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pro vyhledávání: '"Pitkow, Xaq"'
This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question `How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question
Externí odkaz:
http://arxiv.org/abs/2409.02709
We develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control, or efficient control that neglects the cost of synthesizing information. Here we combine these co
Externí odkaz:
http://arxiv.org/abs/2406.14427
Recent work on object-centric world models aim to factorize representations in terms of objects in a completely unsupervised or self-supervised manner. Such world models are hypothesized to be a key component to address the generalization problem. Wh
Externí odkaz:
http://arxiv.org/abs/2401.00057
Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thu
Externí odkaz:
http://arxiv.org/abs/2310.03186
Autor:
Karantzas, Nikos, Besier, Emma, Caro, Josue Ortega, Pitkow, Xaq, Tolias, Andreas S., Patel, Ankit B., Anselmi, Fabio
Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain open questions. Recent literature suggests
Externí odkaz:
http://arxiv.org/abs/2203.08822
Many complex systems are composed of interacting parts, and the underlying laws are usually simple and universal. While graph neural networks provide a useful relational inductive bias for modeling such systems, generalization to new system instances
Externí odkaz:
http://arxiv.org/abs/2202.10996
Sampling and Variational Inference (VI) are two large families of methods for approximate inference that have complementary strengths. Sampling methods excel at approximating arbitrary probability distributions, but can be inefficient. VI methods are
Externí odkaz:
http://arxiv.org/abs/2110.09618
Autor:
Boominathan, Lokesh, Pitkow, Xaq
Sensory observations about the world are invariably ambiguous. Inference about the world's latent variables is thus an important computation for the brain. However, computational constraints limit the performance of these computations. These constrai
Externí odkaz:
http://arxiv.org/abs/2110.07873
Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a weighted
Externí odkaz:
http://arxiv.org/abs/2110.06871
Autor:
Fei, Yicheng, Pitkow, Xaq
Probabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for
Externí odkaz:
http://arxiv.org/abs/2107.05729