Sunday, October 10, 2021

The principle of inversion, Sign+ & Sign+ (c)RS

The principle of inversion, Sign+ & Sign+ (c)RS

In principle one uses a wave inverter circuit to invert a Waveform,
Such a circuit is called a wave inverter or converter; A diode matrix <>

Maths of a positive integer form allow all positive values, Invert the wave & All Negative.

So what do you propose is the purpose of wave inverting a waveform on an Integer Processor?

All Integer values of Positive Value subtracted + Inversion = Remainder Negative value,

In our case we are using the number to compute the differential in a L - R = Center Value.

Larger number - Smaller number = All values positive, Inversion leaves us with the remainder on the positive & Negative Value Set..

Rather than invert we sign (1 Bit) & therefore can subtract the value from a larger value without using Negative Values in a whole line,

Principally A Sin, Cosine pair are both positive; Or we can invert with a single Bit.

NON-FPU, All Integer & Hence for a codec & Display unit we can use all positive Values in out maths & most objectives involving subtracting waves;

We can displace for 0.0> Values of a sub 0 value such as 0.001

https://science.n-helix.com/2018/01/integer-floats-with-remainder-theory.html

https://science.n-helix.com/2021/02/multi-operation-maths.html

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

Useful here:
https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html
https://science.n-helix.com/2021/10/eccd-vr-3datmos-enhanced-codec.html

*****

Precision & Inversion - Waveform delta, Timing, Scaling, Inversion & reversion in timing circuits & Computation, Audio, Video & Visual Systems (c)RS

Usage cases include : Defining Audio , World beating power grids , Computer Chips, AMP's & PreAMPS & Power chargers & power supply or packages.

*

Such is called an inverted 4/3 Analog or digital Wave converter, Where in a Wave of high density is converted into a low frequency; Mostly about timing and precision,

Low to high conversion is mostly about smooth wave modulation and specifically for situations commanding Very precise tight waveforms of Factored precision on lower bit order processors & principally is used for timing clocks;

For example Red Laser light amplitude modulators & timers of the slow & thus high precision simple function with an almost impossible to beat TIME Precision,

Usage cases include : Defining Audio , World beating power grids , Computer Chips, AMP's & PreAMPS & Power chargers & power supply or packages.

"Inverted Driver Geometry (IDG), with the bass/mid driver sited above the treble unit rather than below. This aids time-alignment."

(c)RS

Examples :

Mission(tm) Accomplished A Classic British Speaker Brand : Wireless : https://www.forbes.com/sites/marksparrow/2021/09/30/mission-accomplished-as-classic-british-speaker-brand-goes-wireless/

(Principally, Because hay! in Napoli we like a good price)

Saturday, October 9, 2021

ECCD-VR-3DAtmos - Enhanced Codec Compression Digital VR

ECCD-VR-3DAtmos - Enhanced Codec Compression Digital VR 7(+16) 1(+2) (L + R) With combined Bitrate Centre channel (c)RS


For TOSLink, ARC, eARC, Bluetooth

TOSLink specifics are 384Kb/s , If we can manage 1Mb/s Many Codecs work as is.
Bluetooth has the same issues with Data rate & ARC also.

Bluetooth has a specific capacity of 10Mb,
But often 1Mb/s is Codec maximum with reasonable CPU usage.

Specifically, the Encoder & Decoder rate of 1mb/s capacity can do 7.1 with Atmos VR Channels from 16 to 38.

The VR channel capacity is achieved by combining Extended channels (L + R) With combined Bitrate Center channel,

The capacity to decode with interpolation &or Mathematical Dithering of the (L + R) Center,
Therefore extending virtual channels within the Dolby Atmos + DTS Standards.

