no code implementations • NeurIPS 2017 • Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler
Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale.
no code implementations • ICML 2018 • Karren Yang, Abigail Katcoff, Caroline Uhler
We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available.
no code implementations • ICML Workshop Deep_Phenomen 2019 • Adityanarayanan Radhakrishnan, Karren Yang, Mikhail Belkin, Caroline Uhler
The ability of deep neural networks to generalize well in the overparameterized regime has become a subject of significant research interest.
no code implementations • CVPR 2020 • Karren Yang, Bryan Russell, Justin Salamon
Self-supervised audio-visual learning aims to capture useful representations of video by leveraging correspondences between visual and audio inputs.
no code implementations • CVPR 2021 • Karren Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
no code implementations • CVPR 2021 • Karren Yang, Wan-Yi Lin, Manash Barman, Filipe Condessa, Zico Kolter
Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information between modalities.
no code implementations • 27 Mar 2023 • Karren Yang, Ting-yao Hu, Jen-Hao Rick Chang, Hema Swetha Koppula, Oncel Tuzel
Here, we ask two fundamental questions about this strategy: when is synthetic data effective for personalization, and why is it effective in those cases?
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 18 Sep 2023 • Hsuan Su, Ting-yao Hu, Hema Swetha Koppula, Raviteja Vemulapalli, Jen-Hao Rick Chang, Karren Yang, Gautam Varma Mantena, Oncel Tuzel
In this paper, we propose a new strategy for adapting ASR models to new target domains without any text or speech from those domains.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 15 Dec 2023 • Chien-Yu Lin, Qichen Fu, Thomas Merth, Karren Yang, Anurag Ranjan
Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook.
1 code implementation • 23 Jul 2020 • Neha Prasad, Karren Yang, Caroline Uhler
In this paper, we present Super-OT, a novel approach to computational lineage tracing that combines a supervised learning framework with optimal transport based on Generative Adversarial Networks (GANs).
1 code implementation • 15 Jun 2020 • Karren Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
3 code implementations • 27 Feb 2019 • Raj Agrawal, Chandler Squires, Karren Yang, Karthik Shanmugam, Caroline Uhler
Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making.
Methodology
1 code implementation • 23 Oct 2023 • Byeongjoo Ahn, Karren Yang, Brian Hamilton, Jonathan Sheaffer, Anurag Ranjan, Miguel Sarabia, Oncel Tuzel, Jen-Hao Rick Chang
Given audio recordings from 2-4 microphones and the 3D geometry and material of a scene containing multiple unknown sound sources, we estimate the sound anywhere in the scene.
1 code implementation • CVPR 2022 • Karren Yang, Dejan Markovic, Steven Krenn, Vasu Agrawal, Alexander Richard
Since facial actions such as lip movements contain significant information about speech content, it is not surprising that audio-visual speech enhancement methods are more accurate than their audio-only counterparts.