Search Results for author: Frank Liu

Found 10 papers, 2 papers with code

Deep Learning with Physics Priors as Generalized Regularizers

no code implementations14 Dec 2023 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 uncertainties.

Adversarial Estimation of Topological Dimension with Harmonic Score Maps

no code implementations11 Dec 2023 Eric Yeats, Cameron Darwin, Frank Liu, Hai Li

Quantification of the number of variables needed to locally explain complex data is often the first step to better understanding it.

Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach

no code implementations19 Oct 2023 Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Jan Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li

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.

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

no code implementations24 Aug 2023 Karthik Somayaji NS, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li

Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.

reinforcement-learning Reinforcement Learning (RL)

Disentangling Learning Representations with Density Estimation

1 code implementation8 Feb 2023 Eric Yeats, Frank Liu, Hai Li

Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues.

Density Estimation Disentanglement

NashAE: Disentangling Representations through Adversarial Covariance Minimization

1 code implementation21 Sep 2022 Eric Yeats, Frank Liu, David Womble, Hai Li

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 assumptions on the number or distribution of the individual latent variables to be extracted).

Disentanglement

On the Stochastic Stability of Deep Markov Models

no code implementations NeurIPS 2021 Ján Drgoňa, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar

Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems.

Representation Learning

SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders

no code implementations14 Jan 2020 Inseok Hwang, Jinho Lee, Frank Liu, Minsik Cho

Our intuition is that, the more similarity exists between the unknown data samples and the part of known data that an autoencoder was trained with, the better chances there could be that this autoencoder makes use of its trained knowledge, reconstructing output samples closer to the originals.

Data Augmentation

Single-Net Continual Learning with Progressive Segmented Training (PST)

no code implementations28 May 2019 Xiaocong Du, Gouranga Charan, Frank Liu, Yu Cao

Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference.

Continual Learning

Beautiful and damned. Combined effect of content quality and social ties on user engagement

no code implementations1 Nov 2017 Luca M. Aiello, Rossano Schifanella, Miriam Redi, Stacey Svetlichnaya, Frank Liu, Simon Osindero

Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement.

Recommendation Systems

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