Browsing Center for Brains, Minds & Machines by Author "Liao, Qianli"
Now showing items 1-9 of 9
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Biologically-plausible learning algorithms can scale to large datasets
(Center for Brains, Minds and Machines (CBMM), arXiv.org, 2018-11-08)The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address ... -
Can a biologically-plausible hierarchy e ectively replace face detection, alignment, and recognition pipelines?
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-03-27)The standard approach to unconstrained face recognition in natural photographs is via a detection, alignment, recognition pipeline. While that approach has achieved impressive results, there are several reasons to be ... -
A Definition of General Problem Solving
(2020-07-13)What is general intelligence? What does it mean by general problem solving? We attempt to give a definition of general problem solving, characterize the common process of problem solving and provide a basic algorithm that ... -
Flexible Intelligence
(2020-06-18)We discuss the problem of flexibility in intelligence, a relatively little-studied topic in machine learning and AI. Flexibility can be understood as out-of-distribution generalization, and it can be achieved by converting ... -
Hierarchically Local Tasks and Deep Convolutional Networks
(Center for Brains, Minds and Machines (CBMM), 2020-06-24)The main success stories of deep learning, starting with ImageNet, depend on convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines. ... -
Implicit dynamic regularization in deep networks
(Center for Brains, Minds and Machines (CBMM), 2020-08-17)Square loss has been observed to perform well in classification tasks, at least as well as crossentropy. However, a theoretical justification is lacking. Here we develop a theoretical analysis for the square loss that also ... -
Theoretical Issues in Deep Networks
(Center for Brains, Minds and Machines (CBMM), 2019-08-17)While deep learning is successful in a number of applications, it is not yet well understood theoretically. A theoretical characterization of deep learning should answer questions about their approximation power, the ... -
Universal Format Conversions
(2020-06-05)Information is the fuel for intelligence. Any competitive intelligence system should be information hungry. “Formats” on the other hand, is the container for information. Accessing information without the ability to decipher ... -
Universal Metaphysics
(2019-12-31)The development of natural science especially physics allows us to understand to a large extent the material world. However, the world also contains a large amount of concepts that are non-material and abstract, which are ...
