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most citedMethods for Interpreting and Understanding Deep Neural Networks

2.8k citations

Showing 2017Show all

90 papers · 1 filter

cs.LO2017★ 6 cited

Language and Proofs for Higher-Order SMT (Work in Progress)

Haniel Barbosa, Jasmin Christian Blanchette, Simon Cruanes +2

Satisfiability modulo theories (SMT) solvers have throughout the years been able to cope with increasingly expressive formulas, from ground logics to full first-order logic modulo…

cs.CV2017

Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images

Tribhuvanesh Orekondy, Mario Fritz, Bernt Schiele

Images convey a broad spectrum of personal information. If such images are shared on social media platforms, this personal information is leaked which conflicts with the privacy of…

cs.CV2017★ 8 cited

GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB

Franziska Mueller, Florian Bernard, Oleksandr Sotnychenko +4

We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence. Our tracking method combines a convolutional neural network with a k…

physics.chem-ph2017★ 2.3k cited

SchNet - a deep learning architecture for molecules and materials

Kristof T. Schütt, Huziel E. Sauceda, Pieter-Jan Kindermans +2

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in ch…

cs.HC2017★ 1 cited

A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks

Arif Khan, Ingmar Steiner, Yusuke Sugano +2

Phonetic segmentation is the process of splitting speech into distinct phonetic units. Human experts routinely perform this task manually by analyzing auditory and visual cues usin…

cs.CV2017★ 1 cited

Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB

Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller +4

We propose a new single-shot method for multi-person 3D pose estimation in general scenes from a monocular RGB camera. Our approach uses novel occlusion-robust pose-maps (ORPM) whi…