Columnar Machine: Fast estimation of structured sparse codes

Colors of Autism Spectrum: A Single Paradigm Explains the Heterogeneity in Autism Spectrum Disorder

“…This structure offers fast feedforward estimation of the groups that are to be selected (Lőrincz et al, 2016). Lőrincz et al (2016) considered structured sparse representation as a model of minicolumnar organization. This minicolumnar organization is another potential source of problems in autism.…”

“…In turn, problems with top-down processing may involve impairments in component formation and, in turn, impairments in bottom-up computations. In any autoencoder model, the success of top-down processing depends on the efficiency of memory consolidation, which is a prerequisite for successful bottom-up processing since top-down encoding is the first step in sparse representations and is followed by sparse representation-supervised fast bottom-up estimations (Sprechmann, Bronstein, and Sapiro, 2015;Lőrincz et al, 2016). In turn, ADHD impairments of top-down processing may contribute to ASD symptoms.…”

“…The applied strategy should be context dependent, and as such, it is restricted if the quality of formed components is poor. In turn, the presence or absence of components or the quality of the learned components does influence cognitive strategy via the feedforward estimation of the available sparse components (Sprechmann, Bronstein, and Sapiro, 2015) and (Lőrincz et al, 2016). In addition, if holistic, i.e., more complex, recognition is impaired, then component-based recognition followed by inferences may become typical that helps to train the holistic learning process as illustrated in (Lőrincz et al, 2018).…”

“…Unique solutions are warranted by sparsification. Slow operation is a drawback, but fast feedforward estimation of the relevant groups can speed up operations (Lőrincz et al, 2016). If the number of groups required for proper autoencoding is larger than the dimension of the input (if minicolumns are small), then the operation slows down.…”

Affine Transformation Based Ontology Sparse Vector Learning Algorithm

“…When it comes to the field of chemical and biology, the situation may become very complex since we need to deal with high dimensional data or big data. Under this background, sparse vector learning algorithms are introduced in biology and chemical ontology computation (see Afzali et al [17], Khormuji, and Bazrafkan [18], Ciaramella and Borzi [19], Lorincz et al [20], Saadat et al [21], Yamamoto et al [22], Lorintiu et al [23], Mesnil and Ruzzene [24], Gopi et al [25], and Dowell and Pinson [26] for more details). For example, if we aim to find what kind of genes causes a certain genetic disease, there are millions of genes in human's bodies and the computation task is complex and tough.…”