54 citations · 82 across the 11 of their papers we have counts for
11 papers
Encoder-Decoder Diffusion Language Models for Efficient Training and Inference
Marianne Arriola, Yair Schiff, Hao Phung +2
Discrete diffusion models enable parallel token sampling for faster inference than autoregressive approaches. However, prior diffusion models use a decoder-only architecture, which…
RanDeS: Randomized Delta Superposition for Multi-Model Compression
Hangyu Zhou, Aaron Gokaslan, Volodymyr Kuleshov +1
From a multi-model compression perspective, model merging enables memory-efficient serving of multiple models fine-tuned from the same base, but suffers from degraded performance d…
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
Marianne Arriola, Aaron Gokaslan, Justin T. Chiu +5
Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeli…
Vid3D: Synthesis of Dynamic 3D Scenes using 2D Video Diffusion
Rishab Parthasarathy, Zachary Ankner, Aaron Gokaslan
A recent frontier in computer vision has been the task of 3D video generation, which consists of generating a time-varying 3D representation of a scene. To generate dynamic 3D scen…
Simple and Effective Masked Diffusion Language Models
Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff +5
While diffusion models excel at generating high-quality images, prior work reports a significant performance gap between diffusion and autoregressive (AR) methods in language model…
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan +3
Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics. However, modeling genomic sequences introduces challenges such as the need to mo…
On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI
Daniel McDuff, Tim Korjakow, Scott Cambo +9
Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use c…
Diffusion Models With Learned Adaptive Noise
Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa +1
Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images. Central to these algorithms is the diffusion process, a set of equations which ma…
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images
Aaron Gokaslan, A. Feder Cooper, Jasmine Collins +6
We assemble a dataset of Creative-Commons-licensed (CC) images, which we use to train a set of open diffusion models that are qualitatively competitive with Stable Diffusion 2 (SD2…
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
Yingheng Wang, Yair Schiff, Aaron Gokaslan +4
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning. Here, we p…
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
Jonathan Chang, Nishanth Kumar, Sean Hastings +6
Robots need to learn skills that can not only generalize across similar problems but also be directed to a specific goal. Previous methods either train a new skill for every differ…