3 accepted papers
We tackle the problem of learning object detectors without supervision. Differently from weakly-supervised object detection, we do not assume image-level class labels. Instead, we extract a supervisory signal from audio-visual data, using the audio component to "teach" the object detector. While thi…
The exponentially large sample space of general binary probabilistic models renders intractable standard operations such as exact marginalization, inference, and normalization. Typically, researchers deal with these distributions via deterministic approximations, the class of belief propagation me…
We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy. The proposed framework relies on projections via structured matrices that we call Structured Spinners, which are formed as products of three structured matrix-blocks…