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Found 24 papers, 12 papers with code
Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer
The long-coveted task of reconstructing 3D geometry from images is still a standing problem.
Uncertainty-Aware Principal Component Analysis
We present a technique to perform dimensionality reduction on data that is subject to uncertainty.
AlgoNet: $C^\infty$ Smooth Algorithmic Neural Networks
These networks integrate smooth versions of classic algorithms into the topology of neural networks.
Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections
We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic.
$C^\infty$ Smooth Algorithmic Neural Networks for Solving Inverse Problems
Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.
Procedural Urban Forestry
The placement of vegetation plays a central role in the realism of virtual scenes.
Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning
Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set.
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision
Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints.
Propagating Distributions through Neural Networks
We propose a new approach to propagating probability distributions through neural networks.
A Sampling-Free Approximation of Gaussian Variational Auto-Encoders
We propose a sampling-free approximate formulation of Gaussian variational auto-encoders.
Learning with Algorithmic Supervision via Continuous Relaxations
The integration of algorithmic components into neural architectures has gained increased attention recently, as it allows training neural networks with new forms of supervision such as ordering constraints or silhouettes instead of using ground truth labels.
Style Agnostic 3D Reconstruction via Adversarial Style Transfer
Recently introduced differentiable renderers can be leveraged to learn the 3D geometry of objects from 2D images, but those approaches require additional supervision to enable the renderer to produce an output that can be compared to the input image.
Monotonic Differentiable Sorting Networks
We introduce a family of sigmoid functions and prove that they produce differentiable sorting networks that are monotonic.
GenDR: A Generalized Differentiable Renderer
In this work, we present and study a generalized family of differentiable renderers.
Differentiable Top-k Classification Learning
In this work, we relax this assumption and optimize the model for multiple k simultaneously instead of using a single k. Leveraging recent advances in differentiable sorting and ranking, we propose a differentiable top-k cross-entropy classification loss.
Ranked #57 on
Image Classification
on ImageNet
Deep Differentiable Logic Gate Networks
Recently, research has increasingly focused on developing efficient neural network architectures.
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing computational overhead, assuming a batch size smaller than the number of neurons.
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models.
3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking
To the best of our knowledge we are the first to present a framework for 2D/3D animal posture and trajectory tracking that works in both indoor and outdoor environments for up to 10 individuals.
Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges
We quantitatively show the value of exposing the beam search tree and present five detailed analysis scenarios addressing the identified challenges.
Uncertainty Quantification via Stable Distribution Propagation
We propose a new approach for propagating stable probability distributions through neural networks.
generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation
Large language models (LLMs) are widely deployed in various downstream tasks, e. g., auto-completion, aided writing, or chat-based text generation.
Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization
Based on the adapters broken apart for separate training content and style, we then make the entity parameter space by reconstructing the content and style PLPs matrices, followed by fine-tuning the combined adapter to generate the target object with the desired appearance.
Is that really a question? Going beyond factoid questions in NLP
Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer.
