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Showing papers from Swiss Federal Institute of Technology Lausanne Show all papers

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Shifting the Gradient: Understanding How Defensive Training Methods Protect Language Model Integrity

Satchel Grant, Victor Gillioz, Jake Ward, Tom McGrath

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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Hierarchical Concept Geometry in Language Representations Emerges from Word Co-occurrence

Andres Nava, Matthieu Wyart

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
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Position: Lottery Tickets Do Not Explain Overparameterization. How About Escape Dimensions?

Flavio Martinelli, Johanni Brea, Wulfram Gerstner

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 1/5
83%Must read
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Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping

Learned stochastic stopping reduces out-of-distribution variance in looped transformers by decoupling loop count from sequence length during training. It improves accuracy-stability trade-offs across algorithmic tasks, though it can stabilize suboptimal computation.

Hsun-Yu Kuo, El Mahdi Chayti, Patrik Reizinger, Wieland Brendel and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 2/5
70%Highly rated
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Stochastic Optimization with Random Search

Random search for stochastic optimization works under weaker smoothness assumptions and achieves faster convergence via variance-reduced variants using translation invariance to balance noise.

El Mahdi Chayti, Taha EL BAKKALI EL KADI, Omar Saadi, Martin Jaggi

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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4/20 AI panelreviewers recommend it

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AI panel: 4 of 20 reviewers recommend it
lenient 2/5
medium 2/10
strict 0/5
88%Must read
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Learn from your own latents and not from tokens: A sample-complexity theory

Latent prediction learns hierarchical latent trees with samples constant in depth L, exponentially more efficient than token-level self-supervision, making explicit multi-scale stacking largely redundant.

Daniel Korchinski, Alessandro Favero, Matthieu Wyart

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 3 on Hugging Face

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 3/5
71%Highly rated
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Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning

Neural LoFi frames deep training as iterative spectral low-degree filtering, predicting layer-wise feature selection, concept emergence, and compositional depth via low-degree correlation dynamics.

Yatin Dandi, Matteo Vilucchio, Luca Arnaboldi, Hugo Tabanelli and 1 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 0/5
78%Highly rated
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Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning

DYSCO uses multi-view contrastive learning to recover latent dynamics and governing equations from noisy high-dimensional data, with theoretical identification guarantees and empirical validation across diverse regimes.

Paolo Muratore, Mackenzie Mathis

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 3/5
72%Highly rated
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Eyes on VLM: Benchmarking Gaze Following and Social Gaze Prediction in Vision Language Models

EyeVLM benchmarks vision-language models on gaze following and social gaze prediction, finding they lack precise gaze understanding despite training improvements.

Hengfei Wang, Anshul Gupta, Pierre Vuillecard, Jean-marc Odobez

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 0/5