Its primary process is to train multiple layers of artificial neural networks to find abstractions in domain-specific datasets. As a promising supplement to existing power- and bandwidth-constrained digital neural networks, light field neural network has various potential applications: brain-inspired optical computation, high-bandwidth power-efficient neural network inference, and light-speed programmable lens/displays/detectors that operate in visible light.ĭeep learning has recently experienced tremendous success in delivering impressive results in various domains, including computer vision, medical analysis, natural language processing, and automatic vehicles 1, 2. It realizes a programmable incoherent optical neural network, a well-known challenge that delivers light-speed, high-bandwidth, and power-efficient neural network inference via processing parallel visible light signals in the free space. In addition, the functional learning paradigm is numerically and physically verified with an original light field neural network (LFNN). It offers a methodology to build hardware without handcrafted design, strict fabrication, and precise assembling, thus forging paths for hardware design, chip manufacturing, physical neuron training, and system control. The paradigm targets training non-differentiable hardware, and therefore solves many interdisciplinary challenges at once: the precise modeling and control of high-dimensional systems, the on-site calibration of multimodal hardware imperfectness, and the end-to-end training of non-differentiable and modeless physical neurons through implicit gradient propagation. This is a red-letter day! * z64555 erases "Thursday" and rewrites it in red ink TIL the entire homing code is held up by shoestrings and duct tape, basically.This research proposes a deep-learning paradigm, termed functional learning (FL), to physically train a loose neuron array, a group of non-handcrafted, non-differentiable, and loosely connected physical neurons whose connections and gradients are beyond explicit expression. "God damn, how did this ever work at all?!" (.) so more than two hours but once again we have reached the inevitable conclusion How did this code ever work in the first place!? Welcome to OpenGL, where standards compliance is optional, and error reporting inconsistent It was all working perfectly until I actually tried it on an actual mission. Everything points to "this should work fine", and yet it's clearly not working. (the very next day) this ****ing code did it to me again "That doesn't really make sense to me, but I'll assume it was being done for a reason." **** ME THE REASON IS PEOPLE ARE STUPID ESPECIALLY ME God damn, I do not understand how this is breaking. Because the "reason" often turns out to be "nobody noticed it was wrong". "I am one of the best FREDders on Earth" -General Battuta literary criticism is vladimir putin "There's probably a reason the code is the way it is" is a very dangerous line of thought. When you gaze long into BMPMAN, BMPMAN also gazes into you. schrödinbug (noun) - a bug that manifests itself in running software after a programmer notices that the code should never have worked in the first place. Ph'nglui mglw'nafh Codethulhu GitHub wgah'nagl fhtagn. This is a red-letter day! * z64555 erases "Thursday" and rewrites it in red ink TIL the entire homing code is held up by shoestrings and duct tape, basically. The FreeSpace Universe Reference Project.
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