Brandon Amos @brandondamos
research scientist @MetaAI (FAIR) | optimization, machine learning, control, and reinforcement learning | PhD from @SCSatCMU bamos.github.io New York, NY Joined January 2014-
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1/What does it mean for an LLM to “memorize” a doc? Exactly regurgitating a NYT article? Of course. Just training on NYT?Harder to say We take big strides in this discourse w/*Adversarial Compression* w/@A_v_i__S @zhilifeng @zacharylipton @zicokolter 🌐:locuslab.github.io/acr-memorizati…🧵
GenAI does not stop, so it is time for a new blog post. Since LLMs are everywhere, I decided to take a look at them, my curious readers. And a bonus: An implementation of a teenyGPT 🤖✨ 📄Post: jmtomczak.github.io/blog/20/20_llm… 🖥️Code: github.com/jmtomczak/intr…
Everything can be formulated as an optimization problem.
Everything can be formulated as an optimization problem.
New paper alert: React-OT: Optimal Transport for Generating Transition State in Chemical Reactions (arxiv.org/abs/2404.13430). React-OT formulates TS search as a transport problem, approaching chemical accuracy while taking only 0.5 seconds in inference on a single GPU. #compchem
We have a new preprint out - your language model is not a reward, it’s a Q function! 1. The likelihood of the preferred answer must go down - it’s a policy divergence 2. MCTS guided decoding on language is equivalent to likelihood search on DPO 3. DPO learns credit assignment
Excited to share Diffusion-DPO, a method to directly align diffusion models to user preference. DPO-tuned SDXL obtains a 70% win rate over SDXL on PartiPrompts, a new SOTA for open source models! It is also effective at Learning from AI Feedback. arxiv.org/abs/2311.12908 (1/N)
1/ New work on ML+PDEs: differentiable PDE-constrained optimization as a layer in neural networks can be made much faster by scaling up via mixture-of-experts → also better training stability and improved accuracy! Accepted at #ICLR2024: openreview.net/forum?id=u3dX2…
same with my paper collection 😅 (except instead of buying them I pay for cloud storage so I can save as many vision and graphics papers as I want in full resolution)
same with my paper collection 😅 (except instead of buying them I pay for cloud storage so I can save as many vision and graphics papers as I want in full resolution) https://t.co/vpBNZbc8Gg
We studied In-Context learning with hundreds to thousands of examples. My favorite example: I sent *one million* tokens to Gemini 1.5 Pro for linear classification with 64 dimensional integer-valued vectors and many-shot learning performs similarly to k-Nearest Neighbours.
New #NVIDIA paper: Real-time text-to-3D generation #ICCV2023 3D generation from text requires expensive per-prompt optimization. We train 1 model on many prompts for real-time generalization to unseen prompts, interpolations and more! ATT3D details: research.nvidia.com/labs/toronto-a…
Our team at PNNL is hiring passionate postdoc to do research in Scientific Machine Learning methods with applications to applied energy and beyond. careers.pnnl.gov/jobs/9003?lang…
I'm delighted to announce a differentiable INLA implementation in JAX, from my colleague @geraschenko. In principle this lets you fit latent Gaussian MRFs (e.g., for spatial stats, or SLAM) with gradient-based methods, although currently the memory use is prohibitive . The trick…
Introducing 𝐀𝐋𝐎𝐇𝐀 𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐞𝐝 🌋 - Pushing the boundaries of dexterity with low-cost robots and AI. @GoogleDeepMind Finally got to share some videos after a few months. Robots are fully autonomous filmed in one continuous shot. Enjoy!
🚀 How can meta-learning, self-attention & JAX power the next generation of Evolutionary Optimizers 🦎? Excited to share my @DeepMind internship project and our #ICLR2023 paper ‘Discovering Evolution Strategies via Meta-Black-Box Optimization’ 🎉 📜: openreview.net/forum?id=mFDU0…
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
🦎Can we teach Transformers to perform in-context Evolutionary Optimization? Surely! We propose Evolutionary Algorithm Distillation for pre-training Transformers to mimic teachers 🧑🏫 🎉 Work done @GoogleDeepMind 🗼with @alanyttian & @yujin_tang 🤗 📜: arxiv.org/abs/2403.02985
5 more days to apply for a summer internship in Scientific Machine Learning at PNNL! careers.pnnl.gov/jobs/8943?lang…
🎨Spent some time refactoring the 2021 post on diffusion model with new content: lilianweng.github.io/posts/2021-07-… ⬇️ ⬇️ ⬇️ 🎬Then another short piece on diffusion video models: lilianweng.github.io/posts/2024-04-… (Yes, I had an intensive weekend🥹)
There's no reason not to use resets to speed up RL in LLM-land, where it's just generating from a prefix! You can also use the broader set of hybrid RL techniques to speed up *repeated* RL (e.g. in inverse RL --gokul.dev/filter/ or arxiv.org/abs/2402.08848).
