A neural scene representation framework enforcing 3D transformations.
problem Learning 3D scene representations from images without 3D supervision.
method Introducing a loss enforcing equivariance of the scene representation with 3D transformations.
result Real-time neural rendering with comparable results to models requiring minutes for inference.
Enhances neural rendering with geometry-aware attention.
problem Efficiently modeling complex 3D scenes.
method Introduces Epipolar Cross Attention (ECA) for non-local operations.
result Significant improvement in Generative Query Networks (GQN) performance.
The paper uses differentiable rendering to generate semantic counterexamples for improving neural network robustness.
problem Neural networks' brittleness to semantic transformations.
method Differentiable rendering for generating realistic images that model semantic changes, combined with adversarial machine learning attacks.
result Semantic counterexamples improve generalization, robustness, and transferability of neural networks.
A new metric for detecting out-of-distribution samples using neural rendering models.
problem Difficulty in detecting out-of-distribution samples with existing deep generative models.
method Derive metrics for out-of-distribution detection using a neural rendering model.
result Lower likelihood of latent variables is assigned to out-of-distribution samples.
Automates UI implementation from designer images.
problem Automating UI implementation from designer images.
method Generative model training and imitation learning.
result 92.5% accuracy on Android Button attribute inference.
Automates hair color digitization using imaging and deep learning.
problem Challenges in capturing and rendering realistic hair colors.
method Combines imaging, path-tracing, and self-supervised machine learning.
result Accurately captures and renders hair color with synthetic images.
DVAO predicts volumetric ambient occlusion for real-time volume rendering.
problem Predicting per-voxel ambient occlusion in volumetric data sets.
method Deep learning neural network that considers global information through transfer function.
result DVAO supports real-time volume interaction and generalizes to various modalities.
Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. In this paper, we propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Ca…
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" …
VectorNet predicts car behavior using vectorized HD maps and agent dynamics.
problem Predicting behavior in multi-agent systems with self-driving cars.
method VectorNet uses hierarchical graph neural networks on vectorized representations of HD maps and agent trajectories.
result VectorNet achieves comparable or better performance than state-of-the-art methods while using fewer parameters and less computational power.
DSRGAN learns independent structure and rendering without tuple supervision.
problem Learning disentangled representation for natural image generation without tuple supervision.
method Introducing an auxiliary domain with a common underlying-structure space, and designing a parallel generative network with a common Progressive Rendering Architecture.
result DSRGAN significantly outperforms state-of-the-art methods in disentanglability.
Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on per…
In this paper we present Percival, a browser-embedded, lightweight, deep learning-powered ad blocker. Percival embeds itself within the browser's image rendering pipeline, which makes it possible to intercept every image obtained during page execution and to perform blocking based on applying machine learning for image…
GrowNet uses shallow neural networks for gradient boosting, outperforming existing methods.
problem Improving gradient boosting performance through shallow neural networks.
method Unified gradient boosting framework with shallow neural networks as weak learners, incorporating corrective steps.
result GrowNet outperformed state-of-the-art boosting methods in classification, regression, and learning to rank tasks.
Neural Processes combine the strengths of neural networks and Gaussian processes to achieve both flexible learning and fast prediction in stochastic processes. However, a large class of problems comprises underlying temporal dependency structures in a sequence of stochastic processes that Neural Processes (NP) do not e…
NESYM combines AI and Earth models for new climate insights.
problem Replacing traditional Earth models with AI.
method Neural Earth System Modelling (NESYM) integrating AI and climate models.
result Artificial intelligence may render traditional models obsolete.
ROOTS learns to represent and render 3D scenes with object-centric models.
problem Learning to represent and render 3D scenes with object-centric compositionality.
method Probabilistic generative model for learning object representations and scene rendering from partial observations.
result The model can infer 3D object representations and render scenes from arbitrary viewpoints.
NeRF-VAE generates 3D scenes with geometric structure from few images.
problem Generating 3D scenes from few images with geometric consistency.
method Combines NeRF and VAE, incorporating shared geometric structure.
result NeRF-VAE can infer and render geometrically-consistent scenes from unseen environments.
Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Deconvolutional Generative Model (DGM), a new probabilistic generative model whose inference calculations correspond to those in a given CNN architecture. The DGM uses a CNN to design the prior distributio…
Describes rendering scenes in Nil geometry.
problem None explicitly stated in the abstract.
method Expository account of rendering real-time scenes in Nil geometry.
result Interesting geometric phenomena observed.
Proposes a model to generate 3D-aware images from 2D images.
problem Generating 3D-aware images from 2D images.
method Likelihood-based top-down model using Neural Radiance Fields and energy-based latent variables.
result Model can infer 3D object structures from 2D images and generate novel views.
Stabilized neural differential equations enforce constraints on dynamical systems.
problem Ensuring dynamical systems preserve known constraints like conservation laws.
method SNDEs with a stabilization term to enforce manifold constraints.
result SNDEs outperform existing methods and broaden constraint types.
This paper improves anomaly detection in lane rendering images for safer navigation.
problem Anomalies in lane rendering images can mislead drivers, posing safety risks.
method Proposes a four-phase pipeline using Transformer models, self-supervised pre-training, and fine-tuning.
result The pipeline enhances detection accuracy and reduces training time.
NanoFlow reduces parameter complexity in normalizing flows.
problem Efficient parameter complexity in flow-based models.
method Single neural density estimator with flow indication embedding.
result Sublinear parameter complexity achieved.
