|
github.com
|
<span class="icon icon--github"><svg viewbox="0 0 16 16" width="24px" height="24px"><path fill="#828282" d="M7.999,0.431c-4.285,0-7.76,3.474-7.76,7.761 c0,3.428,2.223,6.337,5.307,7.363c0.388,0.071,0.53-0.168,0.53-0.374c0-0.184-0.007-0.672-0.01-1.32 c-2.159,0.469-2.614-1.04-2.614-1.04c-0.353-0.896-0.862-1.135-0.862-1.135c-0.705-0.481,0.053-0.472,0.053-0.472 c0.779,0.055,1.189,0.8,1.189,0.8c0.692,1.186,1.816,0.843,2.258,0.645c0.071-0.502,0.271-0.843,0.493-1.037 C4.86,11.425,3.049,10.76,3.049,7.786c0-0.847,0.302-1.54,0.799-2.082C3.768,5.507,3.501,4.718,3.924,3.65 c0,0,0.652-0.209,2.134,0.796C6.677,4.273,7.34,4.187,8,4.184c0.659,0.003,1.323,0.089,1.943,0.261 c1.482-1.004,2.132-0.796,2.132-0.796c0.423,1.068,0.157,1.857,0.077,2.054c0.497,0.542,0.798,1.235,0.798,2.082 c0,2.981-1.814,3.637-3.543,3.829c0.279,0.24,0.527,0.713,0.527,1.437c0,1.037-0.01,1.874-0.01,2.129 c0,0.208,0.14,0.449,0.534,0.373c3.081-1.028,5.302-3.935,5.302-7.362C15.76,3.906,12.285,0.431,7.999,0.431z"></path></svg>
</span>
|
|
twitter.com
|
<span class="icon icon--twitter"><svg viewbox="0 0 16 16" width="24px" height="24px"><path fill="#828282" d="M15.969,3.058c-0.586,0.26-1.217,0.436-1.878,0.515c0.675-0.405,1.194-1.045,1.438-1.809c-0.632,0.375-1.332,0.647-2.076,0.793c-0.596-0.636-1.446-1.033-2.387-1.033c-1.806,0-3.27,1.464-3.27,3.27 c0,0.256,0.029,0.506,0.085,0.745C5.163,5.404,2.753,4.102,1.14,2.124C0.859,2.607,0.698,3.168,0.698,3.767 c0,1.134,0.577,2.135,1.455,2.722C1.616,6.472,1.112,6.325,0.671,6.08c0,0.014,0,0.027,0,0.041c0,1.584,1.127,2.906,2.623,3.206 C3.02,9.402,2.731,9.442,2.433,9.442c-0.211,0-0.416-0.021-0.615-0.059c0.416,1.299,1.624,2.245,3.055,2.271 c-1.119,0.877-2.529,1.4-4.061,1.4c-0.264,0-0.524-0.015-0.78-0.046c1.447,0.928,3.166,1.469,5.013,1.469 c6.015,0,9.304-4.983,9.304-9.304c0-0.142-0.003-0.283-0.009-0.423C14.976,4.29,15.531,3.714,15.969,3.058z"></path></svg>
</span>
|
|
scholar.google.com
|
<span class="icon icon--scholar"><svg fill="#828282" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 47 47" width="24px" height="24px"><path d="M28.82,21c0,0.65-0.08,1.82-0.9,2.67c-0.58,0.59-1.55,1.03-2.46,1.03c-3.09,0-4.51-4.08-4.51-6.54c0-0.95,0.19-1.94,0.8-2.7 c0.58-0.75,1.59-1.22,2.52-1.22C27.27,14.24,28.82,18.35,28.82,21z"></path><path d="M26.88,30.65c2.74,1.98,3.92,2.97,3.92,4.84c0,2.27-1.83,3.97-5.3,3.97c-3.86,0-6.34-1.87-6.34-4.48s2.3-3.48,3.1-3.78 c1.51-0.52,3.46-0.59,3.78-0.59C26.41,30.61,26.59,30.61,26.88,30.65z"></path><path d="M25,3C12.85,3,3,12.85,3,25c0,12.15,9.85,22,22,22s22-9.85,22-22C47,12.85,37.15,3,25,3z M37,12v5.28 c0.6,0.34,1,0.98,1,1.72v6c0,1.1-0.9,2-2,2s-2-0.9-2-2v-6c0-0.74,0.4-1.38,1-1.72v-3.71l-3.59,3.08c0.35,0.7,0.59,1.55,0.59,2.59 c0,2.72-1.52,4.07-3.03,5.27c-0.47,0.48-1.02,1-1.02,1.81c0,0.8,0.55,1.25,0.95,1.57l1.29,1.03c1.59,1.36,3.03,2.61,3.03,5.14 c0,3.45-3.29,6.94-9.49,6.94c-5.23,0-7.75-2.53-7.75-5.25c0-1.32,0.65-3.19,2.78-4.48c2.23-1.39,5.26-1.58,6.89-1.69 c-0.51-0.66-1.09-1.35-1.09-2.5c0-0.61,0.19-0.98,0.37-1.43c-0.4,0.04-0.8,0.08-1.16,0.08c-3.8,0-5.97-2.88-5.99-5.73H11l10-9h17 l-1.02,0.88C36.98,11.92,37,11.96,37,12z"></path></svg></span>
|
|
arxiv.org
|
'Neural Message-Passing on Attention Graphs for Hallucination Detection'
|
|
arxiv.org
|
'Beyond Next Token Probabilities'
|
|
neurips.cc
|
best reviewers!
