Search Results for author:
Found 13 papers, 1 papers with code
TL-PCA: Transfer Learning of Principal Component Analysis
Accordingly, our results for image datasets show that the representation of test data is improved by TL-PCA for dimensionality reduction where the learned subspace dimension is lower or higher than the number of target data examples.
How Do the Architecture and Optimizer Affect Representation Learning? On the Training Dynamics of Representations in Deep Neural Networks
We explore the representational similarity of DNN layers, each layer with respect to its own representations throughout the training process.
How Much Training Data is Memorized in Overparameterized Autoencoders? An Inverse Problem Perspective on Memorization Evaluation
Given a degraded training image, we define the recovery of the original training image as an inverse problem and formulate it as an optimization task.
Frozen Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks
We study how the generalization behavior of transfer learning is affected by the dataset size in the source and target tasks, the number of transferred layers that are kept frozen in the target DNN training, and the similarity between the source and target tasks.
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective
We also use decision boundary methods to visualize double descent phenomena.
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
The rapid recent progress in machine learning (ML) has raised a number of scientific questions that challenge the longstanding dogma of the field.
Double Descent and Other Interpolation Phenomena in GANs
We show that overparameterization can improve generalization performance and accelerate the training process.
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression
We define a transfer learning approach to the target task as a linear regression optimization with a regularization on the distance between the to-be-learned target parameters and the already-learned source parameters.
Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding
In this work we propose a framework for joint optimization of the MRI reconstruction and lossy compression, producing compressed representations of medical images that achieve improved trade-offs between quality and bit-rate.
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks
We analytically characterize the generalization error of the target task in terms of the salient factors in the transfer learning architecture, i. e., the number of examples available, the number of (free) parameters in each of the tasks, the number of parameters transferred from the source to target task, and the relation between the two tasks.
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
We study the linear subspace fitting problem in the overparameterized setting, where the estimated subspace can perfectly interpolate the training examples.
Postprocessing of Compressed Images via Sequential Denoising
In this work we propose a novel postprocessing technique for compression-artifact reduction.
Motion-Compensated Coding and Frame-Rate Up-Conversion: Models and Analysis
In this paper, we study the effect of frame-rate and compression bit-rate on block-based ME and MC as commonly utilized in inter-frame coding and frame-rate up conversion (FRUC).
