91 research outputs found
Fast, invariant representation for human action in the visual system
Humans can effortlessly recognize others' actions in the presence of complex
transformations, such as changes in viewpoint. Several studies have located the
regions in the brain involved in invariant action recognition, however, the
underlying neural computations remain poorly understood. We use
magnetoencephalography (MEG) decoding and a dataset of well-controlled,
naturalistic videos of five actions (run, walk, jump, eat, drink) performed by
different actors at different viewpoints to study the computational steps used
to recognize actions across complex transformations. In particular, we ask when
the brain discounts changes in 3D viewpoint relative to when it initially
discriminates between actions. We measure the latency difference between
invariant and non-invariant action decoding when subjects view full videos as
well as form-depleted and motion-depleted stimuli. Our results show no
difference in decoding latency or temporal profile between invariant and
non-invariant action recognition in full videos. However, when either form or
motion information is removed from the stimulus set, we observe a decrease and
delay in invariant action decoding. Our results suggest that the brain
recognizes actions and builds invariance to complex transformations at the same
time, and that both form and motion information are crucial for fast, invariant
action recognition
GURLS: a Toolbox for Regularized Least Squares Learning
We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm. The toolbox takes advantage of all the favorable properties of least squares and is tailored to deal in particular with multi-category/multi-label problems. One of the main advantages of GURLS is that it allows training and tuning a multi-category classifier at essentially the same cost of one single binary classifier. The toolbox provides a set of basic functionalities including different training strategies and routines to handle computations with very large matrices by means of both memory-mapped storage and distributed task execution. The system is modular and can serve as a basis for easily prototyping new algorithms. The toolbox is available for download, easy to set-up and use
The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).
This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream -- from V1, V2, V4 and to IT -- is to discount image transformations, after learning them during development
A Neural Architecture for Designing Truthful and Efficient Auctions
Auctions are protocols to allocate goods to buyers who have preferences over
them, and collect payments in return. Economists have invested significant
effort in designing auction rules that result in allocations of the goods that
are desirable for the group as a whole. However, for settings where
participants' valuations of the items on sale are their private information,
the rules of the auction must deter buyers from misreporting their preferences,
so as to maximize their own utility, since misreported preferences hinder the
ability for the auctioneer to allocate goods to those who want them most.
Manual auction design has yielded excellent mechanisms for specific settings,
but requires significant effort when tackling new domains. We propose a deep
learning based approach to automatically design auctions in a wide variety of
domains, shifting the design work from human to machine. We assume that
participants' valuations for the items for sale are independently sampled from
an unknown but fixed distribution. Our system receives a data-set consisting of
such valuation samples, and outputs an auction rule encoding the desired
incentive structure. We focus on producing truthful and efficient auctions that
minimize the economic burden on participants. We evaluate the auctions designed
by our framework on well-studied domains, such as multi-unit and combinatorial
auctions, showing that they outperform known auction designs in terms of the
economic burden placed on participants
GURLS: A Least Squares Library for Supervised Learning
We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non- specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines for efficient model selection. The library is particularly well suited for multi-output problems (multi-category/multi-label). GURLS is currently available in two independent implementations: Matlab and C++. It takes advantage of the favorable properties of regularized least squares algorithm to exploit advanced tools in linear algebra. Routines to handle computations with very large matrices by means of memory-mapped storage and distributed task execution are available. The package is distributed under the BSD license and is available for download at https://github.com/LCSL/GURLS
Unsupervised learning of invariant representations
The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (. n\u2192 1e). The next phase is likely to focus on algorithms capable of learning from very few labeled examples (. n\u21921), like humans seem able to do. We propose an approach to this problem and describe the underlying theory, based on the unsupervised, automatic learning of a "good" representation for supervised learning, characterized by small sample complexity. We consider the case of visual object recognition, though the theory also applies to other domains like speech. The starting point is the conjecture, proved in specific cases, that image representations which are invariant to translation, scaling and other transformations can considerably reduce the sample complexity of learning. We prove that an invariant and selective signature can be computed for each image or image patch: the invariance can be exact in the case of group transformations and approximate under non-group transformations. A module performing filtering and pooling, like the simple and complex cells described by Hubel and Wiesel, can compute such signature. The theory offers novel unsupervised learning algorithms for "deep" architectures for image and speech recognition. We conjecture that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and selective for recognition-and show how this representation may be continuously learned in an unsupervised way during development and visual experienc
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