#paralleloptimization 検索結果

Mikhail Bragin

2021/03/10

Distributed and Asynchronous Coordination of a Mixed-Integer Linear System via Surrogate Lagrangian Relaxation doi.org/10.36227/techr… #distributedmethods #optimizationmethods #paralleloptimization #MILP #MIP #OR #SLR #LR #LagrangianRelaxation #asynchronousmethods

"#paralleloptimization" に一致する結果はありません

Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices.…

thinkymachines's tweet image. Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices.…

Pharos | Testnet Live

@pharos_network

/08/27

Understanding Pharos’ Parallel Execution Framework 🧵

Luke Wroblewski

@LukeW

2017/01/11

Images are a huge part of bandwidth size/costs. We're using machine learning to cut 75% bandwidth per image. blog.google/products/googl…

LukeW's tweet image. Images are a huge part of bandwidth size/costs. We're using machine learning to cut 75% bandwidth per image. blog.google/products/googl…

Skilled Coder

@theskilledcoder

/07/14

Algorithm Upgrades: From Naive → Optimal

Scott Linderman

@scott_linderman

/10/29

We show that predictability enables parallelizability for a variety of algorithms, including DEER, quasi-DEER, and gradient descent! [16/17]

scott_linderman's tweet image. We show that predictability enables parallelizability for a variety of algorithms, including DEER, quasi-DEER, and gradient descent! [16/17]

Parag Agrawal

@paraga

/09/26

Added the parallel search api to the chart for completeness.

Perplexity

@perplexity_ai

/09/25

Perplexity Search API achieves leading quality across single-step and deep research benchmarks, consistently outperforming competitors.

perplexity_ai's tweet image. Perplexity Search API achieves leading quality across single-step and deep research benchmarks, consistently outperforming competitors.

Nishith Jain

@kingnish24

3 時間

LLMs are powerful but painfully slow at inference. Every token is generated sequentially, bottlenecked by memory bandwidth. MEDUSA proposes a simple yet elegant fix: parallel decoding heads. Let’s see how it works 🧵

kingnish24's tweet image. LLMs are powerful but painfully slow at inference. Every token is generated sequentially, bottlenecked by memory bandwidth. MEDUSA proposes a simple yet elegant fix: parallel decoding heads. Let’s see how it works 🧵

Tom Dörr

@tom_doerr

/10/30

Finds duplicates and outliers in image datasets

J1m

@James_Marotta

/10/29

Efficient client-side filtering, who needs React compiler. 👀

Proful

@profulsadangi

2020/09/27

Javascript array functions map filter find reduce

Ravinesh Raj

@ravineshraj

8 分

📢 Developers, this #Thanksgiving , we’re serving something special! @pixazoai SeedVR2 Image Upscale API is now live — upscale your images to 4K clarity with cutting-edge AI. API 👉 pixazo.ai/models/ai-imag… #AI #ImageUpscale #Developers #APIs #AIGeneration Input => Output

ravineshraj's tweet image. 📢 Developers, this #Thanksgiving , we’re serving something special!

@pixazoai SeedVR2 Image Upscale API is now live — upscale your images to 4K clarity with cutting-edge AI.

API 👉 pixazo.ai/models/ai-imag…

#AI #ImageUpscale #Developers #APIs #AIGeneration
Input =&gt; Output

Colossal

@thisiscolossal

2018/01/25

Color Palettes of Historic Paintings Subdivided with Algorithms by Dimitris Ladopoulos thisiscolossal.com/2018/01/painti…

thisiscolossal's tweet image. Color Palettes of Historic Paintings Subdivided with Algorithms by Dimitris Ladopoulos thisiscolossal.com/2018/01/painti…

Luis

@lusxvr

/07/02

Today, we are open-sourcing our pipeline to deduplicate large-scale image datasets. On one GPU, we can deduplicate 10k images against 1M indexed test images in ~60 seconds. But how?

lusxvr's tweet image. Today, we are open-sourcing our pipeline to deduplicate large-scale image datasets.

On one GPU, we can deduplicate 10k images against 1M indexed test images in ~60 seconds. But how?

Peyman Milanfar

@docmilanfar

2020/05/25

1/8 What does it mean to filter an image (or signal)? Often we choose, or design, a set of weights and apply them to the input image. But what loss/objective function does this process optimize (if any)? Should we care?