(L + R) With combined Bitrate Center channel,

(L + /VR\ + R) Sub (L + /VR\ + R)

(L + /VR\ + R) VRCenterSub (L + /VR\ + R)

(L + /VR\ + R) Sub (L + /VR\ + R)

(c)Rupert Summerskill

*

SBC — 200 to 328kbps
AAC — 128 to 256kbps
LC3 — 160 to 345kbps
LDAC — 300kbps, 660kbps, 990kbps
aptX — 352kbps
aptX HD — 576kbps
aptX Adaptive — 279 to 420kbps
aptX Lossless — 120kbps to >1Mbps

*

https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html

https://science.n-helix.com/2021/09/temporal-aliasing-image-shaping-polygon.html

https://is.gd/VESA_HDMI_HDR

Dolby Atmos 3D Audio in production
https://www.youtube.com/watch?v=Bmq1Zj2Z0-8

https://www.soundguys.com/the-ultimate-guide-to-bluetooth-headphones-aac-20296/

https://hdbluetooth.com/bluetooth-audio-codecs-explained/

https://www.nextpit.com/bluetooth-audio-codecs

Useful codec speed improvement:
https://science.n-helix.com/2021/10/the-principle-of-inversion-sign-sign-crs.html

#ASIO Produces lower latency from audio Input/Output Cycles https://is.gd/FasterAudioASIO
ASIO #FasterAudio : but a lot faster, like in gaming or production https://www.asio4all.org/

Samples for Codec & Sound optimisation, Recorded on 2D Mic in 3D


DJ Bobby laser sample , 2 min sample 3D Audio + MC Vocal by JN 
https://is.gd/BobbyLaserJN_EchoZ313 
https://is.gd/BobbyLaserJN_AtmosEchoZ313
https://is.gd/DJPolyEstervsJN7_1

Buddhist Sentience Laboratory
https://is.gd/BuddhistTempleRune3D

https://is.gd/Z313EchoDOT7_1_3D

Monday, October 4, 2021

HE-AAC+SBC overlapping wave domains Quadratic Quantifier with wavelets (c)RS 2021

HE-AAC+SBC overlapping wave domains Quadratic Quantifier with wavelets (c)RS 2021


Wavelet Quantification of 4 Band Harmonic overlay for Radio, DAB, Bluetooth & WIFI & VESA Codec, Display Port & HDMI & TOS-Link

For application of all MP3 & AAC & DSC Standard Wave Compression techniques & Quadratic Banding.

SBC overlapping wave domains: (C)Rupert Summerskill

Principally in Radio harmonics : 4 Band SBC : HE-AAC Quadratic filter mask overlay quantifier

Long wave

Medium Wave

Short wave

UHF

4 bands

Signed =

Reflection Stereo (Simple reflection mapping, binaural)

Dynamic range from centric (2x Bits : Detailed)

8 Bit signed quantizers (Implemented in 16Bit F16b Signed) Long wave

9 to 12 : 12 to 14 : 9 to 14 Bit Quantifiers : Medium & Short Wave

14 to 16Bit & F12b to F16b Signed with 4Bit Remainder float : UHF

For application of all MP3 & AAC & DSC Standard Wave Compression techniques & Quadratic Banding.

Wavelet Quantification of 4 Band Harmonic overlay for Radio, DAB, Bluetooth & WIFI & VESA Codec, Display Port & HDMI & TOS-Link

Dolby Atmos : 7.1 SBC, HE-AAC, AptX F16b-Signed : HE-DA-SBCTank(tm) (c)RS

7.1 HE-SBC (3 Synced streams of R+L+Center Float : F16b+Signed, Combined Global 3D Aria Center mapped over 7.1/7.2 Channels) (c)RS

*

Harmonic beautification : JN

Observe an Earth Quake that is 85% or more within the harmonics:

Of the 2hz to 8Khz range if capable.. Tremble like a kit to Ibiza; Dark metal sings..

5Khz to 14Khz Hum the world like a tiger, Sing like a bee..; Be dark & ruthfull like "HE"; Be flexible!

