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Found 37 papers, 9 papers with code
Re-evaluating Evaluation
Progress in machine learning is measured by careful evaluation on problems of outstanding common interest.
Domain Generalization for Object Recognition with Multi-task Autoencoders
The problem of domain generalization is to take knowledge acquired from a number of related domains where training data is available, and to then successfully apply it to previously unseen domains.
The Mechanics of n-Player Differentiable Games
The first is related to potential games, which reduce to gradient descent on an implicit function; the second relates to Hamiltonian games, a new class of games that obey a conservation law, akin to conservation laws in classical mechanical systems.
Differentiable Game Mechanics
The decomposition motivates Symplectic Gradient Adjustment (SGA), a new algorithm for finding stable fixed points in differentiable games.
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition.
Real World Games Look Like Spinning Tops
This paper investigates the geometrical properties of real world games (e. g. Tic-Tac-Toe, Go, StarCraft II).
LOGAN: Latent Optimisation for Generative Adversarial Networks
Training generative adversarial networks requires balancing of delicate adversarial dynamics.
Domain generalization via invariant feature representation
This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains?
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
A long-standing obstacle to progress in deep learning is the problem of vanishing and exploding gradients.
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
Modern convolutional networks, incorporating rectifiers and max-pooling, are neither smooth nor convex; standard guarantees therefore do not apply.
Strongly-Typed Agents are Guaranteed to Interact Safely
As artificial agents proliferate, it is becoming increasingly important to ensure that their interactions with one another are well-behaved.
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
We propose Scatter Component Analyis (SCA), a fast representation learning algorithm that can be applied to both domain adaptation and domain generalization.
Ranked #7 on
Domain Adaptation
on Office-Caltech
Strongly-Typed Recurrent Neural Networks
This paper imports ideas from physics and functional programming into RNN design to provide guiding principles.
Deep Online Convex Optimization by Putting Forecaster to Sleep
The main result is that error backpropagation on a convolutional network is equivalent to playing out a circadian game.
Deep Online Convex Optimization with Gated Games
Corollaries of the main result include: (i) a game-theoretic description of the representations learned by a neural network; (ii) a logarithmic-regret algorithm for training neural nets; and (iii) a formal setting for analyzing conditional computation in neural nets that can be applied to recently developed models of attention.
Compliance-Aware Bandits
Motivated by clinical trials, we study bandits with observable non-compliance.
Semantics, Representations and Grammars for Deep Learning
Deep learning is currently the subject of intensive study.
Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies
Firstly, we present a temporal-difference based method for learning the gradient of the value-function.
Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks
Using these results, we derive a new credit assignment algorithm for nonparametric regression, Kickback, that is significantly simpler than Backprop.
Falsifiable implies Learnable
The paper demonstrates that falsifiability is fundamental to learning.
Cortical prediction markets
We investigate cortical learning from the perspective of mechanism design.
Correlated random features for fast semi-supervised learning
Random views are justified by recent theoretical and empirical work showing that regression with random features closely approximates kernel regression, implying that random views can be expected to contain accurate estimators.
Randomized co-training: from cortical neurons to machine learning and back again
Despite its size and complexity, the human cortex exhibits striking anatomical regularities, suggesting there may simple meta-algorithms underlying cortical learning and computation.
Uncovering the Temporal Dynamics of Diffusion Networks
Time plays an essential role in the diffusion of information, influence and disease over networks.
Stable Opponent Shaping in Differentiable Games
A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity to multi-agent RL.
Towards a learning-theoretic analysis of spike-timing dependent plasticity
This paper suggests a learning-theoretic perspective on how synaptic plasticity benefits global brain functioning.
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
We propose a novel Bayesian approach to solve stochastic optimization problems that involve finding extrema of noisy, nonlinear functions.
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'.
Detecting emergent processes in cellular automata with excess information
Many natural processes occur over characteristic spatial and temporal scales.
Information Theory Information Theory Cellular Automata and Lattice Gases Neurons and Cognition
Smooth markets: A basic mechanism for organizing gradient-based learners
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact.
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Adversarial training, a special case of multi-objective optimization, is an increasingly prevalent machine learning technique: some of its most notable applications include GAN-based generative modeling and self-play techniques in reinforcement learning which have been applied to complex games such as Go or Poker.
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG).
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Here we argue that a systematic study of many-player zero-sum games is a crucial element of artificial intelligence research.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics
Even simple games learning dynamics can yield chaotic behavior.
Quantifying causal influences
Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy.
Statistics Theory Statistics Theory
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of its performance.
Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution
We show that MARL converges to the desired outcome if the rewards are designed so that exerting effort is the iterated dominance solution, but fails if it is merely a Nash equilibrium.
