56 citations · 117 across the 12 of their papers we have counts for
14 papers
Physics-Informed Learning of Feedback-Linearizing Representations
Pavlos Kallinikidis, Fengjun Yang, David Snyder +4
Feedback linearization is a powerful tool in nonlinear control, but finding the linearizing coordinate transform remains challenging. Feedback linearizing transforms are governed b…
FoeGlass: Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors
Sepehr Dehdashtian, Jacob H Seidman, Vishnu N Boddeti +1
Audio deepfake detection (ADD) models are critical for countering the malicious use of text-to-speech (TTS) models. Evaluating and strengthening ADD models requires developing data…
Post-hoc Selective Classification for Reliable Synthetic Image Detection
Kaixiang Zheng, Jacob H. Seidman
As synthetic images become increasingly realistic, reliable synthetic image detection techniques are of pressing need to prevent their misuse. Despite satisfactory in-distribution…
PolyJuice Makes It Real: Black-Box, Universal Red Teaming for Synthetic Image Detectors
Sepehr Dehdashtian, Mashrur M. Morshed, Jacob H. Seidman +2
Synthetic image detectors (SIDs) are a key defense against the risks posed by the growing realism of images from text-to-image (T2I) models. Red teaming improves SID's effectivenes…
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions
Xinyu Liao, Aoyang Qin, Jacob Seidman +3
Most existing generative models are limited to learning a single probability distribution from the training data and cannot generalize to novel distributions for unseen data. An ar…
CViT: Continuous Vision Transformer for Operator Learning
Sifan Wang, Jacob H Seidman, Shyam Sankaran +3
Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various…
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
Thomas Beckers, Jacob Seidman, Paris Perdikaris +1
Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which…
Variational Autoencoding Neural Operators
Jacob H. Seidman, Georgios Kissas, George J. Pappas +1
Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate modeling and physical systems. A natur…
Random Weight Factorization Improves the Training of Continuous Neural Representations
Sifan Wang, Hanwen Wang, Jacob H. Seidman +1
Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals. However, training them to captur…
NOMAD: Nonlinear Manifold Decoders for Operator Learning
Jacob H. Seidman, Georgios Kissas, Paris Perdikaris +1
Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid m…
Learning Operators with Coupled Attention
Georgios Kissas, Jacob Seidman, Leonardo Ferreira Guilhoto +3
Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general bla…
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado +1
The fragility of deep neural networks to adversarially-chosen inputs has motivated the need to revisit deep learning algorithms. Including adversarial examples during training is a…
A Control-Theoretic Approach to Analysis and Parameter Selection of Douglas-Rachford Splitting
Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado +1
Douglas-Rachford splitting and its equivalent dual formulation ADMM are widely used iterative methods in composite optimization problems arising in control and machine learning app…
A Chebyshev-Accelerated Primal-Dual Method for Distributed Optimization
Jacob H. Seidman, Mahyar Fazlyab, George J. Pappas +1
We consider a distributed optimization problem over a network of agents aiming to minimize a global objective function that is the sum of local convex and composite cost functions.…