Aaditya 'Adi' Prakash @aaditya_prakash

Staff Machine Learning Scientist at Spring Discovery and Adjunct Faculty at BayPath University. ex: PathAI, Philips Research, Microsoft Research. PhD ML. iamaaditya.github.io/about Boston, MA Joined December 2008

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Aaditya 'Adi' Prakash @aaditya_prakash

2 months ago

I am always amazed with the inference speed of @GroqInc . Given Kimi K2 is a super big model, the results come almost instantaneously. 👏

Groq Inc @GroqInc

2 months ago

I am always amazed with the inference speed of @GroqInc . Given Kimi K2 is a super big model, the results come almost instantaneously. 👏

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Xavier(Xavi) Amatriain @xamat

2 years ago

As many of you know, over the past few months I have been sharing Prompt Engineering resources in different forms. I have now compiled them all into a cohesive publication and uploaded to arxiv: arxiv.org/abs/2401.14423

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Alex Kontorovich @AlexKontorovich

2 years ago

This is just wild! Nov 9: @wtgowers, Green, Manners, and Tao prove the polynomial Freiman-Ruzsa conjecture in char 2. arxiv.org/abs/2311.05762 Nov 18: Tao announces that he wants to formalize it in Lean, building on Mathlib. terrytao.wordpress.com/2023/11/18/for… Dec 4: One little theorem…

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Aaditya 'Adi' Prakash @aaditya_prakash

2 years ago

Evaluation of #llava. Seems very poor - I think it might not be trained well enough. #LLM

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Spring Science @SpringDiscovery

3 years ago

Come get live, 1:1 demos of AI software that gives scientists superpowers @ #SLAS2023, and also check out our posters: springdiscovery.com/slas Learn all about using AI to go beyond traditional image analysis, check out our new public benchmark dataset, and more...

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Aaditya 'Adi' Prakash @aaditya_prakash

3 years ago

Looking forward to this webinar. Ruff an extremely fast linting for Python (when I say extreme, I mean it) github.com/charliermarsh/… info.jetbrains.com/pycharm-webina… @charliermarsh 👏

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Aaditya 'Adi' Prakash @aaditya_prakash

3 years ago

If you want to explore an amazing Cell Painting dataset - JUMP-CP : 3M images, 75M single-cells, 5 channels, 306 compounds, and 6 genetic perturbations (ORF & CRISPR). Any feedback is welcome.

Anne Carpenter, PhD @DrAnneCarpenter

3 years ago

If you want to explore an amazing Cell Painting dataset - JUMP-CP : 3M images, 75M single-cells, 5 channels, 306 compounds, and 6 genetic perturbations (ORF & CRISPR). Any feedback is welcome.

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Stat.ML Papers @StatMLPapers

3 years ago

Stop using the elbow criterion for k-means and how to choose the number of clusters instead. (arXiv:2212.12189v1 [stat.ML]) ift.tt/TJaIE2X

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Dana-Farber’s Breast Oncology Center @DFCI_BreastOnc

3 years ago

#SABCS22 | Poster | Prognostic & Predictive Factors Quantitative analysis of fiber-level collagen features in H&E whole-slide images predicts neoadjuvant therapy response in patients with HER2+ #breastcancer @EMittendorfMD @aaditya_prakash @BusemBKurt @SchnittStuart @stolaney1

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Rémi Flamary 🦋 @RFlamary

3 years ago

Visual introduction to optimal transport. My first Manim video (best seen in full screen)! Special thanks to @gabrielpeyre for his suggestions.

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Aaditya 'Adi' Prakash @aaditya_prakash

3 years ago

This might be the best explanation of the Fourier Transform on the internet: jezzamon.com/fourier/index.… author: @jezzamonn

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Aaditya 'Adi' Prakash @aaditya_prakash

3 years ago

Our awesome Lead Scientist - Rachel Jacobson, explains how we use machine learning to harness the complexities of inflammasome biology for novel drug discovery youtube.com/watch?v=7LN4mT…

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Aaditya 'Adi' Prakash @aaditya_prakash

3 years ago

The inventor of Confocal Microscopy is none other than Marvin Minsky, inventor of Artificial Intelligence web.media.mit.edu/~minsky/papers…

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Aaditya 'Adi' Prakash @aaditya_prakash

3 years ago

Today I learned that there is a board that certified medical illustrators. You can find some of the awesome illustrations (including the famous Coronavirus image) here: ami.org/medical-illust…

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Aaditya 'Adi' Prakash @aaditya_prakash

3 years ago

Interpretability of Machine Learning in Medicine is very important. However, Multiple Instance Learning (used with large images like Pathology) is not interpretable. We propose Additive MIL (similar to SHAP), which solves this problem: arxiv.org/abs/2206.01794 Feedbacks welcome.