Anna Choromanska

Orcid: 0000-0002-2556-7009

Affiliations:
  • New York University, Courant Institute of Mathematical Sciences
  • Columbia University New York, Department of Electrical Engineering


According to our database1, Anna Choromanska authored at least 58 papers between 2012 and 2024.

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Bibliography

2024
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models.
CoRR, 2024

2023
Automatic document classification via transformers for regulations compliance management in large utility companies.
Neural Comput. Appl., August, 2023

ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022
DNN Patching: Progressive Fixing and Augmenting the Functionalities of DNNs for Autonomous Vehicles.
IEEE Robotics Autom. Lett., 2022

TAME: Task Agnostic Continual Learning using Multiple Experts.
CoRR, 2022

Overcoming Catastrophic Forgetting via Direction-Constrained Optimization.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Backdoor Attacks on the DNN Interpretation System.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
ESAFE: Enterprise Security and Forensics at Scale.
CoRR, 2021

AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop.
CoRR, 2021

A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020
Learning-Based Real-Time Process-Aware Anomaly Monitoring for Assured Autonomy.
IEEE Trans. Intell. Veh., 2020

Continual learning with direction-constrained optimization.
CoRR, 2020

SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions.
CoRR, 2020

Multi-modal Experts Network for Autonomous Driving.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Learning to Score Behaviors for Guided Policy Optimization.
Proceedings of the 37th International Conference on Machine Learning, 2020

LdSM: Logarithm-depth Streaming Multi-label Decision Trees.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
A deep learning gated architecture for UGV navigation robust to sensor failures.
Robotics Auton. Syst., 2019

LSALSA: accelerated source separation via learned sparse coding.
Mach. Learn., 2019

Wasserstein Reinforcement Learning.
CoRR, 2019

Skin Lesion Segmentation and Classification with Deep Learning System.
CoRR, 2019

Invertible Autoencoder for Domain Adaptation.
Comput., 2019

Extreme Multiclass Classification Criteria.
Comput., 2019

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Reconfigurable Network for Efficient Inferencing in Autonomous Vehicles.
Proceedings of the International Conference on Robotics and Automation, 2019

Beyond Backprop: Online Alternating Minimization with Auxiliary Variables.
Proceedings of the 36th International Conference on Machine Learning, 2019

Towards Automated Melanoma Detection With Deep Learning: Data Purification and Augmentation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2018
Beyond Backprop: Alternating Minimization with co-Activation Memory.
CoRR, 2018

VisualBackProp for learning using privileged information with CNNs.
CoRR, 2018

LSALSA: efficient sparse coding in single and multiple dictionary settings.
CoRR, 2018

Invertible Autoencoder for domain adaptation.
CoRR, 2018

Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

VisualBackProp: Efficient Visualization of CNNs for Autonomous Driving.
Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI.
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018

2017
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car.
CoRR, 2017

Sensor modality fusion with CNNs for UGV autonomous driving in indoor environments.
Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017

Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation.
Proceedings of the 34th International Conference on Machine Learning, 2017

Entropy-SGD: Biasing Gradient Descent Into Wide Valleys.
Proceedings of the 5th International Conference on Learning Representations, 2017

Structured adaptive and random spinners for fast machine learning computations.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Differentially-private learning of low dimensional manifolds.
Theor. Comput. Sci., 2016

Simultaneous Learning of Trees and Representations for Extreme Classification, with Application to Language Modeling.
CoRR, 2016

On the boosting ability of top-down decision tree learning algorithm for multiclass classification.
CoRR, 2016

VisualBackProp: visualizing CNNs for autonomous driving.
CoRR, 2016

Binary embeddings with structured hashed projections.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Deep learning with Elastic Averaging SGD.
Proceedings of the 3rd International Conference on Learning Representations, 2015

Logarithmic Time Online Multiclass prediction.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Open Problem: The landscape of the loss surfaces of multilayer networks.
Proceedings of The 28th Conference on Learning Theory, 2015

The Loss Surfaces of Multilayer Networks.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Automated Analysis of Images from Confocal Laser Scanning Microscopy Applied to Observation of Calcium Channel Subunits in Nerve Cell Model Line Subjected Electroporation and Calcium.
Proceedings of the Intelligent Information and Database Systems - 7th Asian Conference, 2015

2014
Selected machine learning reductions.
PhD thesis, 2014

The Loss Surface of Multilayer Networks.
CoRR, 2014

Differentially- and non-differentially-private random decision trees.
CoRR, 2014

Semistochastic Quadratic Bound Methods for Convex and Nonconvex Learning Problems.
Proceedings of the 2nd International Conference on Learning Representations, 2014

Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering.
CoRR, 2014

2013
Stochastic Bound Majorization.
CoRR, 2013

Fast Spectral Clustering via the Nyström Method.
Proceedings of the Algorithmic Learning Theory - 24th International Conference, 2013

2012
Online Clustering with Experts.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Majorization for CRFs and Latent Likelihoods.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012


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