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Found 11 papers, 2 papers with code
A Review of Benchmarks for Visual Defect Detection in the Manufacturing Industry
The field of industrial defect detection using machine learning and deep learning is a subject of active research.
Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning
Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands.
Representation Transfer by Optimal Transport
We use optimal transport to quantify the match between two representations, yielding a distance that embeds some invariances inherent to the representation of deep networks.
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.
Explicit Induction Bias for Transfer Learning with Convolutional Networks
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.
Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases
This paper tackles the problem of endogenous link prediction for Knowledge Base completion.
Theory of Optimizing Pseudolinear Performance Measures: Application to F-measure
We study the theoretical properties of a subset of non-linear performance measures called pseudo-linear performance measures which includes $F$-measure, \emph{Jaccard Index}, among many others.
Optimizing F-Measures by Cost-Sensitive Classification
We present a theoretical analysis of F-measures for binary, multiclass and multilabel classification.
Sparsity by Worst-Case Penalties
We illustrate on real and artificial datasets that this accuracy is required to for the correctness of the support of the solution, which is an important element for the interpretability of sparsity-inducing penalties.
Support Vector Machines with a Reject Option
We consider the problem of binary classification where the classifier may abstain instead of classifying each observation.
