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Found 17 papers, 7 papers with code
Learning Aggregation Functions
Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability.
Classification of cancer pathology reports: a large-scale comparative study
We report about the application of state-of-the-art deep learning techniques to the automatic and interpretable assignment of ICD-O3 topography and morphology codes to free-text cancer reports.
MARTHE: Scheduling the Learning Rate Via Online Hypergradients
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization.
Scheduling the Learning Rate Via Hypergradients: New Insights and a New Algorithm
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization.
Learning and Interpreting Multi-Multi-Instance Learning Networks
We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e. g., a document could be represented as a bag of sentences, which in turn are bags of words).
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning.
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning
In (Franceschi et al., 2018) we proposed a unified mathematical framework, grounded on bilevel programming, that encompasses gradient-based hyperparameter optimization and meta-learning.
Off the Beaten Track: Using Deep Learning to Interpolate Between Music Genres
We describe a system based on deep learning that generates drum patterns in the electronic dance music domain.
A Bridge Between Hyperparameter Optimization and Learning-to-learn
We consider a class of a nested optimization problems involving inner and outer objectives.
Shift Aggregate Extract Networks
We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs.
Forward and Reverse Gradient-Based Hyperparameter Optimization
We study two procedures (reverse-mode and forward-mode) for computing the gradient of the validation error with respect to the hyperparameters of any iterative learning algorithm such as stochastic gradient descent.
Cell identification in whole-brain multiview images of neural activation
We present a scalable method for brain cell identification in multiview confocal light sheet microscopy images.
Graph Invariant Kernels
Vertices of the subgraphs are then compared by a kernel that combines the similarity of their labels and the similarity of their structural role, using a suitable vertex invariant.
Ranked #1 on
Graph Classification
on FRANKENSTEIN
kLog: A Language for Logical and Relational Learning with Kernels
The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification.
Predicting the Geometry of Metal Binding Sites from Protein Sequence
Metal binding is important for the structural and functional characterization of proteins.
