By this definition even remembering the past images and planning the future events can't be performed other than in the present moment and in relation to current state of affairs (see also Lynds, 2003 Droege, 2009). We have argued previously (Fingelkurts et al., 2010) that phenomenal consciousness refers to a higher level of organization in the brain and captures all immediate and undeniable (from the first-person perspective) phenomena of subjective experiences (hearing, seeing, touching, feeling, embodiment, moving, and thinking) that present to any person right now (subjective present) and right here (subjective space). Broadly speaking, the human brain is the specific physical “location,” where the subjective mental reality and the objective neurobiological reality are intimately connected along a unified metastable continuum (Fingelkurts et al., 2009, 2013). According to this approach, subjective consciousness is a real phenomenon that is tightly anchored to a biological reality within the human brain. In this Opinion Article we shall build our argument based on the biological realism approach to consciousness proposed by Revonsuo ( 2006). Thus, the question of what could be the neurophysiological mechanisms responsible for these experiences should be addressed. In order to explain such features of consciousness as phenomenal unity and continuity within the current present along with a succession of discrete thoughts that give rise to feeling of the past and future, a reference to mechanisms outside the phenomenal realm is necessary (Revonsuo, 2003). Thus, phenomenal content seems to be minimally conscious if it is integrated into a single and coherent model of reality during a “virtual window” of presence (Metzinger, 2003 see also Brown, 1998 Varela, 1999 Smythies, 2003). Some researchers even argue that conscious awareness necessarily demands that mental content is somehow held “frozen” within a discrete progressive present moment (James, 1890 Lynds, 2003). Following Droege ( 2009) we could state that consciousness has a peculiar affinity for presence. We provide a practically efficient implementation of our approach, and use K-matrices in a Transformer network to attain 36% faster end-to-end inference speed on a language translation task.It is the every person's daily phenomenal experience that conscious states represent their contents as occurring now. In addition, K-matrices can capture latent structure in models: for a challenging permuted image classification task, adding a K-matrix to a standard convolutional architecture can enable learning the latent permutation and improve accuracy by over 8 points. K-matrices can also simplify hand-engineered pipelines-we replace filter bank feature computation in speech data preprocessing with a learnable kaleidoscope layer, resulting in only 0.4% loss in accuracy on the TIMIT speech recognition task. For example, replacing channel shuffles in ShuffleNet improves classification accuracy on ImageNet by up to 5%. We empirically validate that K-matrices can be automatically learned within end-to-end pipelines to replace hand-crafted procedures, in order to improve model quality. We consider a different approach: we introduce a family of matrices called kaleidoscope matrices (K-matrices) that provably capture any structured matrix with near-optimal space (parameter) and time (arithmetic operation) complexity. However, choosing which of the myriad structured transformations to use (and its associated parameterization) is a laborious task that requires trading off speed, space, and accuracy. Abstract: Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps.
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