Search Results for author:
Found 6 papers, 0 papers with code
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Context Forest for efficient object detection with large mixture models
We present Context Forest (ConF), a technique for predicting properties of the objects in an image based on its global appearance.
Joint calibration of Ensemble of Exemplar SVMs
We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum.
Object localization in ImageNet by looking out of the window
We propose a method for annotating the location of objects in ImageNet.
An active search strategy for efficient object class detection
First, we exploit context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set.
Associative embeddings for large-scale knowledge transfer with self-assessment
By transferring knowledge from the images that have bounding-box annotations to the others, our method is capable of automatically populating ImageNet with many more bounding-boxes and even pixel-level segmentations.
