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Featured Researchers

Max Welling
111 publications
Anima Anandkumar
86 publications
Qiang Liu
73 publications
Zhe Wang
66 publications
Yang Feng
53 publications
Deva Ramanan
52 publications
Hao Tang
50 publications
David Eppstein
40 publications
Cheng Zhang
36 publications
Sameer Singh
33 publications
Jun Guo
33 publications

Sherpa: Robust Hyperparameter Optimization for Machine Learning

Sherpa is a hyperparameter optimization library for machine learning mod...
05/08/2020 ∙ by Lars Hertel, et al. ∙ 111 ∙ share

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A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting

We study epidemic forecasting on real-world health data by a graph-struc...
02/13/2019 ∙ by Zhijian Li, et al. ∙ 26 ∙ share

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Probabilistic Kernel Support Vector Machines

We propose a probabilistic enhancement of standard kernel Support Vecto...
04/14/2019 ∙ by Yongxin Chen, et al. ∙ 24 ∙ share

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Learning from Learning Machines: Optimisation, Rules, and Social Norms

There is an analogy between machine learning systems and economic entiti...
12/29/2019 ∙ by Travis LaCroix, et al. ∙ 24 ∙ share

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Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compos...
05/02/2020 ∙ by Sanjay Subramanian, et al. ∙ 22 ∙ share

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Extreme Classification via Adversarial Softmax Approximation

Training a classifier over a large number of classes, known as 'extreme ...
02/15/2020 ∙ by Robert Bamler, et al. ∙ 20 ∙ share

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Tightening Bounds for Variational Inference by Revisiting Perturbation Theory

Variational inference has become one of the most widely used methods in ...
09/30/2019 ∙ by Robert Bamler, et al. ∙ 19 ∙ share

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Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation

Robots and self-driving vehicles face a number of challenges when naviga...
09/14/2019 ∙ by Jinwei Xing, et al. ∙ 13 ∙ share

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Image Reconstruction with Predictive Filter Flow

We propose a simple, interpretable framework for solving a wide range of...
11/28/2018 ∙ by Shu Kong, et al. ∙ 12 ∙ share

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Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation

Leveraging synthetically rendered data offers great potential to improve...
02/27/2020 ∙ by Yunhan Zhao, et al. ∙ 12 ∙ share

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Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets

Deep neural nets (DNNs) compression is crucial for adaptation to mobile ...
03/02/2020 ∙ by Thu Dinh, et al. ∙ 12 ∙ share

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Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving

In Autonomous Vehicles (AVs), one fundamental pillar is perception, whic...
07/16/2019 ∙ by Yulong Cao, et al. ∙ 11 ∙ share

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AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation

Radiation therapy (RT) is a common treatment for head and neck (HaN) can...
08/15/2018 ∙ by Wentao Zhu, et al. ∙ 10 ∙ share

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Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency

As deep reinforcement learning (RL) is applied to more tasks, there is a...
12/23/2019 ∙ by Piyush Gupta, et al. ∙ 10 ∙ share

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Active Bayesian Assessment for Black-Box Classifiers

Recent advances in machine learning have led to increased deployment of ...
02/16/2020 ∙ by Disi Ji, et al. ∙ 9 ∙ share

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Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation

Monocular estimation of 3d human pose has attracted increased attention ...
04/07/2020 ∙ by Zhe Wang, et al. ∙ 9 ∙ share

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Multi-View Collaborative Network Embedding

Real-world networks often exist with multiple views, where each view des...
05/17/2020 ∙ by Sezin Kircali Ata, et al. ∙ 8 ∙ share

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Memory capacity of neural networks with threshold and ReLU activations

Overwhelming theoretical and empirical evidence shows that mildly overpa...
01/20/2020 ∙ by Roman Vershynin, et al. ∙ 7 ∙ share

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Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes

Dynamic scenes that contain both object motion and egomotion are a chall...
03/09/2019 ∙ by Hirak J Kashyap, et al. ∙ 6 ∙ share

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Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets

Training activation quantized neural networks involves minimizing a piec...
03/13/2019 ∙ by Penghang Yin, et al. ∙ 6 ∙ share

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Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications

Representing entities and relations in an embedding space is a well-stud...
05/02/2019 ∙ by Pouya Pezeshkpour, et al. ∙ 6 ∙ share

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Curiosity-Driven Multi-Criteria Hindsight Experience Replay

Dealing with sparse rewards is a longstanding challenge in reinforcement...
06/09/2019 ∙ by John B. Lanier, et al. ∙ 3 ∙ share

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Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts

We develop new representations and algorithms for three-dimensional (3D)...
06/11/2019 ∙ by Zhile Ren, et al. ∙ 3 ∙ share

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Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks

Quantized deep neural networks (QDNNs) are attractive due to their much ...
08/15/2018 ∙ by Penghang Yin, et al. ∙ 2 ∙ share

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Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking

Echocardiography has become routinely used in the diagnosis of cardiomyo...
06/18/2019 ∙ by Wentao Zhu, et al. ∙ 2 ∙ share

