Simone Antonelli
@siantonelli.bsky.social
PhD student @cispa.de | Graph ML and Data Attribution | prev: ML intern @ amboss.tech
🌐 siantonelli.github.io
🌐 siantonelli.github.io
Reposted by Simone Antonelli
🔝 CISPA was awarded top marks in the Helmholtz Association's international evaluation.
The “Outstanding” grade recognizes a globally leading research position and groundbreaking work with major impact and disruptive societal or economic potential.
cispa.de/en/evaluation
The “Outstanding” grade recognizes a globally leading research position and groundbreaking work with major impact and disruptive societal or economic potential.
cispa.de/en/evaluation
June 18, 2025 at 3:14 PM
🔝 CISPA was awarded top marks in the Helmholtz Association's international evaluation.
The “Outstanding” grade recognizes a globally leading research position and groundbreaking work with major impact and disruptive societal or economic potential.
cispa.de/en/evaluation
The “Outstanding” grade recognizes a globally leading research position and groundbreaking work with major impact and disruptive societal or economic potential.
cispa.de/en/evaluation
Reposted by Simone Antonelli
📢 New paper CVPR 25!
Can meshes capture fuzzy geometry? Volumetric Surfaces uses adaptive textured shells to model hair, fur without the splatting / volume overhead. It’s fast, looks great, and runs in real time even on budget phones.
🔗 autonomousvision.github.io/volsurfs/
📄 arxiv.org/pdf/2409.02482
Can meshes capture fuzzy geometry? Volumetric Surfaces uses adaptive textured shells to model hair, fur without the splatting / volume overhead. It’s fast, looks great, and runs in real time even on budget phones.
🔗 autonomousvision.github.io/volsurfs/
📄 arxiv.org/pdf/2409.02482
May 5, 2025 at 1:00 PM
📢 New paper CVPR 25!
Can meshes capture fuzzy geometry? Volumetric Surfaces uses adaptive textured shells to model hair, fur without the splatting / volume overhead. It’s fast, looks great, and runs in real time even on budget phones.
🔗 autonomousvision.github.io/volsurfs/
📄 arxiv.org/pdf/2409.02482
Can meshes capture fuzzy geometry? Volumetric Surfaces uses adaptive textured shells to model hair, fur without the splatting / volume overhead. It’s fast, looks great, and runs in real time even on budget phones.
🔗 autonomousvision.github.io/volsurfs/
📄 arxiv.org/pdf/2409.02482
Reposted by Simone Antonelli
GLOW is returning on 𝗠𝗮𝗿𝗰𝗵 𝟮𝟲𝘁𝗵, 𝟱𝗽𝗺 𝗖𝗘𝗧 with a special guest: @petar-v.bsky.social 🌟
He will lecture on LLMs as GNNs – a topic which received quite some attention at our last session.
Specifically, we will learn how Graph ML tools can help understand LLM generalisation
He will lecture on LLMs as GNNs – a topic which received quite some attention at our last session.
Specifically, we will learn how Graph ML tools can help understand LLM generalisation
March 20, 2025 at 7:39 PM
GLOW is returning on 𝗠𝗮𝗿𝗰𝗵 𝟮𝟲𝘁𝗵, 𝟱𝗽𝗺 𝗖𝗘𝗧 with a special guest: @petar-v.bsky.social 🌟
He will lecture on LLMs as GNNs – a topic which received quite some attention at our last session.
Specifically, we will learn how Graph ML tools can help understand LLM generalisation
He will lecture on LLMs as GNNs – a topic which received quite some attention at our last session.
Specifically, we will learn how Graph ML tools can help understand LLM generalisation
Reposted by Simone Antonelli
🌟Applications open- LOGML 2025🌟
👥Mentor-led projects, expert talks, tutorials, socials, and a networking night
✍️Application form: logml.ai
🔬Projects: www.logml.ai/projects.html
📅Apply by 6th April 2025
✉️Questions? logml.committee@gmail.com
#MachineLearning #SummerSchool #LOGML #Geometry
👥Mentor-led projects, expert talks, tutorials, socials, and a networking night
✍️Application form: logml.ai
🔬Projects: www.logml.ai/projects.html
📅Apply by 6th April 2025
✉️Questions? logml.committee@gmail.com
#MachineLearning #SummerSchool #LOGML #Geometry
LOGML 2025
London Geometry and Machine Learning Summer School, July 7-11 2025
logml.ai
March 11, 2025 at 3:25 PM
🌟Applications open- LOGML 2025🌟
👥Mentor-led projects, expert talks, tutorials, socials, and a networking night
✍️Application form: logml.ai
🔬Projects: www.logml.ai/projects.html
📅Apply by 6th April 2025
✉️Questions? logml.committee@gmail.com
#MachineLearning #SummerSchool #LOGML #Geometry
👥Mentor-led projects, expert talks, tutorials, socials, and a networking night
✍️Application form: logml.ai
🔬Projects: www.logml.ai/projects.html
📅Apply by 6th April 2025
✉️Questions? logml.committee@gmail.com
#MachineLearning #SummerSchool #LOGML #Geometry
Reposted by Simone Antonelli
⭐️Mentor applications open⭐️
We're excited to announce that LOGML summer school will return in London: July 7-11 2025. We are seeking mentors to lead group projects at the intersection of geometry and machine learning. Find out more and apply:
logml.ai
We're excited to announce that LOGML summer school will return in London: July 7-11 2025. We are seeking mentors to lead group projects at the intersection of geometry and machine learning. Find out more and apply:
logml.ai
January 22, 2025 at 1:00 PM
⭐️Mentor applications open⭐️
We're excited to announce that LOGML summer school will return in London: July 7-11 2025. We are seeking mentors to lead group projects at the intersection of geometry and machine learning. Find out more and apply:
logml.ai
We're excited to announce that LOGML summer school will return in London: July 7-11 2025. We are seeking mentors to lead group projects at the intersection of geometry and machine learning. Find out more and apply:
logml.ai
Reposted by Simone Antonelli
📢Prepend “Singular” to “Task Vectors” and get +15% average accuracy for free!
1. Perform a low-rank approximation of layer-wise task vectors.
2. Minimize task interference by orthogonalizing inter-task singular vectors.
🧵(1/6)
1. Perform a low-rank approximation of layer-wise task vectors.
2. Minimize task interference by orthogonalizing inter-task singular vectors.
🧵(1/6)
January 8, 2025 at 7:00 PM
📢Prepend “Singular” to “Task Vectors” and get +15% average accuracy for free!
1. Perform a low-rank approximation of layer-wise task vectors.
2. Minimize task interference by orthogonalizing inter-task singular vectors.
🧵(1/6)
1. Perform a low-rank approximation of layer-wise task vectors.
2. Minimize task interference by orthogonalizing inter-task singular vectors.
🧵(1/6)