James Lucas @james_r_lucas
Research Scientist @ NVIDIA Machine Learning PhD Candidate @ University of Toronto; Vector Institute cs.toronto.edu/~jlucas/ Toronto, Ontario Joined April 2018-
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Generating nice meshes in AI pipelines is hard. Our #SIGGRAPHAsia2024 paper proposes a new representation which guarantees manifold connectivity, and even supports polygonal meshes -- a big step for downstream editing and simulation. (1/N) SpaceMesh: research.nvidia.com/labs/toronto-a……
Our work builds closely on DyHPO (arxiv.org/abs/2202.09774 ) with Martin Witsuba, Arlind Kadra, and @josifgrabocka, as well as Permutation-invariant Graph Metanetworks (research.nvidia.com/labs/toronto-a…) with @dereklim_lzh, @HaggaiMaron, @MarcTLaw, myself and @james_r_lucas.
After a hiatus of three years, I'm back to writing blog posts on nice math that deserves more attention. This time, I'm going to write a two part blog post on a bit of a hot take: Everyone should learn optimal transport. mufan-li.github.io/OT1/
Appreciate those of you that stay till the last poster presentation and drop by our poster! Shout out to @james_r_lucas for the incredible supports and @shaohua0116 for the pic 👍
Appreciate those of you that stay till the last poster presentation and drop by our poster! Shout out to @james_r_lucas for the incredible supports and @shaohua0116 for the pic 👍 https://t.co/ll4icoQbzX
Excited to release 🚀NeRF-XL🚀, a principled method to distribute your NeRF's parameters to multiple GPUs! research.nvidia.com/labs/toronto-a… It is heuristics-free and mathematically equiv. with training your NeRF on a single (large) GPU. Let's scale it up!🌎
New #NVIDIA ICLR spotlight paper: What if neural nets could process, analyze, interpret, and edit other neural nets weights? These models, termed metanetworks, are useful for various tasks. We develop new metanetworks for processing diverse input neural net architectures. 1/n
I am hiring a postdoc under joint supervision with Laleh Seyyed-Kalantari to work on understanding the internal representations of foundation models, with a specific focus on high-impact societal applications like medicine and law. Location is in Toronto or Waterloo. Inquire...
☕ The new LATTE3D model from #NVIDIAResearch turns text prompts into high-quality 3D representations of objects and animals within a second on a single RTX GPU. #GTC24 👀 nvda.ws/3TzYN3s
📢📢📢 Check out XCube, a generative model for very-high resolution 3D shapes and scenes! Project Page: tinyurl.com/ykex2wur Project Video: youtu.be/GX0lzwy8nUI Props to @xuanchi13 for leading this project!!!! [1/N]
🚀 Introducing our #SIGGRAPHAsia work “Adaptive Shells”, a novel #NeRF formulation that yields high visual fidelity and greatly accelerates rendering. TLDR: Auto-derived bounding shells result in up to 10x faster inference than InstantNGP! [1/n]
If you are in Paris, come check out our work on real-time text-to-3D generation at #ICCV2023 on Friday from 10:30 AM-12:30 PM in Room "Foyer Sud" - 035!
If you are in Paris, come check out our work on real-time text-to-3D generation at #ICCV2023 on Friday from 10:30 AM-12:30 PM in Room "Foyer Sud" - 035!
We'll be presenting our poster on Friday, 10:30am-12:30pm, #035 in Foyer Sud. Come say hi!
We'll be presenting our poster on Friday, 10:30am-12:30pm, #035 in Foyer Sud. Come say hi!
In high-stakes settings (e.g. AV), it is common practice to augment real training data with synthetic data (e.g. from a driving simulator). Naturally, we were surprised to not find principled algorithms to combine labeled sim and real data! Until now: tinyurl.com/42f327vw
How much data should you collect when starting a new ML project? What if there are multiple sources of data from different domains? We try to answer these questions in our #NeurIPS2022 paper: Optimizing Data Collection for Machine Learning. See the thread and webpage!
How much data should you collect when starting a new ML project? What if there are multiple sources of data from different domains? We try to answer these questions in our #NeurIPS2022 paper: Optimizing Data Collection for Machine Learning. See the thread and webpage!
Excited to share our #NeurIPS2022 @NVIDIAAI work GET3D, a generative model that directly produces explicit textured 3D meshes with complex topology, rich geometric details, and high fidelity textures. #3D Project page: nv-tlabs.github.io/GET3D/
📢[NOW LIVE] Toronto Annotation Suite (TORAS) is available to the public! TORAS is a web-based AI-powered data #annotation platform we have been developing at the @UofT for over 4 years. We hope that you find it useful in annotating your data! More info: aidemos.cs.toronto.edu/toras
🎙️Attention Neural Fields researchers!! This one is for you: today we're proud to officially release our research library #NVIDIAKaolinWisp. Wisp is packed with an interactive renderer and various building blocks for optimizing NeRF / SDF pipelines. Code: github.com/NVIDIAGameWork…
Diffusion Models are taking over Deep Generative Learning! Like to get started? 🔥 📢 We re-recorded our ~4 hour tutorial on Diffusion Models we presented at @CVPR this year (w/ @RuiqiGao & @ArashVahdat)! Youtube: youtube.com/watch?v=cS6JQp… Slides, etc.: …2-tutorial-diffusion-models.github.io
Reminder that the ICML Workshop on Distribution-Free Uncertainty Quantification starts TOMORROW! dfuq.rocks/22 We have 70 awesome papers on UQ for time-series, object detection, distribution shift, and more. All are welcome in Room 308!
🆕📜We study large language models’ ability to extrapolate to longer problems! 1) finetuning (with and without scratchpad) fails 2) few-shot scratchpad confers significant improvements 3) Many more findings (see the table & thread) Paper: [arxiv.org/abs/2207.04901] 1/
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