313 research outputs found
Fast and Guaranteed Tensor Decomposition via Sketching
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in
statistical learning of latent variable models and in data mining. In this
paper, we propose fast and randomized tensor CP decomposition algorithms based
on sketching. We build on the idea of count sketches, but introduce many novel
ideas which are unique to tensors. We develop novel methods for randomized
computation of tensor contractions via FFTs, without explicitly forming the
tensors. Such tensor contractions are encountered in decomposition methods such
as tensor power iterations and alternating least squares. We also design novel
colliding hashes for symmetric tensors to further save time in computing the
sketches. We then combine these sketching ideas with existing whitening and
tensor power iterative techniques to obtain the fastest algorithm on both
sparse and dense tensors. The quality of approximation under our method does
not depend on properties such as sparsity, uniformity of elements, etc. We
apply the method for topic modeling and obtain competitive results.Comment: 29 pages. Appeared in Proceedings of Advances in Neural Information
Processing Systems (NIPS), held at Montreal, Canada in 201
Tactile-Filter: Interactive Tactile Perception for Part Mating
Humans rely on touch and tactile sensing for a lot of dexterous manipulation
tasks. Our tactile sensing provides us with a lot of information regarding
contact formations as well as geometric information about objects during any
interaction. With this motivation, vision-based tactile sensors are being
widely used for various robotic perception and control tasks. In this paper, we
present a method for interactive perception using vision-based tactile sensors
for a part mating task, where a robot can use tactile sensors and a feedback
mechanism using a particle filter to incrementally improve its estimate of
objects (pegs and holes) that fit together. To do this, we first train a deep
neural network that makes use of tactile images to predict the probabilistic
correspondence between arbitrarily shaped objects that fit together. The
trained model is used to design a particle filter which is used twofold. First,
given one partial (or non-unique) observation of the hole, it incrementally
improves the estimate of the correct peg by sampling more tactile observations.
Second, it selects the next action for the robot to sample the next touch (and
thus image) which results in maximum uncertainty reduction to minimize the
number of interactions during the perception task. We evaluate our method on
several part-mating tasks with novel objects using a robot equipped with a
vision-based tactile sensor. We also show the efficiency of the proposed action
selection method against a naive method. See supplementary video at
https://www.youtube.com/watch?v=jMVBg_e3gLw .Comment: Accepted at RSS202
3D View Prediction Models of the Dorsal Visual Stream
Deep neural network representations align well with brain activity in the
ventral visual stream. However, the primate visual system has a distinct dorsal
processing stream with different functional properties. To test if a model
trained to perceive 3D scene geometry aligns better with neural responses in
dorsal visual areas, we trained a self-supervised geometry-aware recurrent
neural network (GRNN) to predict novel camera views using a 3D feature memory.
We compared GRNN to self-supervised baseline models that have been shown to
align well with ventral regions using the large-scale fMRI Natural Scenes
Dataset (NSD). We found that while the baseline models accounted better for
ventral brain regions, GRNN accounted for a greater proportion of variance in
dorsal brain regions. Our findings demonstrate the potential for using
task-relevant models to probe representational differences across visual
streams.Comment: 2023 Conference on Cognitive Computational Neuroscienc
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