Beatrix M. G. Nielsen
@beatrixmgn.bsky.social
PhD student in machine learning at DTU, Copenhagen.
Especially interested in model representations.
Especially interested in model representations.
5/5
@ema-ridopoco.bsky.social and @andreadittadi.bsky.social will be presenting a poster in San Diego and Luigi and I will be presenting at Eurips (eurips.cc) in Copenhagen so come on by! 😄
@ema-ridopoco.bsky.social and @andreadittadi.bsky.social will be presenting a poster in San Diego and Luigi and I will be presenting at Eurips (eurips.cc) in Copenhagen so come on by! 😄
EurIPS Copenhagen 2025 - A NeurIPS-endorsed conference in Europe
A NeurIPS-endorsed conference in Europe held in Copenhagen, Denmark
eurips.cc
October 21, 2025 at 5:49 AM
5/5
@ema-ridopoco.bsky.social and @andreadittadi.bsky.social will be presenting a poster in San Diego and Luigi and I will be presenting at Eurips (eurips.cc) in Copenhagen so come on by! 😄
@ema-ridopoco.bsky.social and @andreadittadi.bsky.social will be presenting a poster in San Diego and Luigi and I will be presenting at Eurips (eurips.cc) in Copenhagen so come on by! 😄
4/5
We also show that it is possible to define a metric between probability distributions and a measure of representational dissimilarity such that when distributions are close in this sense, we get similar representations.
We also show that it is possible to define a metric between probability distributions and a measure of representational dissimilarity such that when distributions are close in this sense, we get similar representations.
October 21, 2025 at 5:48 AM
4/5
We also show that it is possible to define a metric between probability distributions and a measure of representational dissimilarity such that when distributions are close in this sense, we get similar representations.
We also show that it is possible to define a metric between probability distributions and a measure of representational dissimilarity such that when distributions are close in this sense, we get similar representations.
3/5
The two models agree on their prediction for the highest likelihood label. They also disagree on the ranking by likelihood of the remaining labels, and while this has a negligible effect on the KL divergence, it means the relation between their representations is non-linear.
The two models agree on their prediction for the highest likelihood label. They also disagree on the ranking by likelihood of the remaining labels, and while this has a negligible effect on the KL divergence, it means the relation between their representations is non-linear.
October 21, 2025 at 5:48 AM
3/5
The two models agree on their prediction for the highest likelihood label. They also disagree on the ranking by likelihood of the remaining labels, and while this has a negligible effect on the KL divergence, it means the relation between their representations is non-linear.
The two models agree on their prediction for the highest likelihood label. They also disagree on the ranking by likelihood of the remaining labels, and while this has a negligible effect on the KL divergence, it means the relation between their representations is non-linear.
2/5
We prove that a small KL divergence between models is not enough to guarantee similar representations. Here is an example of how to construct two models with small KL divergence, but representations which are far from being linear transformations of each other.
We prove that a small KL divergence between models is not enough to guarantee similar representations. Here is an example of how to construct two models with small KL divergence, but representations which are far from being linear transformations of each other.
October 21, 2025 at 5:48 AM
2/5
We prove that a small KL divergence between models is not enough to guarantee similar representations. Here is an example of how to construct two models with small KL divergence, but representations which are far from being linear transformations of each other.
We prove that a small KL divergence between models is not enough to guarantee similar representations. Here is an example of how to construct two models with small KL divergence, but representations which are far from being linear transformations of each other.
1/5
We study when and why representations learned by different neural networks are similar from the perspective of identifiability theory, which suggests that a measure of representational similarity should be invariant to transformations that leave the model distribution unchanged.
We study when and why representations learned by different neural networks are similar from the perspective of identifiability theory, which suggests that a measure of representational similarity should be invariant to transformations that leave the model distribution unchanged.
October 21, 2025 at 5:47 AM
1/5
We study when and why representations learned by different neural networks are similar from the perspective of identifiability theory, which suggests that a measure of representational similarity should be invariant to transformations that leave the model distribution unchanged.
We study when and why representations learned by different neural networks are similar from the perspective of identifiability theory, which suggests that a measure of representational similarity should be invariant to transformations that leave the model distribution unchanged.
So if you did not have the opportunity to come by at ACL in Vienna then here is your second chance! :D
Hope to see you there! eurips.cc/ellis.
Hope to see you there! eurips.cc/ellis.
ELLIS UnConference - A NeurIPS-endorsed conference in Europe
A NeurIPS-endorsed conference in Europe held in Copenhagen, Denmark
eurips.cc
October 15, 2025 at 6:40 AM
So if you did not have the opportunity to come by at ACL in Vienna then here is your second chance! :D
Hope to see you there! eurips.cc/ellis.
Hope to see you there! eurips.cc/ellis.
Reposted by Beatrix M. G. Nielsen
- Fully funded PhD fellowship on Explainable NLU: apply by 31 October 2025, start in Spring 2026: candidate.hr-manager.net/ApplicationI...
- Open-topic PhD positions: express your interest through ELLIS by 31 October 2025, start in Autumn 2026: ellis.eu/news/ellis-p...
#NLProc #XAI
- Open-topic PhD positions: express your interest through ELLIS by 31 October 2025, start in Autumn 2026: ellis.eu/news/ellis-p...
#NLProc #XAI
PhD fellowship in Explainable Natural Language Understanding Department of Computer Science Faculty of SCIENCE University of Copenhagen
The Natural Language Processing Section at the Department of Computer Science, Faculty of Science at the University of Copenhagen invites applicants for a PhD f
candidate.hr-manager.net
September 1, 2025 at 2:20 PM
- Fully funded PhD fellowship on Explainable NLU: apply by 31 October 2025, start in Spring 2026: candidate.hr-manager.net/ApplicationI...
- Open-topic PhD positions: express your interest through ELLIS by 31 October 2025, start in Autumn 2026: ellis.eu/news/ellis-p...
#NLProc #XAI
- Open-topic PhD positions: express your interest through ELLIS by 31 October 2025, start in Autumn 2026: ellis.eu/news/ellis-p...
#NLProc #XAI