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Topic: interpretability

Seminar
2 seminars
Conference
1 conference
Job
1 job

In Machine Learning and Artificial Intelligence

Join TYREX to incorporate database schemas, constraints and rules into relational graph-neural-network message passing. The project investigates neurosymbolic aggregation and relational routes, creates annotated benchmarks, and evaluates accuracy, robustness, data efficiency and interpretability. The renewable 12-month appointment has a planned start on 1 December 2026 and gross monthly pay of EUR 2,788. Telework may be possible after six months.

Seminar · Computational Linguistics

SQI Seminar Series: Tal Linzen, NYU

Tal Linzen · New York University; Google

Tue, Sep 15, 2026 · 20:00 UTC

Tal Linzen, an associate professor of linguistics and data science at New York University and a research scientist at Google, will speak in MIT's Siegel Family Quest for Intelligence seminar series. His work combines behavioral experiments and computational methods to study language learning and comprehension, alongside large-language-model post-training, evaluation, and interpretability.

Conference · Artificial Intelligence

EMBO | EMBL Symposium: AI and biology 2026

Mar 10–13, 2026

Archive listing for the EMBO | EMBL Symposium held on 10–13 March 2026 at EMBL Heidelberg and online. The meeting connected researchers applying artificial intelligence to sequencing, imaging and structural biology. Programme themes included foundation models, learning across data types, interpretability, generative modelling and translating algorithms into biological insight. The official event page retains the programme and speaker information. This event has ended; registration and abstract submission are closed.

Seminar · Computational Neuroscience

Probing neural population dynamics with recurrent neural networks

Chethan Pandarinath · Emory University and Georgia Tech

Wed, Jun 12, 2024 · 13:00 UTC

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics with unprecedented detail. However, the sheer volume of data and its dynamical complexity are major barriers to uncovering and interpreting these dynamics. I will present latent factor analysis via dynamical systems, a sequential autoencoding approach that enables inference of dynamics from neuronal population spiking activity on single trials and millisecond timescales. I will also discuss recent adaptations of the method to uncover dynamics from neural activity recorded via 2P Calcium im