Yair Schiff

According to our database, Yair Schiff authored at least 16 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling.

Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov

CoRR, 2024

DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems.

Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez

CoRR, 2024

2023

Auditing and Generating Synthetic Data with Controllable Trust Trade-offs.

Brian Belgodere, Pierre L. Dognin, Adam Ivankay, Igor Melnyk, Youssef Mroueh, Aleksandra Mojsilovic, Jirí Navrátil, Apoorva Nitsure, Inkit Padhi, Mattia Rigotti, Jerret Ross, Yair Schiff, Radhika Vedpathak, Richard A. Young

CoRR, 2023

InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models.

Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov

Proceedings of the International Conference on Machine Learning, 2023

Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows.

Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov

Proceedings of the International Conference on Machine Learning, 2023

Semi-Parametric Inducing Point Networks and Neural Processes.

Richa Rastogi, Yair Schiff, Alon Hacohen, Zhaozhi Li, Ian Lee, Yuntian Deng, Mert R. Sabuncu, Volodymyr Kuleshov

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning with Stochastic Orders.

Carles Domingo-Enrich, Yair Schiff, Youssef Mroueh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks.

David Alvarez-Melis, Yair Schiff, Youssef Mroueh

Trans. Mach. Learn. Res., 2022

Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge.

Pierre L. Dognin, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, Richard A. Young, Brian Belgodere

J. Artif. Intell. Res., 2022

Cloud-Based Real-Time Molecular Screening Platform with MolFormer.

Brian Belgodere, Vijil Chenthamarakshan, Payel Das, Pierre L. Dognin, Toby Kurien, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, Richard A. Young

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations.

Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Gi and Pal Scores: Deep Neural Network Generalization Statistics.

Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen

CoRR, 2021

Predicting Deep Neural Network Generalization with Perturbation Response Curves.

Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Tabular Transformers for Modeling Multivariate Time Series.

Inkit Padhi, Yair Schiff, Igor Melnyk, Mattia Rigotti, Youssef Mroueh, Pierre L. Dognin, Jerret Ross, Ravi Nair, Erik Altman

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

Alleviating Noisy Data in Image Captioning with Cooperative Distillation.

Pierre L. Dognin, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff

CoRR, 2020

Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics.

Yair Schiff, Vijil Chenthamarakshan, Karthikeyan Natesan Ramamurthy, Payel Das

CoRR, 2020