11Khz to 18Khz Rings like a bell!; Sing for eternity of thy child..

16Khz to 22Kzh Sing like a harp...; The sonnet of the angels!

21Kzh to 24Khz you know the scream of metal harps! Electro ARC!

22Khz to 35Khz You know the world of the atom; The singing of a mid day sun... The heat of a pan.

*

Optimum Dynamic Direct Routing Table : ODDRT : RS


Reaching straight for Encode AAC, AptX, SBCn Dolby, DTX from the decode is the most logical choice, Needs encode path to H265, H264, VVC with as few deviations from source.

Direct container AC3, AAC 8 Support 64Bit Integer (For CPU Pure Code) Or+ AVX &@ SiMD..
Directly supports Driver Encode path though Float from GPU & CPU Driver layer,
Latency is minimal with a rout of:

Optimum Dynamic Direct Routing Table : ODDRT : Direct Encode Rout List of method: 6 V 5 or 4

Older: 6 Path : 6 Cycles
Encode, Decode, Re-encode PCM, Recode Dolby 7.1, DTS, Encode AAC or AptX or SBC or DTS

Newer: 5 Path 4 Cycles (Including System Driver functions: Virtual, Loudness, EQ etcetera)

Encode, Decode, Re-encode Dolby 7.1, DTS, Encode AAC or AptX or SBC or DTS
*

(c)RS

https://is.gd/VESA_HDMI_HDR

https://www.androidauthority.com/lossless-bluetooth-audio-2740550/

*
https://www.ffmpeg.org/index.html#news

FFMPG Outputs the Bluetooth Codecs we need http://soundexpert.org/articles/-/blogs/audio-quality-of-bluetooth-aptx

AAC, SBC, AptX Codec With legal representation for all standards submission by Google, Cloudflare & CB for all operating systems & devices (c)RS

*

Improved 3D Audio Containers : Codec's as vehicles for Audio & Video Enhancement: AV.En,

With minimum Processing (CPU+SiMD) on devices such as monitors & AMP's & Bluetooth headsets,

Powerful &(small)

https://science.n-helix.com/2021/09/temporal-aliasing-image-shaping-polygon.html

https://www.androidauthority.com/lossless-bluetooth-audio-2740550/

https://hdbluetooth.com/bluetooth-audio-codecs-explained/

https://www.nextpit.com/bluetooth-audio-codecs

https://www.trustedreviews.com/news/sound-and-vision-does-aptx-lossless-herald-new-era-for-bluetooth-streaming-4161529

https://www.trustedreviews.com/opinion/sound-and-vision-is-3d-audio-the-next-battleground-for-headphones-4151733

Useful codec speed improvement:
https://science.n-helix.com/2021/10/the-principle-of-inversion-sign-sign-crs.html

Dolby Atmos 3D Audio in production

https://www.youtube.com/watch?v=Bmq1Zj2Z0-8

Codec's For Audio & Video


Challenge accepted : Testing the Codecs Qualification 'Levels" :

Higher Dynamic Range Qualification Audio Test cases 5.1 HD 24Bit Stereo Mic's 


Sing 3D


#ASIO Produces lower latency from audio Input/Output Cycles https://is.gd/FasterAudioASIO
ASIO #FasterAudio : but a lot faster, like in gaming or production https://www.asio4all.org/
#WAX it live & WAX it FAST #ASIO for production https://www.asio4all.org/

Samples for Codec & Sound optimisation, Recorded on 2D Mic in 3D

DJ Bobby laser sample , 2 min sample 3D Audio + MC Vocal by JN
https://is.gd/BobbyLaserJN_EchoZ313
https://is.gd/BobbyLaserJN_AtmosEchoZ313
https://is.gd/DJPolyEstervsJN7_1

Buddhist Sentience Laboratory
https://is.gd/BuddhistTempleRune3D

https://is.gd/Z313EchoDOT7_1_3D

Friday, October 1, 2021

Noise VIOLATION Technology Bluetooth : Noise Reduction Technology & Noise Enhancement Technology in use (c)RS

Noise VIOLATION Technology Bluetooth : Noise Reduction Technology & Noise Enhancement Technology in use (c)RS

But ANC Bluetooth technology is provably viable for Noise cancelation!
& Also for noise enhancement telemetry..

