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Found 15 papers, 5 papers with code
Navigating protein landscapes with a machine-learned transferable coarse-grained model
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
Transforming Gait: Video-Based Spatiotemporal Gait Analysis
Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation.
Spatiotemporal Characterization of Gait from Monocular Videos with Transformers
Using more than 9000 monocular video from an instrumented gait analysis lab, we evaluated the performance of existing algorithms for measuring kinematics.
Shallow Univariate ReLu Networks as Splines: Initialization, Loss Surface, Hessian, & Gradient Flow Dynamics
Our implicit regularization results are complementary to recent work arXiv:1906. 07842, done independently, which showed that initialization scale critically controls implicit regularization via a kernel-based argument.
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples
Inspired by theoretical work on linear full-width convolutional models, we hypothesize that the local (i. e. bounded-width) convolutional operations commonly used in current neural networks are implicitly biased to learn high frequency features, and that this is one of the root causes of high frequency adversarial examples.
A Functional Characterization of Randomly Initialized Gradient Descent in Deep ReLU Networks
Despite their popularity and successes, deep neural networks are poorly understood theoretically and treated as 'black box' systems.
LEARNING DIFFICULT PERCEPTUAL TASKS WITH HODGKIN-HUXLEY NETWORKS
We also demonstrate for the first time that a network with excitatory and inhibitory neurons and nonnegative synapse strengths can successfully solve computer vision problems.
Deep Learning-Enhanced Variational Monte Carlo Method for Quantum Many-Body Physics
Artificial neural networks have been successfully incorporated into variational Monte Carlo method (VMC) to study quantum many-body systems.
Strongly Correlated Electrons Disordered Systems and Neural Networks
RelGAN: Relational Generative Adversarial Networks for Text Generation
Generative adversarial networks (GANs) have achieved great success at generating realistic images.
Ranked #3 on
Text Generation
on EMNLP2017 WMT
A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model
This conjugate prior yields a new regularizer based on paths rendered in the generative model for training CNNs-the Rendering Path Normalization (RPN).
Towards a Better Understanding and Regularization of GAN Training Dynamics
Generative adversarial networks (GANs) are notoriously difficult to train and the reasons underlying their (non-)convergence behaviors are still not completely understood.
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Backpropagation-based visualizations have been proposed to interpret convolutional neural networks (CNNs), however a theory is missing to justify their behaviors: Guided backpropagation (GBP) and deconvolutional network (DeconvNet) generate more human-interpretable but less class-sensitive visualizations than saliency map.
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning
Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks.
Overcomplete Frame Thresholding for Acoustic Scene Analysis
In this work, we derive a generic overcomplete frame thresholding scheme based on risk minimization.
Training Neural Networks Without Gradients: A Scalable ADMM Approach
With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks.
