From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design
Engineering design is undergoing a transformative shift with the advent of AI, marking a new era in how we approach product, system, and service planning. Large language models have demonstrated...
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
Generative models have had a profound impact on vision and language, paving the way for a new era of multimodal generative applications. While these successes have inspired researchers to explore...
GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback
Generating executable CAD programs from images requires alignment between visual geometry and symbolic program representations, a capability that current methods fail to learn reliably as design...
Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
Inference-Time Scaling (ITS) improves language models by allocating more computation at generation time. Particle Filtering (PF) has emerged as a strong ITS method for complex mathematical...
Few-Shot Diffusion Models
Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable models with remarkable sample generation quality and training stability. These properties can be...
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned...
Diffusing the Optimal Topology: A Generative Optimization Approach
Topology Optimization seeks to find the best design that satisfies a set of constraints while maximizing system performance. Traditional iterative optimization methods like SIMP can be...
Feedback-Driven Vision-Language Alignment with Minimal Human Supervision
Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is often constrained by the need for extensive,...
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
A few-shot generative model should be able to generate data from a novel distribution by only observing a limited set of examples. In few-shot learning the model is trained on data from many sets...
Just Mix Once: Worst-group Generalization by Group Interpolation
Advances in deep learning theory have revealed how average generalization relies on superficial patterns in data. The consequences are brittle models with poor performance with shift in group...
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs
Event-based cameras are novel, efficient sensors inspired by the human vision system, generating an asynchronous, pixel-wise stream of data. Learning from such data is generally performed through...
Learning Common Representation from RGB and Depth Images
We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images. We revise the state of art based on the RGB and depth feature fusion,...
No Representation without Transformation
We extend the framework of variational autoencoders to represent transformations explicitly in the latent space. In the family of hierarchical graphical models that emerges, the latent space is...