Nicholas M. Boffi
@nmboffi.bsky.social
Building generative models for high-dimensional science and engineering.
Assistant prof. @CarnegieMellon & affiliated faculty @mldcmu, previously instructor @NYU_Courant, PhD jointly @Harvard and @MIT
https://nmboffi.github.io
Assistant prof. @CarnegieMellon & affiliated faculty @mldcmu, previously instructor @NYU_Courant, PhD jointly @Harvard and @MIT
https://nmboffi.github.io
this paper is a pretty impressive tour de force in neural network training: arxiv.org/abs/2410.11081
pretty inspiring to me -- network isn't converging? rigorously monitor every term in your loss to identify where in the architecture something is going wrong!
pretty inspiring to me -- network isn't converging? rigorously monitor every term in your loss to identify where in the architecture something is going wrong!
Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models
Consistency models (CMs) are a powerful class of diffusion-based generative models optimized for fast sampling. Most existing CMs are trained using discretized timesteps, which introduce additional hy...
arxiv.org
February 13, 2025 at 12:52 PM
this paper is a pretty impressive tour de force in neural network training: arxiv.org/abs/2410.11081
pretty inspiring to me -- network isn't converging? rigorously monitor every term in your loss to identify where in the architecture something is going wrong!
pretty inspiring to me -- network isn't converging? rigorously monitor every term in your loss to identify where in the architecture something is going wrong!
Reposted by Nicholas M. Boffi
Oscillations naturally emerge in large crowds. That's because your brain works by oscillating.
Emergence of collective oscillations in massive human crowds
www.nature.com/articles/s41...
#neuroscience
Emergence of collective oscillations in massive human crowds
www.nature.com/articles/s41...
#neuroscience
Emergence of collective oscillations in massive human crowds - Nature
Analysis of the confined crowds at the San Fermín festival in Spain shows that dense crowds can self-organize into macroscopic chiral oscillators, coordinating the orbital motion of hundreds of indivi...
www.nature.com
February 10, 2025 at 6:23 PM
Oscillations naturally emerge in large crowds. That's because your brain works by oscillating.
Emergence of collective oscillations in massive human crowds
www.nature.com/articles/s41...
#neuroscience
Emergence of collective oscillations in massive human crowds
www.nature.com/articles/s41...
#neuroscience
what's the status on using jax for image experiments (e.g. with diffusion models)? it seems like standard packages like huggingface diffusers have much less robust implementations of the same neural networks than the pytorch counterpart?
February 3, 2025 at 1:48 PM
what's the status on using jax for image experiments (e.g. with diffusion models)? it seems like standard packages like huggingface diffusers have much less robust implementations of the same neural networks than the pytorch counterpart?
Reposted by Nicholas M. Boffi
📣 Excited to receive the 💥 NSF CAREER 💥 Award. Our group is looking for PhD students and postdocs interested in the Nonlinear Dynamics and the Physics of Living Systems, with support from NSF, HFSP, and other sources. We’d appreciate your help in spreading the word! @ucsdphysci.bsky.social
January 16, 2025 at 9:08 PM
📣 Excited to receive the 💥 NSF CAREER 💥 Award. Our group is looking for PhD students and postdocs interested in the Nonlinear Dynamics and the Physics of Living Systems, with support from NSF, HFSP, and other sources. We’d appreciate your help in spreading the word! @ucsdphysci.bsky.social
some really nice new work by @jiequnh.bsky.social and collaborators arxiv.org/abs/2409.08526
of course i'm super biased, but i think that figuring out how to solve high-dimensional scientific computing problems with ML has potentially very high impact. i'd love to see more work like this
of course i'm super biased, but i think that figuring out how to solve high-dimensional scientific computing problems with ML has potentially very high impact. i'd love to see more work like this
Deep Picard Iteration for High-Dimensional Nonlinear PDEs
We present the Deep Picard Iteration (DPI) method, a new deep learning approach for solving high-dimensional partial differential equations (PDEs). The core innovation of DPI lies in its use of Picard...
arxiv.org
January 16, 2025 at 2:04 PM
some really nice new work by @jiequnh.bsky.social and collaborators arxiv.org/abs/2409.08526
of course i'm super biased, but i think that figuring out how to solve high-dimensional scientific computing problems with ML has potentially very high impact. i'd love to see more work like this
of course i'm super biased, but i think that figuring out how to solve high-dimensional scientific computing problems with ML has potentially very high impact. i'd love to see more work like this
what are the most important open problems in molecular simulation right now that stand to benefit from ML-based methods? any good reviews or references to get up to speed rapidly? @gcorso.bsky.social @hannes-stark.bsky.social?
January 10, 2025 at 1:07 PM
what are the most important open problems in molecular simulation right now that stand to benefit from ML-based methods? any good reviews or references to get up to speed rapidly? @gcorso.bsky.social @hannes-stark.bsky.social?
there's a lot of interest right now in using diffusion and flow matching models for sampling (i.e., no data but access to the energy). is anyone aware of works using diffusion models or score-based approaches for data assimilation? seems like it could be a natural evolution of kalman filter ideas.
January 7, 2025 at 1:22 PM
there's a lot of interest right now in using diffusion and flow matching models for sampling (i.e., no data but access to the energy). is anyone aware of works using diffusion models or score-based approaches for data assimilation? seems like it could be a natural evolution of kalman filter ideas.
question for the academics: how do you manage your (ever-growing) reading list?
do you have a few dedicated reading blocks per week, where you tackle some papers you've been interested in?
do you only read as you need to solve a research problem?
how do you fit textbooks into this?
do you have a few dedicated reading blocks per week, where you tackle some papers you've been interested in?
do you only read as you need to solve a research problem?
how do you fit textbooks into this?
January 6, 2025 at 1:06 PM
question for the academics: how do you manage your (ever-growing) reading list?
do you have a few dedicated reading blocks per week, where you tackle some papers you've been interested in?
do you only read as you need to solve a research problem?
how do you fit textbooks into this?
do you have a few dedicated reading blocks per week, where you tackle some papers you've been interested in?
do you only read as you need to solve a research problem?
how do you fit textbooks into this?