3 citations · 5 across the 11 of their papers we have counts for
11 papers
Accelerated Gradient Descent for Faster Convergence with Minimal Overhead
Manuel Graca, L. Miguel Silveira, Arlindo Oliveira +1
In this paper, we present CT-AGD (Curvature-Tuned Accelerated Gradient Descent), an optimization method for non-convex optimization problems in deep learning training tasks. CT-AGD…
Do Counterfactual Examples Complicate Adversarial Training?
Eric Yeats, Cameron Darwin, Eduardo Ortega +2
We leverage diffusion models to study the robustness-performance tradeoff of robust classifiers. Our approach introduces a simple, pretrained diffusion method to generate low-norm…
Deep Learning with Physics Priors as Generalized Regularizers
Frank Liu, Agniva Chowdhury
In various scientific and engineering applications, there is typically an approximate model of the underlying complex system, even though it contains both aleatoric and epistemic u…
Adversarial Estimation of Topological Dimension with Harmonic Score Maps
Eric Yeats, Cameron Darwin, Frank Liu +1
Quantification of the number of variables needed to locally explain complex data is often the first step to better understanding it. Existing techniques from intrinsic dimension es…
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach
Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana +5
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Rec…
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
Karthik Somayaji NS, Yu Wang, Malachi Schram +4
Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years due to the growing interest in deploying RL agents in real-world scenarios. A critical…
Disentangling Learning Representations with Density Estimation
Eric Yeats, Frank Liu, Hai Li
Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues. We present Gaussian Channel Autoencode…
NashAE: Disentangling Representations through Adversarial Covariance Minimization
Eric Yeats, Frank Liu, David Womble +1
We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e.g., no…
Gradient-based Novelty Detection Boosted by Self-supervised Binary Classification
Jingbo Sun, Li Yang, Jiaxin Zhang +4
Novelty detection aims to automatically identify out-of-distribution (OOD) data, without any prior knowledge of them. It is a critical step in data monitoring, behavior analysis an…
On the Stochastic Stability of Deep Markov Models
Ján Drgoňa, Sayak Mukherjee, Jiaxin Zhang +2
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the f…
Single-Net Continual Learning with Progressive Segmented Training (PST)
Xiaocong Du, Gouranga Charan, Frank Liu +1
There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learn…