Quentin Ferry (MIT): Emergence and Function of Abstract Representations in Self-Supervised Transformers

Michael Spratling (University of Luxembourg): A margin-based replacement for cross-entropy loss that improves the robustness of deep neural networks on image classification tasks

Luke Eilers (University of Bern): A generalized neural tangent kernel for surrogate gradient learning

Samuel Lippl (Columbia University): The impact of task structure, representational geometry, and learning mechanism on compositional generalization

Anita Keshmirian (Ludwig Maximilian University of Munich): Investigating Causal Judgments in Humans and Large Language Models

Sunayana Rane (Princeton): Can Generative Multimodal Models Count to Ten?

Michael Lepori (Brown): A Mechanistic Analysis of Same-Different Relations in ViTs

Paul Riechers (Beyond Institute for Theoretical Science; BITS): Computational mechanics predicts internal representations of transformers

Aly Lidayan (UC Berkeley): RL Algorithms Are BAMDP Policies: Understanding Exploration, Intrinsic Motivation, and Optimality

Nasir Ahmad (Donders Institute for Brain, Cognition and Behaviour): Correlations are ruining your gradient descent

Motahareh Pourrahimi (McGill; Mila): Human-like Behavior and Neural Representations Emerge in a Neural Network Trained to Search for Natural Objects from Pixels

Pablo Lanillos (Spanish National Research Council): Object-centric reasoning and control from pixels

Chiara Mastrogiuseppe (Universitat Pompeu Fabra): Controlled Maximal Variability Leads to Reliable Performance in Recurrent Neural Networks