Search Results for author:
Found 16 papers, 3 papers with code
Spreads in Effective Learning Rates: The Perils of Batch Normalization During Early Training
Using large LRs is analogous to applying an explicit solver to a stiff non-linear ODE, causing overshooting and vanishing gradients in lower layers after the first step.
Initialization Noise in Image Gradients and Saliency Maps
In this paper, we examine gradients of logits of image classification CNNs by input pixel values.
General Nonlinearities in SO(2)-Equivariant CNNs
Invariance under symmetry is an important problem in machine learning.
Nonlinearities in Steerable SO(2)-Equivariant CNNs
Invariance under symmetry is an important problem in machine learning.
ActCooLR – High-Level Learning Rate Schedules using Activation Pattern Temperature
We consider the aspect of learning rate (LR-)scheduling in neural networks, which often significantly affects achievable training time and generalization performance.
Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability
Switching between different levels of resolution is essential for multiscale modeling, but restoring details at higher resolution remains challenging.
Chemical Physics Computational Physics
Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks
In this paper, we apply harmonic distortion analysis to understand the effect of nonlinearities in the spectral domain.
Meta-Learning for Recalibration of EMG-Based Upper Limb Prostheses
An EMG-based upper limb prosthesis relies on a statistical pattern recognition system to map the EMG signal of residual forearm muscles into the appropriate hand movements.
Deep Non-Line-of-Sight Reconstruction
We demonstrate that our feed-forward network, even though it is trained solely on synthetic data, generalizes to measured data from SPAD sensors and is able to obtain results that are competitive with model-based reconstruction methods.
Progressive Stochastic Binarization of Deep Networks
By focusing computational attention using progressive sampling, we reduce inference costs on ImageNet further by a factor of up to 33% (before network pruning).
Investigations on End-to-End Audiovisual Fusion
Audiovisual speech recognition (AVSR) is a method to alleviate the adverse effect of noise in the acoustic signal.
Improving Speaker-Independent Lipreading with Domain-Adversarial Training
We present a Lipreading system, i. e. a speech recognition system using only visual features, which uses domain-adversarial training for speaker independence.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis.
Lipreading with Long Short-Term Memory
Lipreading, i. e. speech recognition from visual-only recordings of a speaker's face, can be achieved with a processing pipeline based solely on neural networks, yielding significantly better accuracy than conventional methods.
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images.
A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation
In this paper, we introduce a new approach to partial, intrinsic isometric matching.
