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Found 5 papers, 1 papers with code
Bridging Associative Memory and Probabilistic Modeling
Based on the observation that associative memory's energy functions can be seen as probabilistic modeling's negative log likelihoods, we build a bridge between the two that enables useful flow of ideas in both directions.
Disentangling Fact from Grid Cell Fiction in Trained Deep Path Integrators
Work on deep learning-based models of grid cells suggests that grid cells generically and robustly arise from optimizing networks to path integrate, i. e., track one's spatial position by integrating self-velocity signals.
Testing Assumptions Underlying a Unified Theory for the Origin of Grid Cells
At both the population and single-cell levels, we find evidence suggesting that neither of the assumptions are likely true in biological neural representations.
Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle
Double descent is a surprising phenomenon in machine learning, in which as the number of model parameters grows relative to the number of data, test error drops as models grow ever larger into the highly overparameterized (data undersampled) regime.
Streaming Inference for Infinite Non-Stationary Clustering
Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents.
