Search Results for author:
Found 10 papers, 4 papers with code
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.
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.
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.
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).
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.
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.
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
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.
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.
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.