But also for Screen, Video, Sound & File..


Hard Drive & SSD Storage..

LED Screens make a tiny bit of noise & we can profile that noise & remove it though processing.

Noise VIOLATION Technology Bluetooth : Noise Reduction Technology & Noise Enhancement:
Technology in use

Laser Mouse is sensitive enough to track sound, As in Sonic vibration that move a mouse over a mad & thus earth quake detections..

Mic can if low pass filtering is examined...

Obviously TV stations have used vibration on cameras, Stabilizers may interfere..

But analysis of low Long wave distortions makes these a network of machines to examine common issues..

We can likewise use Electronic exhaustive examination on electric noise to examine most longwave telescope issues...

Electric Cells process & filter noise so average light is what we have!

(c)Rupert S

QUAKE CON \\//_ entions 2021 - SOUND VIOLATION : QE

https://is.gd/VESA_HDMI_HDR

Wednesday, September 29, 2021

Temporal Aliasing - Image shaping Polygon Resolve Precision Enhancement (c)RS

Temporal Aliasing - Image shaping Polygon Resolve Precision Enhancement - The Vector Scope (c)RS Definition 2021-09


Vector Scope Polygon precision enhancement with temporal shaping matrix (c)Rupert Summerskill

Improve resolution of WebP, Audio & Video elements, Games & 3D & 2D & in principle Geometry, Fonts & Systems, Research faster & better but quicker, Bluetooth & earbuds,  TOSLink, HDMI, Video Playback , Audio Encoding & Decoding  , Video Encoding & Decoding, Audio Tracks, DVD & BlueRay Players, TV's & Monitors & Hardware requiring precision.

Definition of usage:

Audio Codecs
Video Codecs

Polygon Math

Resolution Enhancement
Processor Cycle Usage Reduction
Compression & Storage of object math

Complementary work on subject: RS

https://science.n-helix.com/2021/03/upscaling-enhancement.html
https://science.n-helix.com/2019/06/vulkan-stack.html

https://science.n-helix.com/2019/06/kernel.html

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

*****

The Vector Scope : Definition : RS


*

To Vector scope is to examine an original output of a Vector unit math..
In order to render the results in a higher resolution..
Or in order to upscale a rendered frame buffer (Letterbox) Into a higher resolution frame buffer,

In games that are mode set to a specific resolution (Sonic for example)..

We can install a layer after the game renders the screen that will interpret the resolution to a higher detail.

We can also Render a game in 1920 to the primary frame buffer & resolve additional details in a higher resolution.


***** Resolving material for Vector Precision Enhancement of math objects: RS

High resolution polygon translation table (matrix)

We can save a polygon table that defines higher precision mathematical representations of polygones to decompress & render in higher detail packed float polygons,

F8 definition polygons in F16b

A polygon defined in 8Bit converted though table into 32Bit polygons..
Result ? we do not have to mathematically interpolate in FPU; We load higher precision.

*

Definitions of triganomic objects for precision enhancement of stored Polygon data & maths >

*****

Triganomic curvatures in glTF : (c)RS


Are you now using Tragicomic curvatures ..

Using ARC,SIN,TAN instead of polygons for curves?
You only need to map distance along the curve for a polygon point..

That point can be perfect; For there is no such thing as a limit to a curve except that defined by bit precision..

Being 16Bit or 32Bit SiMD can represent a perfection in 16K HDR..
Even more so a Float unit 186Bit! or divisions thereof for Multiplication & fraction boosted Threading.

Curvature modelling is a plan in which we need no points of a polygon
& thus we can compress the data..

