Causal Neural Paradox (Thought Curvature)
Proposed by: Jordan Micah Bennett on May 23, 2016
A particular manifold paradox emerges qua markov neural sequences’ [1] confluence abound non-trivial causal instruction.[2]
Some C∞(Rn), 'causal neural perturbation curvature' (of the Supermanifold genera), generates uniform symbols on the boundary of its Rn, 'causal neural manifold' (of the manifold genera), betwixt some Uα, 'causal neural atoms' of φi.
Ergo, the paradox axiomatizes.
Introduction[edit]
Deepmind's atari q architecture [1] encompasses non pooling convolutions, therein generating object shift sensitivity, whence the model maximizes some reward over said shifts together with separate changing states for each sampled t state; translation non invariance.
Separately, uetorch,[2] encodes an object trajectory behaviour physics learner, particularly on pooling layers; translation invariance.
It is quite observable, that the childhood neocortical framework pre-encodes certain causal physical laws in the neurons,[3] postulating in perceptual learning abstractions into non-childhood.
As such, it is perhaps exigent that non invariant fabric composes in the invariant, therein engendering time-space complex optimal causal, conscious artificial construction.
If this confluence is reasonable, is such paradoxical?
Partial paradox reduction[edit]
Paradoxical strings have been perturbed to reduce in factor variant/invariant manifold interaction paradigms (Bengio et al.,[4] Kihyuk et al.[5]), that effectively learn to disentangle varying factors.
However, such models relent factors betwixt causal embedding. See Non-partial paradox reduction
Ergo, the paradox, abound causal instruction/abstraction generation, on the temporal difference learning plane, persists.
Non-partial paradox reduction[edit]
∃ some translation invariant karp reducible quantities causal neural atoms -Uα, encoding φi. As such, some causal neural manifold - Rn, stipulates in causal neural atom terms, an interaction sequence.
Some translation variant construct causal neural perturbation curvature - C∞(Rn), generates nominally infinite descriptions in cnm, on some temporal difference horizon. This fabric generates pseudo-novel representations in cna.
Separately, Kihyuk et al.[6] laments a quite prompt, time-space complex optimal manifold construction paradigm, on the order of generic quantity priors/factors.
In contrast, abound the causal perturbation curvature C∞(Rn), some temporal difference horizon Vπ, enumerates distinct descriptions Rnπ, in causal neural manifold - Rn lemma, particularly on the order of non-generic causal neural atom - quantity priors/factors, φπ ∈ Uα.
Conclusion[edit]
The absorption of Rnπ in Vπ, on non-generic quantity priors/factors φiπ, via some C∞(Rn) curvature, descries a causal neural basis, betwixt Rnπ bound perceptual abstractions.
References[edit]
- ↑ 1.0 1.1 Human Level Control Through Deep Reinforcement Learning; Mnih et al
- ↑ 2.0 2.1 Learning Physical Intuition of Block Towers by Example; Lerer et al
- ↑ Observing the unexpected enhances infants’ learning and exploration; Stahl et al
- ↑ Disentangling Factors of Variation for Facial Expression Recognition; Bengio et al
- ↑ Learning to Disentangle Factors of Variation with Manifold Interaction; Yuting et al
- ↑ Efficient learning of sparse, distributed, convolutional feature representations for object recognition; Kihyuk et al
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