Ward Plunet @StartupYou
Phd in Neuroscience looking at the intersection between machine learning and neuroscience #machinelearning #AI #neuroscience Vancouver, Canada Joined December 2011-
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Think people might want to try this out
this is a pretty good one. If you can only see one of the two - try shifting you gaze to in front of the car or behind the car.
this is a pretty good one. If you can only see one of the two - try shifting you gaze to in front of the car or behind the car.
Google presents Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention 1B model that was fine-tuned on up to 5K sequence length passkey instances solves the 1M length problem arxiv.org/abs/2404.07143
Meta confirms Llama 3 coming in the 'next month' techcrunch.com/2024/04/09/met…
Always wondered why more people weren't try to train with compressed text - but in this case that had to device a novel compression technique.
Always wondered why more people weren't try to train with compressed text - but in this case that had to device a novel compression technique.
Considerable reduction in required FLOPs for the same performance.
The new editing tools in DALLE are nice. Not perfect but overall worked for me.
To exploring more of the design space
Worth taking a look at if you want to think of the idea of converting brain networks to NNs.
Worth taking a look at if you want to think of the idea of converting brain networks to NNs.
I'm excited to share our latest fMRI-to-image paper 🥳 MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data! 🧠👁️ arXiv: arxiv.org/abs/2403.11207 project page: medarc.ai/mindeye2
Thinking quietly helps in thinking aloud - we added an additional result to the Quiet-STaR paper! We also open sourced code and weights today.
Thinking quietly helps in thinking aloud - we added an additional result to the Quiet-STaR paper! We also open sourced code and weights today.
I really like the approach of this paper. And this thread offers a very nice explanation of the paper.
I really like the approach of this paper. And this thread offers a very nice explanation of the paper.
Take a read of this perspective on the need for NeuroAI.
Ok this paper is bonkers and I love it biorxiv.org/content/10.110…
Something to read. Deep learning researchers might get inspired.
Something to read. Deep learning researchers might get inspired.
Interesting thread: neuroscience knowledge to help machine learning.
This could speed up research ideas, and narrow down the near infinite possible experiments that could be run.
This could speed up research ideas, and narrow down the near infinite possible experiments that could be run.
Context window of videos with 1M tokens
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LoRA Land 310 Fine-tuned LLMs that Rival GPT-4 Low Rank Adaptation (LoRA) has emerged as one of the most widely adopted methods for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs). LoRA reduces the number of trainable parameters and memory usage while
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Thank @arankomatsuzaki for promoting our work!
Self-Play Preference Optimization for Language Model Alignment SPPO serves as the RLHF counterpart of SPIN and outperforms iterative DPO, Snorkel AI, Self-Rewarding LM, GPT-4 0613 etc arxiv.org/abs/2405.00675
ICML decisions out! 👀 2/2 accepted papers for my first ICML main conf submissions 😌 🥳 All thanks to the hard work of all my amazing co-authors! 🙏
Video of this talk is up on YouTube! youtube.com/watch?v=Ibfdvz… I walk through our Matryoshka Representation Learning 🪆 (MRL) and Adaptive Web-Scale Semantic Search 💃(AdANNS) works #ml twitter pls feel free to share your thoughts, always happy to chat! 😄
Hi Twitter! I'm giving a talk on the recently popular Matryoshka Representation Learning and followup web-scale semantic search work at @UWMadisonECE MLOPT Seminar mlopt.ece.wisc.edu/idea-seminar/ Time: April 26, 2024, 12:30 PM – 1:30 PM CT Everyone is welcome to join on Zoom! :D 1/n
Long context is essentially a length generalization problem. Our recent work "Transformers Can Achieve Length Generalization But Not Robustly" (arxiv.org/abs/2402.09371) showed a surprisingly simple approach to achieving length generalization: train your model several times using…