Bosch

∙ Gerlingen , Germany ∙ 0 followers

Robert Bosch GmbH (/bɒʃ/; German: ), or Bosch, is a world leading multinational engineering and electronics company headquartered in Gerlingen, near Stuttgart, Germany.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks

To verify and validate networks, it is essential to gain insight into th...
08/07/2019 ∙ by Jörg Wagner, et al. ∙ 26 ∙ share

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On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

Uncertainty estimates help to identify ambiguous, novel, or anomalous in...
12/16/2019 ∙ by Kanil Patel, et al. ∙ 22 ∙ share

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An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice

The autonomous driving (AD) industry is exploring the use of knowledge g...
02/29/2020 ∙ by Ruwan Wickramarachchi, et al. ∙ 15 ∙ share

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Defending against Universal Perturbations with Shared Adversarial Training

Classifiers such as deep neural networks have been shown to be vulnerabl...
12/10/2018 ∙ by Chaithanya Kumar Mummadi, et al. ∙ 12 ∙ share

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When are Neural ODE Solutions Proper ODEs?

A key appeal of the recently proposed Neural Ordinary Differential Equat...
07/30/2020 ∙ by Katharina Ott, et al. ∙ 10 ∙ share

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Unifying Model Explainability and Robustness via Machine-Checkable Concepts

As deep neural networks (DNNs) get adopted in an ever-increasing number ...
07/01/2020 ∙ by Vedant Nanda, et al. ∙ 8 ∙ share

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Grid Saliency for Context Explanations of Semantic Segmentation

Recently, there has been a growing interest in developing saliency metho...
07/30/2019 ∙ by Lukas Hoyer, et al. ∙ 5 ∙ share

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Short-Term Prediction and Multi-Camera Fusion on Semantic Grids

An environment representation (ER) is a substantial part of every autono...
03/21/2019 ∙ by Lukas Hoyer, et al. ∙ 4 ∙ share

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The Functional Neural Process

We present a new family of exchangeable stochastic processes, the Functi...
06/19/2019 ∙ by Christos Louizos, et al. ∙ 4 ∙ share

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Wasserstein Adversarial Imitation Learning

Imitation Learning describes the problem of recovering an expert policy ...
06/19/2019 ∙ by Huang Xiao, et al. ∙ 3 ∙ share

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Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes

Existing 3D scene flow estimation methods provide the 3D geometry and 3D...
08/17/2019 ∙ by Fabian Brickwedde, et al. ∙ 3 ∙ share

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Deep Multiple Instance Feature Learning via Variational Autoencoder

We describe a novel weakly supervised deep learning framework that combi...
07/06/2018 ∙ by Shabnam Ghaffarzadegan, et al. ∙ 2 ∙ share

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SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

We introduce the SE(3)-Transformer, a variant of the self-attention modu...
06/18/2020 ∙ by Fabian B. Fuchs, et al. ∙ 1 ∙ share

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Provident Vehicle Detection at Night for Advanced Driver Assistance Systems

In recent years, computer vision algorithms have become more and more po...
07/23/2021 ∙ by Lukas Ewecker, et al. ∙ 1 ∙ share

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Compression for Smooth Shape Analysis

Most 3D shape analysis methods use triangular meshes to discretize both ...
11/29/2017 ∙ by V. Estellers, et al. ∙ 0 ∙ share

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Simple And Efficient Architecture Search for Convolutional Neural Networks

Neural networks have recently had a lot of success for many tasks. Howev...
11/13/2017 ∙ by Thomas Elsken, et al. ∙ 0 ∙ share

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Prediction Scores as a Window into Classifier Behavior

Most multi-class classifiers make their prediction for a test sample by ...
11/18/2017 ∙ by Medha Katehara, et al. ∙ 0 ∙ share

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Universal Adversarial Perturbations Against Semantic Image Segmentation

While deep learning is remarkably successful on perceptual tasks, it was...
04/19/2017 ∙ by Jan-Hendrik Metzen, et al. ∙ 0 ∙ share

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Adversarial Examples for Semantic Image Segmentation

Machine learning methods in general and Deep Neural Networks in particul...
03/03/2017 ∙ by Volker Fischer, et al. ∙ 0 ∙ share

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Efficient Stochastic Inference of Bitwise Deep Neural Networks

Recently published methods enable training of bitwise neural networks wh...
11/20/2016 ∙ by Sebastian Vogel, et al. ∙ 0 ∙ share

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Do Convolutional Neural Networks Learn Class Hierarchy?

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art ...
10/17/2017 ∙ by Bilal Alsallakh, et al. ∙ 0 ∙ share

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Very Deep Convolutional Neural Networks for Raw Waveforms

Learning acoustic models directly from the raw waveform data with minima...
10/01/2016 ∙ by Wei Dai, et al. ∙ 0 ∙ share

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Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing

In a streaming environment, there is often a need for statistical predic...
07/25/2017 ∙ by Shujian Yu, et al. ∙ 0 ∙ share

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Efficient Approximate Solutions to Mutual Information Based Global Feature Selection

Mutual Information (MI) is often used for feature selection when develop...
06/23/2017 ∙ by Hemanth Venkateswara, et al. ∙ 0 ∙ share

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Learning Filter Banks Using Deep Learning For Acoustic Signals

