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Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets
(Center for Brains, Minds and Machines (CBMM), arXiv, 2017-03-13)
The complexity of a learning task is increased by transformations in the input space that preserve class identity. Visual object recognition for example is affected by changes in viewpoint, scale, illumination or planar ...
Detecting Semantic Parts on Partially Occluded Objects
(Center for Brains, Minds and Machines (CBMM), 2017-09-04)
In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is ...
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
(Center for Brains, Minds and Machines (CBMM), arXiv, 2017-04-03)
Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images to be classified. Here we explore the ...
Musings on Deep Learning: Properties of SGD
(Center for Brains, Minds and Machines (CBMM), 2017-04-04)
[previously titled "Theory of Deep Learning III: Generalization Properties of SGD"] In Theory III we characterize with a mix of theory and experiments the generalization properties of Stochastic Gradient Descent in ...
Symmetry Regularization
(Center for Brains, Minds and Machines (CBMM), 2017-05-26)
The properties of a representation, such as smoothness, adaptability, generality, equivari- ance/invariance, depend on restrictions imposed during learning. In this paper, we propose using data symmetries, in the sense of ...
3D Object-Oriented Learning: An End-to-end Transformation-Disentangled 3D Representation
(2017-12-31)
We provide more detailed explanation of the ideas behind a recent paper on “Object-Oriented Deep Learning” [1] and extend it to handle 3D inputs/outputs. Similar to [1], every layer of the system takes in a list of ...
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
(Center for Brains, Minds and Machines (CBMM), arXiv, 2017-03-01)
While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning—leveraging unlabeled examples to learn about the structure of a domain — remains ...
Theory II: Landscape of the Empirical Risk in Deep Learning
(Center for Brains, Minds and Machines (CBMM), arXiv, 2017-03-30)
Previous theoretical work on deep learning and neural network optimization tend to focus on avoiding saddle points and local minima. However, the practical observation is that, at least for the most successful Deep ...
On the Forgetting of College Academice: at "Ebbinghaus Speed"?
(Center for Brains, Minds and Machines (CBMM), 2017-06-20)
How important are Undergraduate College Academics after graduation? How much do we actually remember after we leave the college classroom, and for how long? Taking a look at major University ranking methodologies one can ...
Theory of Deep Learning IIb: Optimization Properties of SGD
(Center for Brains, Minds and Machines (CBMM), 2017-12-27)
In Theory IIb we characterize with a mix of theory and experiments the optimization of deep convolutional networks by Stochastic Gradient Descent. The main new result in this paper is theoretical and experimental evidence ...