Effective training of deep neural networks suffers from two main issues. The first is that the parameter spaces of these models exhibit pathological curvature. Recent methods address this problem by using adaptive preconditioning for Stochastic Gradient Descent (SGD). These methods improve convergence by adapting to th…
R package innsight interprets deep neural networks predictions.
problem Interpreting predictions of deep neural networks.
method Unified and user-friendly framework implementing feature attribution methods for neural networks, independent of deep learning library.
result Offers a variety of visualization tools for tabular, signal, image data or a combination.
We present the OpenAI Remote Rendering Backend (ORRB), a system that allows fast and customizable rendering of robotics environments. It is based on the Unity3d game engine and interfaces with the MuJoCo physics simulation library. ORRB was designed with visual domain randomization in mind. It is optimized for cloud de…
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing. Since the precise statistical properties of neural activity are important in this context, many model…
AI recovers lost art from x-rays.
problem Reconstructing lost artwork under layers of x-ray imaging.
method Neural style transfer applied to x-radiographs.
result Reconstructed lost artwork visible through x-rays.
Improves spline quality and accuracy in computational microscopy.
problem Detecting slender, overlapping structures in microscopy images.
method Differentiable rendering approach for spline refinement.
result Achieves high reliability and sub-pixel accuracy.
We create real-time geodesic rendering for non-isotropic geometries.
problem Challenging visualization of non-isotropic geometries.
method Novel methods for real-time native geodesic rendering.
result Methods can be applied to visualization, machine learning, and video games.
While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal. Likewise, we demonstrate that the common practice of using very few …
Improves synthetic data for deep model training and adaptation.
problem Evaluating and improving synthetic data for deep learning models.
method Proposes a novel learned synthesis technique using generative models for shading and rendering, and uses an ensemble of models to generate datasets.
result Improves classifier performance on real data compared to state-of-the-art methods.
This paper improves neural tangent kernels for better generalization and local elasticity.
problem Performance gap between neural tangent kernels and real-world neural networks.
method Introduces label-aware kernels using Hoeffding decomposition.
result Models trained with proposed kernels simulate NNs better in terms of generalization and local elasticity.
Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (DCNs) have achieved great success in supervised tasks and as such have been widely employed in the semi-supervised learning. In this paper we…
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
A new neural network approach for diffusion on networks.
problem Inference and estimation of diffusion on network structures.
method Neural mean-field dynamics derived from Mori-Zwanzig formalism, approximated by learnable time convolution operators.
result Significantly outperforms existing approaches in accuracy and efficiency.
TPBS models improve robustness to overfitting with localized Dirichlet energy regularization.
problem Global Dirichlet energy-based regularization fails for TPBS models due to perfect interpolation.
method Propose local Dirichlet energy regularization and two inference estimators.
result TPBS models outperform neural networks in overfitting regimes and maintain competitive performance otherwise.
Neural EKF improves structural dynamics prediction.
problem Accurately predicting structural response for health monitoring.
method Neural Extended Kalman Filter (Neural EKF) for learning dynamics.
result Significant predictive capabilities demonstrated on simulated and real-world data.
Noise regularization improves CDE models without overfitting.
problem Overfitting in neural network-based conditional density estimation.
method Noise regularization method that adds random perturbations to data.
result Noise regularization significantly outperforms other methods across various datasets and models.
Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according t…
MPNPs use message passing to exploit relational structure in stochastic processes.
problem Lack of relational information in NPs limits their effectiveness in tasks with neighborhood rules.
method Introduce MPNPs that explicitly use relational structure through message passing.
result MPNPs outperform NPs in tasks with relational information, showing significant gains in few-shot learning.
New neural network models speed up Bayesian multilevel modeling.
problem Complex computational challenges in Bayesian multilevel modeling.
method Probabilistic neural network architectures that leverage multilevel model factorization.
result Efficient posterior inference on unseen datasets with near-instant results.
Study shows DNNs can recover functions with fewer samples than model parameters at overparameterization.
problem Determining reliable function recovery in overparameterized deep neural networks.
method Introducing 'local linear recovery' (LLR) and proving upper bounds on sample sizes for recovery.
result Upper bounds on optimistic sample sizes for function recovery in overparameterized DNNs are achieved.
Bayesian neural networks improve deep learning's accuracy and uncertainty estimation.
problem Overconfident predictions, adversarial attacks, and variability underestimation in deep models.
method Stochastic relaxation of feed-forward rectified neural networks with sparsity-promoting priors and Polya-Gamma data augmentation.
result Improved scalability and robustness to architectural design through approximate variational inference.
Meta-learn sparse Gaussian process inference for faster predictions.
problem Cubic computational cost of exact Gaussian process inference for many observations.
method Meta-learn sparse Gaussian process inference.
result Rapid prediction on new tasks with sparse Gaussian processes.
AR-GANs learn depth and DoF from unlabeled images using aperture rendering and focus cues.
problem Learning depth and DoF from unlabeled natural images with diverse viewpoints and shapes.
method Aperture rendering and focus cues to learn depth and DoF from unlabeled images.
result AR-GANs effectively learn depth and DoF from various datasets, including flower, bird, and face images.
Improved Gaussian Neural Processes for efficient multi-dimensional predictions.
problem Inability to model dependencies in outputs limits CNPs and NPs applicability.
method Proposes a new approach to model output dependencies using latent variables for maximum likelihood training, scalable to 2D and 3D data.
result Proposed models show good performance in synthetic experiments.