8 citations · 24 across the 15 of their papers we have counts for
18 papers
How to Guide Your Language Flow
Rohit Dilip, Tianrong Chen, Yuyang Wang +3
We introduce a new method to guide flow matching models. Our approach, which we call probe guidance, uses the frozen internal states of an existing diffusion model to construct a g…
SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign
Jiarui Lu, Yuyang Wang, Yizhe Zhang +4
Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Devel…
Show Me Examples: Inferring Visual Concepts from Image Sets
Nick Stracke, Kolja Bauer, Stefan Andreas Baumann +3
Vision-language models (VLMs) can follow complex textual instructions, yet they struggle to reason from purely visual context. In particular, current models fail to infer shared co…
STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
Ying Shen, Tianrong Chen, Yuan Gao +6
Deep generative models have advanced rapidly across text and vision, motivating unified multimodal systems that can understand, reason over, and generate interleaved text-image seq…
Learning Long-term Motion Embeddings for Efficient Kinematics Generation
Nick Stracke, Kolja Bauer, Stefan Andreas Baumann +3
Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scene dynamics, exploring multip…
STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows
Jiatao Gu, Ying Shen, Tianrong Chen +6
Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Y…
Adapting Self-Supervised Representations as a Latent Space for Efficient Generation
Ming Gui, Johannes Schusterbauer, Timy Phan +4
We introduce Representation Tokenizer (RepTok), a generative modeling framework that represents an image using a single continuous latent token obtained from self-supervised vision…
SimpleFold: Folding Proteins is Simpler than You Think
Yuyang Wang, Jiarui Lu, Navdeep Jaitly +2
Protein folding models have achieved groundbreaking results typically via a combination of integrating domain knowledge into the architectural blocks and training pipelines. Noneth…
Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows
Ruixiang Zhang, Shuangfei Zhai, Jiatao Gu +6
Autoregressive models have driven remarkable progress in language modeling. Their foundational reliance on discrete tokens, unidirectional context, and single-pass decoding, while…
STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Jiatao Gu, Tianrong Chen, David Berthelot +7
We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer…
Normalizing Flows are Capable Generative Models
Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran +7
Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but ha…
INRFlow: Flow Matching for INRs in Ambient Space
Yuyang Wang, Anurag Ranjan, Josh Susskind +1
Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on irregular or unstructured data like 3D point clouds or…
CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models
Nick Stracke, Stefan Andreas Baumann, Joshua M. Susskind +2
Text-to-image generative models have become a prominent and powerful tool that excels at generating high-resolution realistic images. However, guiding the generative process of the…
Scalable Pre-training of Large Autoregressive Image Models
Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai +5
This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language…
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
Yuyang Wang, Ahmed A. Elhag, Navdeep Jaitly +2
We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by us…
Adaptivity and Modularity for Efficient Generalization Over Task Complexity
Samira Abnar, Omid Saremi, Laurent Dinh +8
Can transformers generalize efficiently on problems that require dealing with examples with different levels of difficulty? We introduce a new task tailored to assess generalizatio…
Pseudo-Generalized Dynamic View Synthesis from a Video
Xiaoming Zhao, Alex Colburn, Fangchang Ma +3
Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniqu…
Diffusion Probabilistic Fields
Peiye Zhuang, Samira Abnar, Jiatao Gu +3
Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative…