Search Results for author:
Found 10 papers, 4 papers with code
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation
We show that neither DRO nor curating the training set should be construed as a complete solution for bias mitigation: in the same way that there is no universally robust training set, there is no universal way to setup a DRO problem and ensure a socially acceptable set of results.
Linear unit-tests for invariance discovery
There is an increasing interest in algorithms to learn invariant correlations across training environments.
Inductive Bias and Language Expressivity in Emergent Communication
Referential games and reconstruction games are the most common game types for studying emergent languages.
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers
Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer.
Structural Inductive Biases in Emergent Communication
In order to communicate, humans flatten a complex representation of ideas and their attributes into a single word or a sentence.
Towards Graph Representation Learning in Emergent Communication
Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces).
Bayesian Optimisation with Gaussian Processes for Premise Selection
Heuristics in theorem provers are often parameterised.
Spatial Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.
Completing partial recipes using item-based collaborative filtering to recommend ingredients
Increased public interest in healthy lifestyles has motivated the study of algorithms that encourage people to follow a healthy diet.
Dilated DenseNets for Relational Reasoning
Despite their impressive performance in many tasks, deep neural networks often struggle at relational reasoning.
