514 citations · 545 across the 6 of their papers we have counts for
6 papers
A Rate-Distortion Framework for Explaining Black-box Model Decisions
Stefan Kolek, Duc Anh Nguyen, Ron Levie +2
We present the Rate-Distortion Explanation (RDE) framework, a mathematically well-founded method for explaining black-box model decisions. The framework is based on perturbations o…
An Extensible Benchmark Suite for Learning to Simulate Physical Systems
Karl Otness, Arvi Gjoka, Joan Bruna +4
Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications. Recently, there has been a surge in data-dr…
Voice Conversion using Convolutional Neural Networks
Shariq Mobin, Joan Bruna
The human auditory system is able to distinguish the vocal source of thousands of speakers, yet not much is known about what features the auditory system uses to do this. Fourier T…
Audio Source Separation with Discriminative Scattering Networks
Pablo Sprechmann, Joan Bruna, Yann LeCun
In this report we describe an ongoing line of research for solving single-channel source separation problems. Many monaural signal decomposition techniques proposed in the literatu…
Training Convolutional Networks with Noisy Labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri +2
The availability of large labeled datasets has allowed Convolutional Network models to achieve impressive recognition results. However, in many settings manual annotation of the da…
Invariant Scattering Convolution Networks
Joan Bruna, Stéphane Mallat
A wavelet scattering network computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification. It…