Viewing profile — nsthorat

Lilac co-creator here :) Lilac is an open-source tool that enables AI practitioners to see and quantify their datasets. Lilac allows users to: - Browse datasets with unstructured d…

I cant tell if this is research about generative AI, or AI generated research. The new internet sucks

How could a bear population quadruple in a month of lockdown when the gestational period of a bear is 200+ days?

This backend work has already begun (and can run posenet, albeit still slower than WebGL): https://github.com/tensorflow/tfjs/tree/master/tfjs-backend-...

Unfortunately there is no attribution, but this tool was created by Daniel Smilkov, who also built TensorFlow Playground and who is a cocreator of TensorFlow.js. https://twitter.co…

There are many reasons to do it in JavaScript: - Many companies and projects have their entire server-side stack in JavaScript and Node.js, and often they want to simply make a pre…

This probably won't buy you anything. The API you still have is WebGL, and JS is not the bottleneck.

We've done some initial tests ourselves. WASM doesn't yet support SIMD so WebGL tends to be 5-10x faster. SIMD is actively being worked on by many smart people in Chromium / other …

Come build a WASM backend for deeplearn.js :)

https://github.com/pair-code/deeplearnjs

Or you improve your algorithms and use the existing hardware (think distributed computing on cheap HDDs).

Why wait?

It doesn't work in node yet, a relevant issue: https://github.com/PAIR-code/deeplearnjs/issues/234

You're right. Some history: We wanted to do hardware accelerated deep learning on the web, but we realized there was no NumPy equivalence. Our linear algebra layer has now matured …

WebGPU conversations are ongoing: https://en.wikipedia.org/wiki/WebGPU WebAssembly is coming along quite nicely. And SwiftShader is a quite nice fallback for blacklisted GPUs. They…

Often times researchers train huge models, but don't think about model size (because they don't have to). We've seen ~200MB production models get down to ~4MB and not lose much pre…

There is lots of work being done in model compression (quantization, simple factorization tricks, better conv kernels like depthwise separable convs, etc). We won’t let that happen…

We call ourselves deeplearn.js, but you can use it for general linear algebra! Our NDArrayMath layer is analogous to NumPy, and we support a large subset of it (we support many of …

This is just the beginning :)

Author of deeplearnjs here. We hear you, and we 100% agree. Stay tuned.

Author of deeplearn.js here. A quick summary: We store NDArrays as floating point WebGLTextures (in rgba channels). Mathematical operations are defined as fragment shaders that ope…