Search Results for author:
Found 7 papers, 5 papers with code
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(HR@10 (full corpus) metric)
Synthesis of Stabilizing Recurrent Equilibrium Network Controllers
We propose a parameterization of a nonlinear dynamic controller based on the recurrent equilibrium network, a generalization of the recurrent neural network.
Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems
Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.
Implicit Graph Neural Networks
Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data.
Implicit Deep Learning
Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks.
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training
In this paper, we introduce a new class of lifted models, Fenchel lifted networks, that enjoy the same benefits as previous lifted models, without suffering a degradation in performance over classical networks.
Context-Aware Policy Reuse
Transfer learning can greatly speed up reinforcement learning for a new task by leveraging policies of relevant tasks.
