jvgemert.bsky.social

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Researcher with public evidence across AI research, Vision & synthetic media, Compute & infrastructure.

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Head of the Computer Vision lab; TU Delft. - Fundamental, empirical, understanding-based, Deep Learning research - Visual inductive priors for data efficiency Web: https://jvgemert.github.io/

Articles & links

In addition to the research findings, this is the type of research I really enjoyed doing 😊 "On Hyperparameter Tuning on the Test Set" arxiv.org/abs/2610.05902

On Hyperparameter Tuning on the Test Set arxiv.org
AI Weekly's analysis →
  • A new arxiv paper by Fregonara, Viering and van Gemert tests test-set hyperparameter tuning on MNIST-1D, CIFAR-10 and three GLUE tasks.
  • The authors find the inflation effect is real and significant but frequently small relative to other sources of noise.
  • Model rankings remain essentially preserved after test-set tuning, so consistent test tuning may not invalidate benchmarks.
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View on Bluesky · ♥ 23 ↻ 4 ↩ 2 · 2 from the directory shared this · 17d ago

I really enjoy finding gaps in our understanding of text-book deep learning. The RNN Homunculus: Long-range input vanishes RNN gradients. Gates (GRU, LSTM) help, but gates are themselves an RNN. Ie: gates are not sufficient. We need short-range data to learn gates first. arxiv…

The RNN Homunculus: Learning to Gate Long-Term Memory arxiv.org
AI Weekly's analysis →
  • Gated RNNs can match or exceed Mamba and xLSTM test accuracy when their gates are learned properly, the authors claim.
  • The analysis finds gated architectures suffer the vanishing gradient problem when all training samples require long-range memory.
  • Adding short-dependency samples lets gated models learn solutions that capture long dependencies and generalize across class labels.
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View on Bluesky · ♥ 18 ↻ 3 ↩ 0 · 2 from the directory shared this · 9d ago

Recent commentary

Honered to get a selfie with our Netherlands Conference on Computer Vision (NCCV) keynote speaker! The brilliant @vickykalogeiton.bsky.social Two zealots of the Data efficiency church in the Religion of Scaling AI 🙏

View on Bluesky · ♥ 11 ↻ 0 ↩ 0 · 127d ago

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