b'Bosch'

research

∙ 08/07/2019

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks

To verify and validate networks, it is essential to gain insight into th...

Jörg Wagner, et al. ∙

research

∙ 10/28/2021

Orientation Probabilistic Movement Primitives on Riemannian Manifolds

Learning complex robot motions necessarily demands to have models that a...

Leonel Rozo, et al. ∙

research

∙ 12/16/2019

On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

Uncertainty estimates help to identify ambiguous, novel, or anomalous in...

Kanil Patel, et al. ∙

research

∙ 03/30/2022

Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems

Knowledge-based entity prediction (KEP) is a novel task that aims to imp...

Ruwan Wickramarachchi, et al. ∙

research

∙ 12/16/2021

How to augment your ViTs? Consistency loss and StyleAug, a random style transfer augmentation

The Vision Transformer (ViT) architecture has recently achieved competit...

Akash Umakantha, et al. ∙

research

∙ 02/29/2020

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...

Ruwan Wickramarachchi, et al. ∙

research

∙ 08/10/2021

BIDCD - Bosch Industrial Depth Completion Dataset

We introduce BIDCD - the Bosch Industrial Depth Completion Dataset. BIDC...

Adam Botach, et al. ∙

research

∙ 12/10/2018

Defending against Universal Perturbations with Shared Adversarial Training

Classifiers such as deep neural networks have been shown to be vulnerabl...

Chaithanya Kumar Mummadi, et al. ∙

research

∙ 08/01/2022

MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network

Estimating 6D poses of objects is an essential computer vision task. How...

Fabian Duffhauss, et al. ∙

research

∙ 07/30/2020

When are Neural ODE Solutions Proper ODEs?

A key appeal of the recently proposed Neural Ordinary Differential Equat...

Katharina Ott, et al. ∙

research

∙ 03/09/2022

What Matters For Meta-Learning Vision Regression Tasks?

Meta-learning is widely used in few-shot classification and function reg...

Ning Gao, et al. ∙

research

∙ 05/24/2022

Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs

We study for the first time uncertainty-aware modeling of continuous-tim...

Çağatay Yıldız, et al. ∙

research

∙ 07/01/2020

Unifying Model Explainability and Robustness via Machine-Checkable Concepts

As deep neural networks (DNNs) get adopted in an ever-increasing number ...

Vedant Nanda, et al. ∙

research

∙ 03/07/2022

Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language

Learning to classify video data from classes not included in the trainin...

Otniel-Bogdan Mercea, et al. ∙

research

∙ 05/31/2022

Contrasting quadratic assignments for set-based representation learning

The standard approach to contrastive learning is to maximize the agreeme...

Artem Moskalev, et al. ∙

research

∙ 07/30/2019

Grid Saliency for Context Explanations of Semantic Segmentation

Recently, there has been a growing interest in developing saliency metho...

Lukas Hoyer, et al. ∙

research

∙ 09/07/2021

Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models

Commonsense reasoning benchmarks have been largely solved by fine-tuning...

Kaixin Ma, et al. ∙

research

∙ 03/21/2019

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

An environment representation (ER) is a substantial part of every autono...

Lukas Hoyer, et al. ∙

research

∙ 06/19/2019

The Functional Neural Process

We present a new family of exchangeable stochastic processes, the Functi...

Christos Louizos, et al. ∙

research

∙ 06/19/2019

Wasserstein Adversarial Imitation Learning

Imitation Learning describes the problem of recovering an expert policy ...

Huang Xiao, et al. ∙

research

∙ 08/17/2019

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...

Fabian Brickwedde, et al. ∙

research

∙ 07/06/2018

Deep Multiple Instance Feature Learning via Variational Autoencoder

We describe a novel weakly supervised deep learning framework that combi...

Shabnam Ghaffarzadegan, et al. ∙

research

∙ 06/18/2020

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

We introduce the SE(3)-Transformer, a variant of the self-attention modu...

Fabian B. Fuchs, et al. ∙

research

∙ 07/23/2021

Provident Vehicle Detection at Night for Advanced Driver Assistance Systems

In recent years, computer vision algorithms have become more and more po...

Lukas Ewecker, et al. ∙

research

∙ 11/29/2017

Compression for Smooth Shape Analysis

Most 3D shape analysis methods use triangular meshes to discretize both ...

V. Estellers, et al. ∙

research

∙ 11/13/2017

Simple And Efficient Architecture Search for Convolutional Neural Networks

Neural networks have recently had a lot of success for many tasks. Howev...