There's no reason not to use resets to speed up RL in LLM-land, where it's just generating from a prefix! You can also use the broader set of hybrid RL techniques to speed up *repeated* RL (e.g. in inverse RL --gokul.dev/filter/ or arxiv.org/abs/2402.08848).
Differentiable Metropolis-Hastings: differentiate through Bayesian estimation to optimize models towards achieving desired probabilistic outcomes, with implementation in #julialang (#sciml) For more information, see arxiv.org/abs/2306.07961
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Two students came in for my office hours. […] Six hours later I took them to have ice-cream 🍦🍦🍦 🥲🥲🥲
Excited to be joining friends and colleagues from @GoogleDeepMind in Vienna for #ICLR2024 in a little over a week! Looking forward to meeting new people and hearing about exciting work on open-ended LLM-powered agents, tool use, and many other topics close to my heart ☺️
Who's building a terminal with LLM completion? Or does this already exist
Is there a bug in OpenReview for #UAI2024? We submitted 3 papers, and none of the reviewers updated their responses and there are only reject or accept decisions—no meta-review.
Diffusion models can be reformulated as equilibrium thermodynamical systems.
@marikgoldstein @brandondamos or solved through amortized inference 😎
1/What does it mean for an LLM to “memorize” a doc? Exactly regurgitating a NYT article? Of course. Just training on NYT?Harder to say We take big strides in this discourse w/*Adversarial Compression* w/@A_v_i__S @zhilifeng @zacharylipton @zicokolter 🌐:locuslab.github.io/acr-memorizati…🧵
GenAI does not stop, so it is time for a new blog post. Since LLMs are everywhere, I decided to take a look at them, my curious readers. And a bonus: An implementation of a teenyGPT 🤖✨ 📄Post: jmtomczak.github.io/blog/20/20_llm… 🖥️Code: github.com/jmtomczak/intr…
@marikgoldstein @ylecun @brandondamos lol I like this one :-)
@ylecun and then specified as a layer @brandondamos
Everything can be formulated as an optimization problem.
once @ylecun told me (heavily paraphrased), it's not F=ma but \min (F-ma)^2. i didn't realize its importance, but it is perhaps the most enlightning perspective i've ever heard.
Our new work on generative models for chemical reactions: much faster inference with flow matching (OT path) training scheme, better leveraging our knowledge about the problem is a key to solve science problems! Check out the paper if you are interested!
New paper alert: React-OT: Optimal Transport for Generating Transition State in Chemical Reactions (arxiv.org/abs/2404.13430). React-OT formulates TS search as a transport problem, approaching chemical accuracy while taking only 0.5 seconds in inference on a single GPU. #compchem
I2SB (i2sb.github.io) + (nearly optiaml) flow matching yields 1000x speed-up compared to standard denoising diffusion for generating highly accurate transition states 🧑🔬⚗️🧪 Check out our new preprint👇 arxiv.org/abs/2404.13430 Very fun collab w/ @chenru_duan @YuanqiD!
New paper alert: React-OT: Optimal Transport for Generating Transition State in Chemical Reactions (arxiv.org/abs/2404.13430). React-OT formulates TS search as a transport problem, approaching chemical accuracy while taking only 0.5 seconds in inference on a single GPU. #compchem
New paper alert: React-OT: Optimal Transport for Generating Transition State in Chemical Reactions (arxiv.org/abs/2404.13430). React-OT formulates TS search as a transport problem, approaching chemical accuracy while taking only 0.5 seconds in inference on a single GPU. #compchem
350 miles in four days, including two centuries, and I'm in DC!
Phi-3 just released by Microsoft. Three small size models (3.8B, 7B and 14B) trained on highly filtered and synthetic data. They report impressive performance since the 3.8B model (trained on 3T tokens) has MMLU of 69% matching Llama3 8B, and the 7B Phi-3 model has 75% MMLU,…
Excellent video-tutorial on the curse of unrolling. There's no feeling like when others build and improve upon your work 🤗
Check out my latest video on the "Curse of Unrolling," a counter-intuitive phenomenon when you unroll differentiate ("piggyback AD") through an iterative algorithm: youtu.be/80w5wDxq26c Even if your primal converges exponentially linear, the Jacobian initially does not. 🧵🧵
We have a new preprint out - your language model is not a reward, it’s a Q function! 1. The likelihood of the preferred answer must go down - it’s a policy divergence 2. MCTS guided decoding on language is equivalent to likelihood search on DPO 3. DPO learns credit assignment
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