Search Results for author:
Found 4 papers, 1 papers with code
Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Exploration
Given an environment (e. g., a simulator) for evaluating samples in a specified design space and a set of weighted evaluation metrics -- one can use Theta-Resonance, a single-step Markov Decision Process (MDP), to train an intelligent agent producing progressively more optimal samples.
Fully Convolutional Scene Graph Generation
This paper presents a fully convolutional scene graph generation (FCSGG) model that detects objects and relations simultaneously.
Speech-Image Semantic Alignment Does Not Depend on Any Prior Classification Tasks
Choosing appropriate neural architectures for encoders in the speech and image branches and using large datasets, one can obtain competitive recall rates without any reliance on any pre-trained initialization or feature extraction: $(speech, image)$ semantic alignment and $speech \rightarrow image$ and $image \rightarrow speech$ retrieval are canonical tasks worthy of independent investigation of their own and allow one to explore other questions---e. g., the size of the audio embedder can be reduced significantly with little loss of recall rates in $speech \rightarrow image$ and $image \rightarrow speech$ queries.
The Impact of Hole Geometry on Relative Robustness of In-Painting Networks: An Empirical Study
In this paper, we study the robustness of a given in-painting neural network against variations in hole geometry distributions.
