
A TheoreticalEmpirical Approach to Estimating Sample Complexity of DNNs
This paper focuses on understanding how the generalization error scales ...
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Continual learning with directionconstrained optimization
This paper studies a new design of the optimization algorithm for traini...
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Backdoor Attacks on the DNN Interpretation System
Interpretability is crucial to understand the inner workings of deep neu...
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SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions
This paper addresses the problem of optimizing partition functions in a ...
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Multimodal Experts Network for Autonomous Driving
Endtoend learning from sensory data has shown promising results in aut...
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Wasserstein Reinforcement Learning
We propose behaviordriven optimization via Wasserstein distances (WDs) ...
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LdSM: Logarithmdepth Streaming Multilabel Decision Trees
We consider multilabel classification where the goal is to annotate eac...
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Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models
We consider distributed optimization under communication constraints for...
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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...
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Adversarial LearningBased OnLine Anomaly Monitoring for Assured Autonomy
The paper proposes an online monitoring framework for continuous realt...
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Beyond Backprop: Alternating Minimization with coActivation Memory
We propose a novel online algorithm for training deep feedforward neural...
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VisualBackProp for learning using privileged information with CNNs
In many machine learning applications, from medical diagnostics to auton...
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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...
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Invertible Autoencoder for domain adaptation
The unsupervised imagetoimage translation aims at finding a mapping be...
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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...
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Explaining How a Deep Neural Network Trained with EndtoEnd Learning Steers a Car
As part of a complete software stack for autonomous driving, NVIDIA has ...
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VisualBackProp: efficient visualization of CNNs
This paper proposes a new method, that we call VisualBackProp, for visua...
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EntropySGD: Biasing Gradient Descent Into Wide Valleys
This paper proposes a new optimization algorithm called EntropySGD for ...
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Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation
We consider multiclass classification where the predictor has a hierarc...
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Binary embeddings with structured hashed projections
We consider the hashing mechanism for constructing binary embeddings, th...
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Deep learning with Elastic Averaging SGD
We study the problem of stochastic optimization for deep learning in the...
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The Loss Surfaces of Multilayer Networks
We study the connection between the highly nonconvex loss function of a...
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Differentially and nondifferentiallyprivate random decision trees
We consider supervised learning with random decision trees, where the tr...
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Semistochastic Quadratic Bound Methods
Partition functions arise in a variety of settings, including conditiona...
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Anna Choromanska
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Assistant Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering.