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2021-12
Gradient Starvation: A Learning Proclivity in Neural Networks
2021-10
Compositional Attention: Disentangling Search and Retrieval
2021-09
Optimizing Neuroprosthetic Therapies via Autonomous Learning Agents
Learning function from structure in neuromorphic networks
2021-07
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance.
Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks.
2021-05
Efficient and robust multi-task learning in the brain with modular task primitives.
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks
2021-03
Implicit Regularization via Neural Feature Alignment
AISTATS 2021
(2021-03-18)
proceedings.mlr.pressPDF[Also on arXiv preprint arXiv:2008.00938 (2020-08-03)]Predictive learning as a network mechanism for extracting low-dimensional latent space representations.
2020-11
Gradient Starvation: A Learning Proclivity in Neural Networks
2020-10
LEAD: Least-Action Dynamics for Min-Max Optimization
2020-08
Implicit Regularization in Deep Learning: A View from Function Space.
2020-07
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
2020-06
On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools
Advantages of biologically-inspired adaptive neural activation in RNNs during learning.
Untangling tradeoffs between recurrence and self-attention in neural networks
2020-05
Low-Dimensional Dynamics of Encoding and Learning in Recurrent Neural Networks
2020-04
Hierarchical Bayesian Optimization of Spatiotemporal Neurostimulations for Targeted Motor Outputs
2020-01
Internal representation dynamics and geometry in recurrent neural networks.
Untangling tradeoffs between recurrence and self-attention in artificial neural networks
2019-09
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion
Modelling Working Memory using Deep Recurrent Reinforcement Learning
2019-07
Predictive learning extracts latent space representations from sensory observations
2019-06
Learning to evoke complex motor outputs with spatiotemporal neurostimulation using a hierarchical and adaptive optimization algorithm
Dimensionality compression and expansion in Deep Neural Networks.
2019-03
Dynamic compression and expansion in a classifying recurrent network
2019-01
Cortical network mechanisms of anodal and cathodal transcranial direct current stimulation in awake primates
Learning to evoke complex motor outputs with spatiotemporal neurostimulation using a hierarchical and adaptive optimization algorithm.
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
2018-11
Signatures and mechanisms of low-dimensional neural predictive manifolds
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