Kevin Tran @kevintvh

Auckland Joined November 2013

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Runway @runwayml

3 years ago

Today, Generative AI takes its next big step forward. Introducing Gen-1: a new AI model that uses language and images to generate new videos out of existing ones. Sign up for early research access: bit.ly/3RxvBZr

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Abbas Kudrati @askudrati

4 years ago

Attending Microsoft #ignite2021 ? Well if you are busy, here is our 2021 MS Ignite "Book of News" . Check this link for all the latest updates, features, and releases announced at this Ignite #microsoft Microsoft Security #newrelease2021 #updatenews lnkd.in/gzcVsi5e

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Databricks @databricks

5 years ago

Check out this guest blog post from @Scribd on how they rebuilt their on-premise data platform in the cloud with @ApacheAirflow, Databricks and @awscloud. dbricks.co/wp200610a by @agentdero #ApacheSpark

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Databricks @databricks

5 years ago

With #MLflow Model Serving, Databricks simplifies the machine learning model lifecycle from the workflow needed to launch new models to updating them with REST endpoints. dbricks.co/wp200625a #DataTeams

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Robocademy @therobocademy

5 years ago

YOLO v4 released! 🎉🎉 Here is the Tutorial on latest YOLO v4 🚀🚀🚀 A Gentle Introduction to YOLO v4 for Object detection in Ubuntu 20.04 #objectdetection #ubuntu #deeplearning #neuralnetwork #yolo #opencv #imageprocessing #machinelearning #machinevision robocademy.com/2020/05/01/a-g…

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Kevin Tran @kevintvh

6 years ago

An Epidemic of AI Misinformation thegradient.pub/an-epidemic-of…

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Sebastian Risi @risi1979

6 years ago

Always wanted to follow up on our work on Evolvable- substrate HyperNEAT that can grow the neural architecture over time. Would be great to make an efficient implementation with the now available tools.

Kazuya Horibe (@kazh289.bsky.social 🦋) @khoribe3

6 years ago

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The Gradient @gradientpub

6 years ago

Overhyped claims about AI have contributed to past AI winters. @GaryMarcus fears that we could be headed down that same path again. Here's what we can do to stop it. #ai #hype thegradient.pub/an-epidemic-of…

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WikiChip @WikiChip

6 years ago

Stealthy AI startup, Groq, makes an initial disclosure of their Tensor Streaming Processor (TSP); a single chip capable of 1 petaOPS, 250 teraFLOPS of compute. #AI #EdgeInference @GroqInc #SoftwareDefinedHardware fuse.wikichip.org/news/3005/groq…

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PyTorch @PyTorch

6 years ago

torchvision v0.4.2: Optimized video reader backend This minor release provides up to 6x speedup for video reading using a new backend option. Read more at: github.com/pytorch/vision…

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KDnuggets @kdnuggets

6 years ago

How to Speed up Pandas by 4x with one line of code - KDnuggets buff.ly/2O6GWPF

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Benedek Rozemberczki @ICML2025 @benrozemberczki

6 years ago

A PyTorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019). github.com/benedekrozembe… @PyTorchPractice @GoogleAI @TensorFlow #DeepLearning #MachineLearning #PyTorch #TensorFlow

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Benedek Rozemberczki @ICML2025 @benrozemberczki

6 years ago

This is a massively parallel implementation of graph2vec that I made. It can process millions of graphs in an hour on a desktop machine. github.com/benedekrozembe… #graph2vec #word2vec #MachineLearning #DeepLearning @gensim_py @RadimRehurek @machinelearn_d

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Benedek Rozemberczki @ICML2025 @benrozemberczki

6 years ago

I created an implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)" with PyTorch. The main idea is neat but a somewhat made up task. github.com/benedekrozembe… @PyTorchPractice @learnpytorch @NetSciPhDs @netscisociety @pythontrending

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Benedek Rozemberczki @ICML2025 @benrozemberczki

6 years ago

A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 19). I am really happy to see this paper at @icmlconf nice theory and results. @PyTorchPractice @learnpytorch @PyTorch @NetSciPhDs @DeepLearningHub

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Ivan Titov @iatitov

6 years ago

If at #iclr2019, consider stopping by @UndefBehavior 's poster on differentiable perturb-and-parse: treating trees as latent variables while relying on differentiable dynamic programming. 11am, Great Hall, BC #16. arxiv.org/abs/1807.09875 #NLProc