Thomas Elsken, et al. ∙

research

∙ 11/18/2017

Prediction Scores as a Window into Classifier Behavior

Most multi-class classifiers make their prediction for a test sample by ...

Medha Katehara, et al. ∙

research

∙ 04/19/2017

Universal Adversarial Perturbations Against Semantic Image Segmentation

While deep learning is remarkably successful on perceptual tasks, it was...

Jan-Hendrik Metzen, et al. ∙

research

∙ 03/03/2017

Adversarial Examples for Semantic Image Segmentation

Machine learning methods in general and Deep Neural Networks in particul...

Volker Fischer, et al. ∙

research

∙ 11/20/2016

Efficient Stochastic Inference of Bitwise Deep Neural Networks

Recently published methods enable training of bitwise neural networks wh...

Sebastian Vogel, et al. ∙

research

∙ 10/17/2017

Do Convolutional Neural Networks Learn Class Hierarchy?

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art ...

Bilal Alsallakh, et al. ∙

research

∙ 10/01/2016

Very Deep Convolutional Neural Networks for Raw Waveforms

Learning acoustic models directly from the raw waveform data with minima...

Wei Dai, et al. ∙

research

∙ 07/25/2017

Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing

In a streaming environment, there is often a need for statistical predic...

Shujian Yu, et al. ∙

research

∙ 06/23/2017

Efficient Approximate Solutions to Mutual Information Based Global Feature Selection

Mutual Information (MI) is often used for feature selection when develop...

Hemanth Venkateswara, et al. ∙

research

∙ 11/29/2016

Learning Filter Banks Using Deep Learning For Acoustic Signals

Designing appropriate features for acoustic event recognition tasks is a...

Shuhui Qu, et al. ∙

research

∙ 06/29/2017

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

Convolutional neural networks (CNNs) show impressive performance for ima...

Waleed M. Gondal, et al. ∙

research

∙ 06/22/2017

Coupled Support Vector Machines for Supervised Domain Adaptation

Popular domain adaptation (DA) techniques learn a classifier for the tar...

Hemanth Venkateswara, et al. ∙

research

∙ 03/08/2017

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

PID control architectures are widely used in industrial applications. De...

Andreas Doerr, et al. ∙

research

∙ 08/30/2015

Directional Decision Lists

In this paper we introduce a novel family of decision lists consisting o...

Marc Goessling, et al. ∙

research

∙ 04/04/2015

Concept Drift Detection for Streaming Data

Common statistical prediction models often require and assume stationari...

Heng Wang, et al. ∙

research

∙ 02/04/2015

Multimodal Task-Driven Dictionary Learning for Image Classification

Dictionary learning algorithms have been successfully used for both reco...

Soheil Bahrampour, et al. ∙

research

∙ 06/29/2015

Detecting Table Region in PDF Documents Using Distant Supervision

Superior to state-of-the-art approaches which compete in table recogniti...

Miao Fan, et al. ∙

research

∙ 12/27/2017

Multiple Instance Deep Learning for Weakly Supervised Audio Event Detection

State-of-the-art audio event detection (AED) systems rely on supervised ...

Shao-Yen Tseng, et al. ∙

research

∙ 05/10/2017

Model Order Reduction for Rotating Electrical Machines

The simulation of electric rotating machines is both computationally exp...

Zeger Bontinck, et al. ∙

research

∙ 01/25/2018

Effective Building Block Design for Deep Convolutional Neural Networks using Search

Deep learning has shown promising results on many machine learning tasks...

Jayanta K. Dutta, et al. ∙

research

∙ 12/27/2017

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

State-of-the-art audio event detection (AED) systems rely on supervised ...

Shao-Yen Tseng, et al. ∙

research

∙ 04/16/2018

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

Predicting the health of components in complex dynamic systems such as a...

Arvind Kumar Shekar, et al. ∙

research

∙ 04/24/2018

Communication channels in safety analysis: An industrial exploratory case study

Safety analysis is a predominant activity in developing safety-critical ...

Yang Wang, et al. ∙

research

∙ 05/03/2018

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

Within the Semantic Web community, SPARQL is one of the predominant lang...

Markus Schröder, et al. ∙

research

∙ 05/31/2018

Scaling provable adversarial defenses

Recent work has developed methods for learning deep network classifiers ...

Eric Wong, et al. ∙

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