A: b16Float for example because we need lower precision sub pixel rendering..

We use this for glTF

(c)Rupert S

https://www.youtube.com/watch?v=rf4yxkB3t4o

High precision FFT Examples : https://is.gd/ProcessorLasso in the SiMD Folder...

Advanced FFT & 3D Audio functions for CPU & GPU https://gpuopen.com/true-audio-next/
https://www.kfr.dev/

*****

High resolution polygon translation table (matrix)


We can save a polygon table that defines higher precision mathematical representations of polygons to decompress & render in higher detail packed float polygons,

F8 definition polygons in F16b

A polygon defined in 8Bit converted though table into 32Bit polygons..

Result ? we do not have to mathematically interpolate in FPU; We load higher precision.

*****

The Vector Scope : Definition


*

To Vector scope is to examine an original output of a Vector unit math..
In order to render the results in a higher resolution..

Or in order to upscale a rendered frame buffer (Letterbox) Into a higher resolution frame buffer,

In games that are mode set to a specific resolution (Sonic for example)..
We can install a layer after the game renders the screen that will interpret the resolution to a higher detail.

We can also Render a game in 1920 to the primary frame buffer & resolve additional details in a higher resolution.

*

We can still scope SiMD & FPU Maths precision outside of understanding the SDK they used..
For precise representation of our desired output virtualisation,

Either:

Original resolution x upscale + SiMD+FPU Vector Scope (the code run by the application or game)

Original resolution x upscale + SiMD+FPU Vector Scope; Into Virtual resolution


To Vector Scope: To understand the maths processes run by the program..

In order to improve precision of the output; We Know that the SiMD+FPU is a lot higher precision..
Than the output Display Resolution,
We can therefore promote the resolutions of all elements in Float values to vector quality.

Vector scope (the code run by the application or game)

We can then Machine Learn from Scope & that equals superior results,
But we can also directly apply those results though SiMD+FPU maths.

GBuffers are indeed a source of SiMD, Float results & we use all the details that we need.

Example method 3D Shaped screens & surfaces, Vector Scope:

Sample the 3D image of the surface & prove the following postulate:

Surface area N +- (Height + Contour Array bFloat16 = (Layer Surface requirement + Layer-N2)

contoured displays & dimples in wafers handled though VectorScope Maths.

(c)Rupert S / DukeThrust

https://is.gd/ProcessorLasso

***** Temporal Precision Enhancement : (c)RS

For Console Games : RCFedra 0.5x4:2:0.5x4 Frame method ML Bilinear & Trilinear Multi Cache Interpolation temporal matrix (c)RS

Alternative 1

One proposes 5 High Resolution frames 15 half frames Interlaced 20 Full frames as a 120 Solution..

Alternative 2

One proposes 5 High Resolution frame sets of :

1 = full frame, 0.5 = Interlace Frame Interpolated across 0.5:1:0.5 frame,

Can be 0.75; Less or more...

The matrix uses Time Interpolated slices across 3 pixels :

Centre = Full frame info and sides X + (shape) the interpolated.

Using bilinear & trilinear Interpolation / Filtering & Simple Association Machine Learning Matrix..
The basic matrix is 16 Node groups from a single dynamic Cache source...

2 Refresh Cycles learning advanced & 1 Behind :

*Localised independent based on DMA Access patterns combined with Data load & refresh.

(c)Rupert Summerskill

https://www.theverge.com/2021/7/1/22558816/ratchet-and-clank-rift-apart-fidelity-mode-40fps-120hz-30fps-refresh-rate

*****

ML Progress statement 2021 : RS


9:02 You know FSR Virtual Screen Resolution with Dynamic letterboxing & Machine Learning..

Requires some core function,

For example Vulkan DirectML..

