Search Results for author:
Found 14 papers, 5 papers with code
Permutation-based Causal Inference Algorithms with Interventions
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.
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available.
Memorization in Overparameterized Autoencoders
The ability of deep neural networks to generalize well in the overparameterized regime has become a subject of significant research interest.
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making.
Methodology
Telling Left from Right: Learning Spatial Correspondence of Sight and Sound
Self-supervised audio-visual learning aims to capture useful representations of video by leveraging correspondences between visual and audio inputs.
Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
Optimal Transport using GANs for Lineage Tracing
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).
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis
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.
Defending Multimodal Fusion Models against Single-Source Adversaries
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.
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
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
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
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
Novel-View Acoustic Synthesis from 3D Reconstructed Rooms
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.
FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple Super-Resolution Pipeline
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.
