinforcement Learning

LdSM: Logarithm-depth Streaming Multi-label Decision Trees

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Skin Lesion Segmentation and Classification with Deep Learning System

Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy

Beyond Backprop: Alternating Minimization with co-Activation Memory

VisualBackProp for learning using privileged information with CNNs

LSALSA: efficient sparse coding in single and multiple dictionary settings

Invertible Autoencoder for domain adaptation

A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI

Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car

VisualBackProp: efficient visualization of CNNs

Entropy-SGD: Biasing Gradient Descent Into Wide Valleys

Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation

Binary embeddings with structured hashed projections

Deep learning with Elastic Averaging SGD

The Loss Surfaces of Multilayer Networks

Differentially- and non-differentially-private random decision trees

Semistochastic Quadratic Bound Methods