Such a feature is a survival trait of core function:


Core function list ML:

Adaptable Tessellation

Adaptable Sharpening

Adaptable Image Enhancement

Adaptable Resolution improvements : Vertex,Polygons,Textures & Shaders

Core function is essential for adaptation of each game engine,

Core ML function is essential for progress & improvement..

Machine learning is in essence : Cognition, Brain function & Development..

Therefore required for improvement to be made.

(c)Rupert S

https://www.youtube.com/watch?v=fzu9oT2JaK8

https://is.gd/ProcessorLasso


*
Dolby Atmos 3D Audio in production
https://www.youtube.com/watch?v=Bmq1Zj2Z0-8


Challenge accepted : Testing the Codecs Qualification 'Levels" :

Higher Dynamic Range Qualification Audio Test cases 5.1 HD 24Bit Stereo Mic's 

Live Thunder 7.1HQ https://is.gd/7_1BiNeuralSample

Sing 3D
https://is.gd/7_1_24BitTuna
https://is.gd/AngelsLove

https://is.gd/3DAtmos7_1_DJ_Jamluca

ASIO #FasterAudio : but a lot faster, like in gaming or production https://www.asio4all.org/

Thursday, March 25, 2021

Upscaling Enhancement

Super resolution API Photo & Video Enhance & upscaling demonstrations

Upscaling Enhancement For Telescopes, Space & Research Aviation Photography & Video
Photo Enhance & upscaling:


Photographic Enhancers:


BloodBorne : "Why not shoot for 4K too? Thus began a week of experiments using a tool called Topaz Video Enhance AI, which uses a number of different AI upscaling models - and it turned out that most of them could deliver appreciably higher detail."



Department of Energy - RGB_Color-Seal_Green-Mark_SC_Vertical V2 Helix.jpg (2.08 MB) https://mirrorace.org/m/3Jz5o

JOE Science Workshop V1 - DcXw0jCU8AA-Jdk.jpg (1.09 MB) https://mirrorace.org/m/3Jz5p

SuperNova image_2144_1e-SN-1993J.jpg (1.74 MB) https://mirrorace.org/m/3Jz5q

XC50 Cray Met Data Test DQ-aoSpUQAAXby-.png (6.72 MB) https://mirrorace.org/m/3Jz5r

**

deadpool V2 3000.jpg (2.81 MB) https://mirrorace.org/m/5LrrU

Such wow art V2 3000 tGi0Ap74NwbRC.jpg (4.09 MB) https://mirrorace.org/m/4pwxi

Friday, March 12, 2021

Brain Bit Precision Int32 FP32, Int16 PF16, Int8 FP8, Int6 FP6, Int4? Idealness of Computational Machine Learning ML TOPS for the human brain

Brain Bit Precision Int32 FP32, Int16 PF16, Int8 FP8, Int6 FP6, Int4? Idealness of Computational Machine Learning ML TOPS for the human brain:

Brain level Int/Float inferencing is ideally in Int8/7 with error bits or float remainders

Comparison List : RS

48Bit Int+Float Int48+FP48 (many connections, Eyes for example) HDR Vison

40BitInt+Float Int40+FP40 HDR Basic

Int16 FP32

Int8 Float16(2Channel, Brain Node)(3% Brain Study)

Int7 (20% Brain Study)

Int6 (80% Brain Study)

Int5 (Wolves (some are 6+))

Int4 (Sheep & worms)

Int3 (Germ biosystems)


Statistically a science test stated 80% of brains in man quantify Bit at 6 20% to 7Bit

XBox X & PlayStation 5 do down to INT4Bit (quite likely for quick inferencing)

Be aware that using 4 bit Int instructions .. potentially means more instructions used per clock cycle & more micro data transfers..

Int8 is most commonly liable to quantify data with minimum error in 8Bit like the Atari STE or the Nintendo 8Bit..

Colour perception for example is many orders of magnitude higher! Or 8bit colours EGA is all we would use..