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PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

We present PyCARL, a PyNN-based common Python programming interface for ...
03/21/2020 ∙ by Adarsha Balaji, et al. ∙ 1 ∙ share

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Universal halting times in optimization and machine learning

The authors present empirical distributions for the halting time (measur...
11/19/2015 ∙ by Levent Sagun, et al. ∙ 0 ∙ share

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DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification

In this work, we present a fully automated lung CT cancer diagnosis syst...
09/16/2017 ∙ by Wentao Zhu, et al. ∙ 0 ∙ share

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Square-Contact Representations of Partial 2-Trees and Triconnected Simply-Nested Graphs

A square-contact representation of a planar graph G=(V,E) maps vertices ...
10/01/2017 ∙ by Giordano Da Lozzo, et al. ∙ 0 ∙ share

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Weakly Supervised Action Localization by Sparse Temporal Pooling Network

We propose a weakly supervised temporal action localization algorithm on...
12/14/2017 ∙ by Phuc Nguyen, et al. ∙ 0 ∙ share

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Improving Malware Detection Accuracy by Extracting Icon Information

Detecting PE malware files is now commonly approached using statistical ...
12/10/2017 ∙ by Pedro Silva, et al. ∙ 0 ∙ share

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Adversarial Deep Structured Nets for Mass Segmentation from Mammograms

Mass segmentation provides effective morphological features which are im...
10/24/2017 ∙ by Wentao Zhu, et al. ∙ 0 ∙ share

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Mondrian Processes for Flow Cytometry Analysis

Analysis of flow cytometry data is an essential tool for clinical diagno...
11/21/2017 ∙ by Disi Ji, et al. ∙ 0 ∙ share

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Generating Natural Adversarial Examples

Due to their complex nature, it is hard to characterize the ways in whic...
10/31/2017 ∙ by Zhengli Zhao, et al. ∙ 0 ∙ share

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Learning Infinite RBMs with Frank-Wolfe

In this work, we propose an infinite restricted Boltzmann machine (RBM),...
10/15/2017 ∙ by Wei Ping, et al. ∙ 0 ∙ share

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Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis...
05/23/2017 ∙ by Wentao Zhu, et al. ∙ 0 ∙ share

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Information Design in Crowdfunding under Thresholding Policies

In crowdfunding, an entrepreneur often has to decide how to disclose the...
09/12/2017 ∙ by Wen Shen, et al. ∙ 0 ∙ share

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Regulating Highly Automated Robot Ecologies: Insights from Three User Studies

Highly automated robot ecologies (HARE), or societies of independent aut...
08/07/2017 ∙ by Wen Shen, et al. ∙ 0 ∙ share

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A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware

Neuromorphic computing is a promising solution for reducing the size, we...
11/04/2016 ∙ by Tiffany Hwu, et al. ∙ 0 ∙ share

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Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks

Many real world systems need to operate on heterogeneous information net...
07/25/2017 ∙ by Parisa Kordjamshidi, et al. ∙ 0 ∙ share

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Energy-Based Spherical Sparse Coding

In this paper, we explore an efficient variant of convolutional sparse c...
10/04/2017 ∙ by Bailey Kong, et al. ∙ 0 ∙ share

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Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity

Spike-timing-dependent plasticity (STDP) incurs both causal and acausal ...
07/11/2016 ∙ by Bruno U. Pedroni, et al. ∙ 0 ∙ share

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OPEB: Open Physical Environment Benchmark for Artificial Intelligence

Artificial Intelligence methods to solve continuous- control tasks have ...
07/04/2017 ∙ by Hamid Mirzaei, et al. ∙ 0 ∙ share

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Multi-Person Pose Estimation via Column Generation

We study the problem of multi-person pose estimation in natural images. ...
09/18/2017 ∙ by Shaofei Wang, et al. ∙ 0 ∙ share

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The FastMap Algorithm for Shortest Path Computations

We present a new preprocessing algorithm for embedding the nodes of a gi...
06/08/2017 ∙ by Liron Cohen, et al. ∙ 0 ∙ share

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Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning

Recent advances in combining deep learning and Reinforcement Learning ha...
05/30/2017 ∙ by Hamid Mirzaei, et al. ∙ 0 ∙ share

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Learning Activation Functions to Improve Deep Neural Networks

Artificial neural networks typically have a fixed, non-linear activation...
12/21/2014 ∙ by Forest Agostinelli, et al. ∙ 0 ∙ share

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Provable Methods for Training Neural Networks with Sparse Connectivity

We provide novel guaranteed approaches for training feedforward neural n...
12/08/2014 ∙ by Hanie Sedghi, et al. ∙ 0 ∙ share

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Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning

We formulate tracking as an online decision-making process, where a trac...
07/17/2017 ∙ by James Steven Supancic III, et al. ∙ 0 ∙ share

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Programs as Black-Box Explanations

Recent work in model-agnostic explanations of black-box machine learning...
11/22/2016 ∙ by Sameer Singh, et al. ∙ 0 ∙ share

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