Designing appropriate features for acoustic event recognition tasks is a...
11/29/2016 ∙ by Shuhui Qu, et al. ∙ 0 ∙ share

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Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

Convolutional neural networks (CNNs) show impressive performance for ima...
06/29/2017 ∙ by Waleed M. Gondal, et al. ∙ 0 ∙ share

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Coupled Support Vector Machines for Supervised Domain Adaptation

Popular domain adaptation (DA) techniques learn a classifier for the tar...
06/22/2017 ∙ by Hemanth Venkateswara, et al. ∙ 0 ∙ share

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Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

PID control architectures are widely used in industrial applications. De...
03/08/2017 ∙ by Andreas Doerr, et al. ∙ 0 ∙ share

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Directional Decision Lists

In this paper we introduce a novel family of decision lists consisting o...
08/30/2015 ∙ by Marc Goessling, et al. ∙ 0 ∙ share

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Concept Drift Detection for Streaming Data

Common statistical prediction models often require and assume stationari...
04/04/2015 ∙ by Heng Wang, et al. ∙ 0 ∙ share

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Multimodal Task-Driven Dictionary Learning for Image Classification

Dictionary learning algorithms have been successfully used for both reco...
02/04/2015 ∙ by Soheil Bahrampour, et al. ∙ 0 ∙ share

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Detecting Table Region in PDF Documents Using Distant Supervision

Superior to state-of-the-art approaches which compete in table recogniti...
06/29/2015 ∙ by Miao Fan, et al. ∙ 0 ∙ share

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Multiple Instance Deep Learning for Weakly Supervised Audio Event Detection

State-of-the-art audio event detection (AED) systems rely on supervised ...
12/27/2017 ∙ by Shao-Yen Tseng, et al. ∙ 0 ∙ share

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Model Order Reduction for Rotating Electrical Machines

The simulation of electric rotating machines is both computationally exp...
05/10/2017 ∙ by Zeger Bontinck, et al. ∙ 0 ∙ share

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Effective Building Block Design for Deep Convolutional Neural Networks using Search

Deep learning has shown promising results on many machine learning tasks...
01/25/2018 ∙ by Jayanta K. Dutta, et al. ∙ 0 ∙ share

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Multiple Instance Deep Learning for Weakly Supervised Small-Footprint Audio Event Detection

State-of-the-art audio event detection (AED) systems rely on supervised ...
12/27/2017 ∙ by Shao-Yen Tseng, et al. ∙ 0 ∙ share

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Building robust prediction models for defective sensor data using Artificial Neural Networks

Predicting the health of components in complex dynamic systems such as a...
04/16/2018 ∙ by Arvind Kumar Shekar, et al. ∙ 0 ∙ share

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Communication channels in safety analysis: An industrial exploratory case study

Safety analysis is a predominant activity in developing safety-critical ...
04/24/2018 ∙ by Yang Wang, et al. ∙ 0 ∙ share

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Simplified SPARQL REST API - CRUD on JSON Object Graphs via URI Paths

Within the Semantic Web community, SPARQL is one of the predominant lang...
05/03/2018 ∙ by Markus Schröder, et al. ∙ 0 ∙ share

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Scaling provable adversarial defenses

Recent work has developed methods for learning deep network classifiers ...
05/31/2018 ∙ by Eric Wong, et al. ∙ 0 ∙ share

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Evidential Deep Learning to Quantify Classification Uncertainty

Deterministic neural nets have been shown to learn effective predictors ...
06/05/2018 ∙ by Murat Sensoy, et al. ∙ 0 ∙ share

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Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection

Automatic heart sound abnormality detection can play a vital role in the...
06/15/2018 ∙ by Ahmed Imtiaz Humayun, et al. ∙ 0 ∙ share

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An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification

In this work, we propose an ensemble of classifiers to distinguish betwe...
06/18/2018 ∙ by Ahmed Imtiaz Humayun, et al. ∙ 0 ∙ share

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Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging

Character-level models of tokens have been shown to be effective at deal...
08/13/2018 ∙ by Apostolos Kemos, et al. ∙ 0 ∙ share

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Neural Architecture Search: A Survey

Deep Learning has enabled remarkable progress over the last years on a v...
08/16/2018 ∙ by Thomas Elsken, et al. ∙ 0 ∙ share

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A Trio Neural Model for Dynamic Entity Relatedness Ranking

Measuring entity relatedness is a fundamental task for many natural lang...
08/24/2018 ∙ by Tu Nguyen, et al. ∙ 0 ∙ share

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How Robust is 3D Human Pose Estimation to Occlusion?

Occlusion is commonplace in realistic human-robot shared environments, y...
08/28/2018 ∙ by István Sárándi, et al. ∙ 0 ∙ share

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Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose Estimation

In this paper we present our winning entry at the 2018 ECCV PoseTrack Ch...
09/13/2018 ∙ by István Sárándi, et al. ∙ 0 ∙ share

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A 5G Architecture for The Factory of the Future

Factory automation and production are currently undergoing massive chang...
09/25/2018 ∙ by Stephan Ludwig, et al. ∙ 0 ∙ share

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Probing Attacks on Physical Layer Key Agreement for Automotive Controller Area Networks (Extended Version)

Efficient key management for automotive networks (CAN) is a critical ele...
10/16/2018 ∙ by Shalabh Jain, et al. ∙ 0 ∙ share

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