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Found 22 papers, 7 papers with code
Exploring the Space of Key-Value-Query Models with Intention
Our goal is to determine whether there are any other stackable models in KVQ Space that Attention cannot efficiently approximate, which we can implement with our current deep learning toolbox and that solve problems that are interesting to the community.
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.
Time-series Imputation of Temporally-occluded Multiagent Trajectories
In multiagent environments, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment.
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index
We propose a vocabulary selection method that views words as members of a team trying to maximize the model{'}s performance.
Game Plan: What AI can do for Football, and What Football can do for AI
The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis.
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision.
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.
AlignNet: Self-supervised Alignment Module
The natural world consists of objects that we perceive as persistent in space and time, even though these objects appear, disappear and reappear in our field of view as we move.
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.
Learning Truthful, Efficient, and Welfare Maximizing Auction Rules
From social networks to supply chains, more and more aspects of how humans, firms and organizations interact is mediated by artificial learning agents.
An Explicitly Relational Neural Network Architecture
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data.
Consistent Jumpy Predictions for Videos and Scenes
These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.
Meta-Learning surrogate models for sequential decision making
We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
The ability to generalize quickly from few observations is crucial for intelligent systems.
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'.
Attentive Neural Processes
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions.
Verification of deep probabilistic models
For example, a machine translation model should produce semantically equivalent outputs for innocuous changes in the input to the model.
Consistent Generative Query Networks
These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.
Neural Processes
A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.
Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models.
Ranked #7 on
Human Pose Forecasting
on HumanEva-I
Towards Deep Symbolic Reinforcement Learning
Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go.
