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Found 9 papers, 4 papers with code
Icy: A benchmark for measuring compositional inductive bias of emergent communication models
We present a benchmark \textsc{Icy} for measuring the compositional inductive bias of models in the context of emergent communications.
TexRel: a Green Family of Datasets for Emergent Communication with Relations
We propose a new dataset TexRel as a playground for the study of emergent communications, in particular for relations.
TexRel: a Green Family of Datasets for Emergent Communications on Relations
We propose a new dataset TexRel as a playground for the study of emergent communications, in particular for relations.
Neural networks can understand compositional functions that humans do not, in the context of emergent communication
We show that it is possible to craft transformations that, applied to compositional grammars, result in grammars that neural networks can learn easily, but humans do not.
Compositionality Through Language Transmission, using Artificial Neural Networks
We propose an architecture and process for using the Iterated Learning Model ("ILM") for artificial neural networks.
Dialog Intent Induction with Deep Multi-View Clustering
We introduce the dialog intent induction task and present a novel deep multi-view clustering approach to tackle the problem.
cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL
This paper presents cltorch, a hardware-agnostic backend for the Torch neural network framework.
Fast Parallel SVM using Data Augmentation
As one of the most popular classifiers, linear SVMs still have challenges in dealing with very large-scale problems, even though linear or sub-linear algorithms have been developed recently on single machines.
Gibbs Max-margin Topic Models with Data Augmentation
Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected prediction rule.
