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neurips papers by affiliation since 2015 $goog #NeurIPS2025

neurips papers by affiliation since 2015 $goog #NeurIPS2025

267,334 views • 7 months ago

We are excited to share that “Continuous Thought Machines” has been accepted as a Spotlight at #NeurIPS2025! 🧠✨ The CTM is an AI that mimics biological brains by using neural dynamics & synchronization to think over time. It can solve complex mazes by building internal maps, gaze around images to classify them, and learn algorithms—all emergent from its core design. This is just the beginning. A hint of what we're exploring next… (video attached!) The team: Luke Darlow Ciaran@ICML🇰🇷 Sebastian Risi Jeffrey Seely Llion Jones

We are excited to share that “Continuous Thought Machines” has been accepted as a Spotlight at #NeurIPS2025! 🧠✨ The CTM is an AI that mimics biological brains by using neural dynamics & synchronization to think over time. It can solve complex mazes by building internal maps, gaze around images to classify them, and learn algorithms—all emergent from its core design. This is just the beginning. A hint of what we're exploring next… (video attached!) The team: Luke Darlow Ciaran@ICML🇰🇷 Sebastian Risi Jeffrey Seely Llion Jones

168,620 views • 9 months ago

How do we reconstruct a 3D scene from photos with varying exposures? Standard methods often fail, leaving you with blown-out colors or disturbing shadows. We're excited to introduce Neural Exposure Fields (NExF), our new work accepted at #NeurIPS2025! 🧵

How do we reconstruct a 3D scene from photos with varying exposures? Standard methods often fail, leaving you with blown-out colors or disturbing shadows. We're excited to introduce Neural Exposure Fields (NExF), our new work accepted at #NeurIPS2025! 🧵

Michael Niemeyer

132,066 views • 9 months ago

On the latest Radical Talks podcast: Geoffrey Hinton X Jeff Dean - a friendship that defined modern AI as breakthrough theories met massive scale. Recorded at #NeurIPS2025, Radical Co-Founder Jordan Jacobs sits down with these two icons of AI to discuss one of history’s most productive collaborations.

On the latest Radical Talks podcast: Geoffrey Hinton X Jeff Dean - a friendship that defined modern AI as breakthrough theories met massive scale. Recorded at #NeurIPS2025, Radical Co-Founder Jordan Jacobs sits down with these two icons of AI to discuss one of history’s most productive collaborations.

Radical Ventures

64,387 views • 7 months ago

For those who missed our #NeurIPS2025 Visual Jenga poster presentation, I managed to record two iterations of our master presenter, Prof. Efros 😉 It's infectious to see Alyosha's energy and his excitement about research -- very inspiring. Enjoy! The full poster is in comments.

For those who missed our #NeurIPS2025 Visual Jenga poster presentation, I managed to record two iterations of our master presenter, Prof. Efros 😉 It's infectious to see Alyosha's energy and his excitement about research -- very inspiring. Enjoy! The full poster is in comments.

50,570 views • 7 months ago

Physical Intelligence Physical Intelligence coffee demo at #NeurIPS2025. What’s the hot take?

Physical Intelligence Physical Intelligence coffee demo at #NeurIPS2025. What’s the hot take?

37,683 views • 7 months ago

[1/N]🚀New decoding paradigm drop!🚀 Introducing Lookahead Reasoning(LR): step-level speculation that stacks with Speculative Decoding(SD). It has been accepted to #NeurIPS2025 🎉 📖 Blog: 💻 Code: 📄 Paper:

[1/N]🚀New decoding paradigm drop!🚀 Introducing Lookahead Reasoning(LR): step-level speculation that stacks with Speculative Decoding(SD). It has been accepted to #NeurIPS2025 🎉 📖 Blog: 💻 Code: 📄 Paper:

43,177 views • 10 months ago

Excited to share our #NeurIPS2025 work on learning motion hierarchies! We introduce a general hierarchical graph learning method that learns structured, interpretable motion directly from data, no prior structure or assumptions needed!!! Project and Paper: Amazing work led by William Koch, Cheng Zheng, and Baiang Li ! See us in San Diego for #NeurIPS2025!