|
|
arxiv.org
|
'Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViT'
|
|
arxiv.org
|
'Neural Message-Passing on Attention Graphs for Hallucination Detection'
|
|
arxiv.org
|
position paper
|
|
youtube.com
|
here
|
|
drive.google.com
|
lecture
|
|
substack.com
|
first GLOW blogpost
|
|
arxiv.org
|
Understanding and Improving Laplacian Positional Encodings For Temporal GNNs
|
|
logml.ai
|
selected as a mentor
|
|
arxiv.org
|
Learning on LLM Output Signatures for Gray-Box Behavior Analysis
|
|
uncertainty-foundation-models.github.io
|
'Quantifying Uncertainty and Hallucination in Foundation Models'
|
|
arxiv.org
|
new preprint
|
|
arxiv.org
|
Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
|
|
arxiv.org
|
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
|
|
sites.google.com
|
Italian LoG 2024 Meetup in Siena
|
|
arxiv.org
|
Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
|
|
neurreps.org
|
NeurReps
|
|
neurips.cc
|
Top Reviewer
|
|
sites.google.com
|
GLOW (Graph Learning On Wednesdays)
|
|
arxiv.org
|
Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
|
|
neurreps.org
|
Symmetry and Geometry in Neural Representations (NeurReps)
|
|
arxiv.org
|
Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
|
|
sites.google.com
|
Italian LoG 2024 Meetup in Siena
|
|
arxiv.org
|
Future Directions in the Theory of Graph Machine Learning
|
|
imperial.ac.uk
|
Prof. Ben Glocker
|
|
wisdom.weizmann.ac.il
|
Prof. Yaron Lipman
|
|
arxiv.org
|
Edge Directionality Improves Learning on Heterophilic Graphs
|
|
arxiv.org
|
Graph Positional Encoding via Random Feature Propagation
|
|
twitter.com
|
give a talk as the penultimate lecture
|
|
arxiv.org
|
Graph Neural Networks for Link Prediction with Subgraph Sketching
|
|
youtube.com
|
video
|
|
twitter.com
|
Amazing experience
|
|
twitter.com
|
one of the twenty best reviewers
|
|
youtube.com
|
take a look
|
|
logconference.org
|
Learning on Graph 2022
|
|
arxiv.org
|
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
|
|
nips.cc
|
NeurIPS 2022
|
|
arxiv.org
|
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
|
|
nips.cc
|
NeurIPS 2022
|
|
geometricdeeplearning.com
|
Geometric Deep Learning AIMS 2022 course
|
|
logml.ai
|
LOGML 2022 summer school
|
|
towardsdatascience.com
|
blogpost
|
|
medium.com
|
blogpost
|
|
dagstuhl.de
|
Graph Embeddings: Theory meets Practice
|
|
twitter.com
|
three days with amazing researchers
|
|
twitter.com
|
interview
|
|
openreview.net
|
Equivariant Subgraph Aggregation Networks
|
|
openreview.net
|
Equivariant Subgraph Aggregation Networks
|
|
iclr.cc
|
ICLR 2022
|
|
youtube.com
|
Subgraphs for more expressive GNNs
|
|
nepalschool.naamii.com.np
|
2021 Nepal Winter School
|
|
towardsdatascience.com
|
Using Subgraphs for More Expressive GNNs
|
|
arxiv.org
|
Weisfeiler and Lehman Go Cellular: CW Networks
|
|
nips.cc
|
NeurIPS 2021
|
|
youtube.com
|
London ML Meetup
|
|
github.com
|
code
|
|
logml.ai
|
LOGML 2021 summer school
|
|
twitter.com
|
I managed to recognise my home town from the picture of a cup of espresso (!)'
|
|
twitter.com
|
blogpost
|
|
sites.google.com
|
TopoNets 2021
|
|
arxiv.org
|
Weisfeiler and Lehman Go Cellular: CW Networks
|
|
blog.twitter.com
|
post
|
|
arxiv.org
|
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
|
|
talks.cam.ac.uk
|
Cambridge's AI Research Talks
|
|
arxiv.org
|
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
|
|
icml.cc
|
ICML 2021
|
|
media.mis.mpg.de
|
presented
|
|
arxiv.org
|
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
|
|
arxiv.org
|
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
|
|
gt-rl.github.io
|
GTRL ICLR 2021 workshop
|
|
arxiv.org
|
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
|
|
arxiv.org
|
SIGN: Scalable Inception Graph Neural Networks
|
|
arxiv.org
|
Learning Interpretable Disease Self-Representations for Drug Repositioning
|
|
grlearning.github.io
|
NeurIPS 2019 Graph Representation Learning Workshop
|
|
arxiv.org
|
Learning Interpretable Disease Self-Representations for Drug Repositioning
|
|
blog.twitter.com
|
Fabula AI is being acquired
|
|
ipam.ucla.edu
|
Deep Geometric Learning of Big Data and Applications
|
|
jekyllrb.com
|
Jekyll
|
|
pages.github.com
|
Github Pages
|
|
github.com
|
orderedlist
|