919 citations
- Massachusetts Institute of TechnologyUS27 papers
- Harvard UniversityUS7 papers
- Institute of Cognitive and Brain SciencesUS7 papers
- Chinese Institute for Brain ResearchCN5 papers
- Tsinghua UniversityCN4 papers
- Beijing Academy of Artificial IntelligenceCN3 papers
- Beijing Normal UniversityCN3 papers
- Columbia UniversityUS3 papers
- Harvard–MIT Division of Health Sciences and TechnologyUS3 papers
- IIT@MITUS3 papers
- Italian Institute of TechnologyIT3 papers
- University of California, BerkeleyUS3 papers
6 papers · 1 filter
Deep-learning Based Modeling of Fault Detachment Stability for Power Grid
Haotian Cui, Xianggen Liu, Yanhao Huang
The project intends to model the stability of power system with a deep learning algorithm to the problem, aiming to delay the removal of the fault. The so-called "fail-delay cut-of…
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco +2
The paper characterizes classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these cond…
Deep vs. shallow networks : An approximation theory perspective
Hrushikesh Mhaskar, Tomaso Poggio
The paper briefy reviews several recent results on hierarchical architectures for learning from examples, that may formally explain the conditions under which Deep Convolutional Ne…
How Important is Weight Symmetry in Backpropagation?
Qianli Liao, Joel Z. Leibo, Tomaso Poggio
Gradient backpropagation (BP) requires symmetric feedforward and feedback connections -- the same weights must be used for forward and backward passes. This "weight transport probl…
GURLS: a Least Squares Library for Supervised Learning
Andrea Tacchetti, Pavan K Mallapragada, Matteo Santoro +1
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-s…
Learning Probability Measures with respect to Optimal Transport Metrics
Guillermo D. Canas, Lorenzo Rosasco
We study the problem of estimating, in the sense of optimal transport metrics, a measure which is assumed supported on a manifold embedded in a Hilbert space. By establishing a pre…