Excited to share our #NeurIPS2025 work on learning motion hierarchies! We introduce a general hierarchical graph learning method that learns structured, interpretable motion directly from data, no prior structure or assumptions needed!!! Project and Paper: Amazing work led by William Koch, Cheng Zheng, and Baiang Li ! See us in San Diego for #NeurIPS2025!

25,314 views • 7 months ago

Unboxing time 🎴✨ Ivan Zhang cracks open the very first Lab Legends booster pack at #NeurIPS2025 Stop by the Cohere booth to collect your cards, trade, and build your legend Explore and build yours: #LabLegends

Unboxing time 🎴✨ Ivan Zhang cracks open the very first Lab Legends booster pack at #NeurIPS2025 Stop by the Cohere booth to collect your cards, trade, and build your legend Explore and build yours: #LabLegends

21,223 views • 7 months ago

🚀 Excited to attend my first #NeurIPS2025 and present our lighting-aware SLAM method, NFL-BA! 🔗 🔦 Why this matters: Traditional Bundle Adjustment (BA) in SLAM assumes static lighting, but many real scenarios—endoscopy, search & rescue, subterranean robotics—use co-located light + camera, creating dynamic, near-field lighting that breaks these assumptions. ✨ What we introduce: Near-Field Lighting Bundle Adjustment Loss (NFL-BA) — a formulation that explicitly models near-field illumination inside the BA objective, allowing SLAM systems to jointly reason about geometry, appearance, and lighting. 📈 Results: Modeling lighting directly leads to ~38% improvement in mapping & tracking across dynamic-lighting sequences in both endoscopy and indoor scenes. 🧩 Plug-and-play: NFL-BA integrates seamlessly into existing neural rendering–based SLAM pipelines, using both implicit (NeRF-style) or explicit (3DGS) scene representations. 🌟 If you’re interested in SLAM, neural rendering, or illumination modeling, come check it out on Friday's poster session, 11-2pm, #4407.

🚀 Excited to attend my first #NeurIPS2025 and present our lighting-aware SLAM method, NFL-BA! 🔗 🔦 Why this matters: Traditional Bundle Adjustment (BA) in SLAM assumes static lighting, but many real scenarios—endoscopy, search & rescue, subterranean robotics—use co-located light + camera, creating dynamic, near-field lighting that breaks these assumptions. ✨ What we introduce: Near-Field Lighting Bundle Adjustment Loss (NFL-BA) — a formulation that explicitly models near-field illumination inside the BA objective, allowing SLAM systems to jointly reason about geometry, appearance, and lighting. 📈 Results: Modeling lighting directly leads to ~38% improvement in mapping & tracking across dynamic-lighting sequences in both endoscopy and indoor scenes. 🧩 Plug-and-play: NFL-BA integrates seamlessly into existing neural rendering–based SLAM pipelines, using both implicit (NeRF-style) or explicit (3DGS) scene representations. 🌟 If you’re interested in SLAM, neural rendering, or illumination modeling, come check it out on Friday's poster session, 11-2pm, #4407.

21,162 views • 7 months ago

I'm uploading a recording here to make up for the cut-off during our onsite presentation NeurIPS Conference #NeurIPS2025. 🙏We are deeply grateful for your previous support and the encouragement to upload this recording. ⭐️Despite the interruption, our work was recognized with an Outstanding Paper award.

I'm uploading a recording here to make up for the cut-off during our onsite presentation NeurIPS Conference #NeurIPS2025. 🙏We are deeply grateful for your previous support and the encouragement to upload this recording. ⭐️Despite the interruption, our work was recognized with an Outstanding Paper award.

17,025 views • 7 months ago

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