16Bit was not good enough.. But 32Bit suites most people! But 10Bit(x4) 40Bit & Dolby 12Bit(x4) 48Bit is a luxury & we love it!


(c)Rupert S https://is.gd/ProcessorLasso


Restricted Boltzmann ML Networks : Brain Efficient

I propose that SIMD of large scale width & depth can implement the model :
Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications

Restricted Boltzmann Machines utilize a percentage correctness based upon energy levels of multiple node values; That represent a percentage chance of a correct solution,

My impression is that Annealer machine simply utilise more hidden values per node on a neural network,
Thus i propose that SIMD of large scale width & depth can implement the model..

A flexible approach is to experiment with percentages from a base value...
100 or 1000; We can therefore attempt to work with percentiles in order to adapt classical computation to the theory of multiplicity.

SiMD in parallel can; As we know with RISC Architecture .. 
Attempt to run an ideal network composing many times Factor & regression learning model..

Once the rules are set; Millions of independent IO OPS can be performed in cyclic learning,

Without sending or receiving data in a way that interferes with the main CPU & GPU Function..

Localised DMA.

"Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers"

Adaptive hyperparameter updating for training restricted Boltzmann machines on:
Quantum annealers
Wide Path SiMD




"Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a
variety of unsupervised machine learning applications such as image recognition, drug discovery,
and materials design. The Boltzmann probability distribution is used as a model to identify network
parameters by optimizing the likelihood of predicting an output given hidden states trained on
available data. Training such networks often requires sampling over a large probability space that
must be approximated during gradient based optimization. Quantum annealing has been proposed
as a means to search this space more efficiently which has been experimentally investigated on
D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature
or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization.
Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave
hardware during neural network training by maximizing the likelihood or minimizing the Shannon
entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental
validation on an image recognition problem. Neural network image reconstruction errors are
evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude
lower image reconstruction error using the maximum likelihood over manually optimizing the
hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the
Shannon entropy for image reconstruction."

(c)Rupert S

Example ML Statistic Variable Conversion : Super Sampling Virtual Resolutions : Talking about machine learning & Hardware functions to use it/Run it; To run within the SiMD & AVX feature-set.

For example this works well with fonts & web browsers & consoles or standard input display hubs or User Interfaces, UI & JS & Webpage code.

In the old days photo applications did exist to use ML Image enhancement on older processors..
So how do they exploit Machine Learning on hardware with MMX for example ?

Procedural process data analytics:

Converting large statistics data bases; On general Tessellation/Interpolation of images
The procedural element is writing the code that interpolates data based upon the statistics database...

Associated colours..
Face identity...
Linearity or curvature...
Association of grain & texture...

Databases get large fast & a 2 MB to 15MB Database makes the most sense...
Averages have to be categorized by either being worthy of 2 Places in the database or an average..

You can still run ML on a database object & then the points in the table are called nodes!

Indeed you can do both, However database conversion makes datasets way more manageable to run within the SiMD & AVX feature-set.

However the matter of inferencing then has to be reduced to statistical averages & sometimes ML runs fine inferencing this way.

Both ways work, Whatever is best for you & the specific hardware.

(c)Rupert S

**

DL-ML slide : Machine Learning DL-ML


By my logic the implementation of a CPU+GPU model would be fluid to both..

Machine Learning : Scientific details relevant to DL-ML slide (CPU,GPU,SiMD Hash table(M1 Vector Matrix-table +Speed)

The vector logic is compatible to both CPU+GPU+SiMD+AVX.

Relevant because we use Vector Matrix Table hardware.. and in notes the Matrix significantly speeds up the process.
(Quantum Light Matrix)

The relevance to us is immense with world VM servers
DL-ML Machine Learning Model compatible with our hardware
By my logic the implementation of a CPU+GPU model would be fluid to both..
The vector logic is compatible with both CPU+GPU.

However this is a model we can use & train..
For common core : Rupert S https://is.gd/ProcessorLasso