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10/08/2022
TAME: Task Agnostic Continual Learning using Multiple Experts
The goal of lifelong learning is to continuously learn from non-stationa...
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09/26/2022
ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals
Among various sensors for assisted and autonomous driving systems, autom...
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01/20/2022
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape
In this paper, we study the sharpness of a deep learning (DL) loss lands...
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12/08/2021
ESAFE: Enterprise Security and Forensics at Scale
Securing enterprise networks presents challenges in terms of both their ...
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11/30/2021
AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop
Modern deep learning (DL) architectures are trained using variants of th...
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05/05/2021
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs
This paper focuses on understanding how the generalization error scales ...
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11/25/2020
Continual learning with direction-constrained optimization
This paper studies a new design of the optimization algorithm for traini...
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11/21/2020
Backdoor Attacks on the DNN Interpretation System
Interpretability is crucial to understand the inner workings of deep neu...
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11/03/2020
SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions
This paper addresses the problem of optimizing partition functions in a ...
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09/18/2020
Multi-modal Experts Network for Autonomous Driving
End-to-end learning from sensory data has shown promising results in aut...
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06/11/2019
Wasserstein Reinforcement Learning
We propose behavior-driven optimization via Wasserstein distances (WDs) ...
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05/24/2019
LdSM: Logarithm-depth Streaming Multi-label Decision Trees
We consider multi-label classification where the goal is to annotate eac...
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05/24/2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models
We consider distributed optimization under communication constraints for...
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02/16/2019
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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11/12/2018
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy
The paper proposes an on-line monitoring framework for continuous real-t...
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06/24/2018
Beyond Backprop: Alternating Minimization with co-Activation Memory
We propose a novel online algorithm for training deep feedforward neural...
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05/24/2018
VisualBackProp for learning using privileged information with CNNs
In many machine learning applications, from medical diagnostics to auton...
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02/13/2018
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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02/10/2018
Invertible Autoencoder for domain adaptation
The unsupervised image-to-image translation aims at finding a mapping be...
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02/08/2018
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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04/25/2017
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 ...
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11/16/2016
VisualBackProp: efficient visualization of CNNs
This paper proposes a new method, that we call VisualBackProp, for visua...
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11/06/2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
This paper proposes a new optimization algorithm called Entropy-SGD for ...
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10/14/2016
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation
We consider multi-class classification where the predictor has a hierarc...
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11/16/2015
Binary embeddings with structured hashed projections
We consider the hashing mechanism for constructing binary embeddings, th...
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12/20/2014
Deep learning with Elastic Averaging SGD
We study the problem of stochastic optimization for deep learning in the...
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11/30/2014
The Loss Surfaces of Multilayer Networks
We study the connection between the highly non-convex loss function of a...
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10/26/2014
Differentially- and non-differentially-private random decision trees
We consider supervised learning with random decision trees, where the tr...
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09/05/2013
