Search Results for author:
Found 8 papers, 2 papers with code
Discrete and continuous representations and processing in deep learning: Looking forward
Motivated by these insights, in this paper we argue that combining discrete and continuous representations and their processing will be essential to build systems that exhibit a general form of intelligence.
Autoregressive Reasoning over Chains of Facts with Transformers
This paper proposes an iterative inference algorithm for multi-hop explanation regeneration, that retrieves relevant factual evidence in the form of text snippets, given a natural language question and its answer.
Structured (De)composable Representations Trained with Neural Networks
The paper proposes a novel technique for representing templates and instances of concept classes.
Convolutional Generation of Textured 3D Meshes
A key contribution of our work is the encoding of the mesh and texture as 2D representations, which are semantically aligned and can be easily modeled by a 2D convolutional GAN.
Distance-based Composable Representations with Neural Networks
We introduce a new deep learning technique that builds individual and class representations based on distance estimates to randomly generated contextual dimensions for different modalities.
Justifying Diagnosis Decisions by Deep Neural Networks
This textual representation is decoded into a diagnosis and the associated textual justification that will help a clinician evaluate the outcome.
Evaluating Textual Representations through Image Generation
The method is illustrated on a medical dataset where the correct representation of spatial information and shorthands are of particular importance.
Generating Continuous Representations of Medical Texts
During training the input to the system is a dataset of captions for medical X-Rays.
