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Found 10 papers, 3 papers with code
Principled Weight Initialization for Hypernetworks
Hypernetworks are meta neural networks that generate weights for a main neural network in an end-to-end differentiable manner.
Assessing SATNet's Ability to Solve the Symbol Grounding Problem
We propose an MNIST based test as an easy instance of the symbol grounding problem that can serve as a sanity check for differentiable symbolic solvers in general.
Chaos persists in large-scale multi-agent learning despite adaptive learning rates
Although such techniques are known to allow for improved convergence guarantees in small games, it has been much harder to analyze them in more relevant settings with large populations of agents.
Enabling SQL-based Training Data Debugging for Federated Learning
The SQL-based training data debugging framework has proved effective to fix this kind of issue in a non-federated learning setting.
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent
Inspired by this, we study standard gradient descent ascent (GDA) dynamics in a specific class of non-convex non-concave zero-sum games, that we call hidden zero-sum games.
No-regret learning and mixed Nash equilibria: They do not mix
Understanding the behavior of no-regret dynamics in general $N$-player games is a fundamental question in online learning and game theory.
Complaint-driven Training Data Debugging for Query 2.0
As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2. 0", which integrates model inference into SQL queries.
Efficiently avoiding saddle points with zero order methods: No gradients required
We consider the case of derivative-free algorithms for non-convex optimization, also known as zero order algorithms, that use only function evaluations rather than gradients.
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
We study a wide class of non-convex non-concave min-max games that generalizes over standard bilinear zero-sum games.
Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation
Furthermore, we applied ensemble learning on training models of intermediate epochs and achieved an improvement of 4. 02 BLEU points over the baseline.
