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Found 27 papers, 9 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.
On the Limitations of Elo: Real-World Games, are Transitive, not Additive
In this study, we investigate the challenge of identifying the strength of the transitive component in games.
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.
Open-Ended Learning Leads to Generally Capable Agents
The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem.
Behavior Priors for Efficient Reinforcement Learning
In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts.
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).
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.
A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern
The work "Loss Landscape Sightseeing with Multi-Point Optimization" (Skorokhodov and Burtsev, 2019) demonstrated that one can empirically find arbitrary 2D binary patterns inside loss surfaces of popular neural networks.
Distilling Policy Distillation
The transfer of knowledge from one policy to another is an important tool in Deep Reinforcement Learning.
Mix&Match - Agent Curricula for Reinforcement Learning
(2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state.
Distral: Robust Multitask Reinforcement Learning
Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning.
Grounded Language Learning in a Simulated 3D World
Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.
Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.
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Sobolev Training for Neural Networks
In many cases we only have access to input-output pairs from the ground truth, however it is becoming more common to have access to derivatives of the target output with respect to the input - for example when the ground truth function is itself a neural network such as in network compression or distillation.
Understanding Synthetic Gradients and Decoupled Neural Interfaces
When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking - without waiting for a true error gradient to be backpropagated - resulting in Decoupled Neural Interfaces (DNIs).
On Loss Functions for Deep Neural Networks in Classification
Deep neural networks are currently among the most commonly used classifiers.
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
Maybe the single most important goal of representation learning is making subsequent learning faster.
Local minima in training of neural networks
Given that deep networks are highly nonlinear systems optimized by local gradient methods, why do they not seem to be affected by bad local minima?
Reinforcement Learning with Unsupervised Auxiliary Tasks
We also introduce a novel mechanism for focusing this representation upon extrinsic rewards, so that learning can rapidly adapt to the most relevant aspects of the actual task.
Decoupled Neural Interfaces using Synthetic Gradients
Training directed neural networks typically requires forward-propagating data through a computation graph, followed by backpropagating error signal, to produce weight updates.
Learning to SMILE(S)
This paper shows how one can directly apply natural language processing (NLP) methods to classification problems in cheminformatics.
On the consistency of Multithreshold Entropy Linear Classifier
Multithreshold Entropy Linear Classifier (MELC) is a recent classifier idea which employs information theoretic concept in order to create a multithreshold maximum margin model.
Fast optimization of Multithreshold Entropy Linear Classifier
Multithreshold Entropy Linear Classifier (MELC) is a density based model which searches for a linear projection maximizing the Cauchy-Schwarz Divergence of dataset kernel density estimation.
Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation
Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods.
Extreme Entropy Machines: Robust information theoretic classification
The main contribution of this paper is proposing a model based on the information theoretic concepts which on the one hand shows new, entropic perspective on known linear classifiers and on the other leads to a construction of very robust method competetitive with the state of the art non-information theoretic ones (including Support Vector Machines and Extreme Learning Machines).
Cluster based RBF Kernel for Support Vector Machines
In the classical Gaussian SVM classification we use the feature space projection transforming points to normal distributions with fixed covariance matrices (identity in the standard RBF and the covariance of the whole dataset in Mahalanobis RBF).
Multithreshold Entropy Linear Classifier
Then we prove that our method is a multithreshold large margin classifier, which shows the analogy to the SVM, while in the same time works with much broader class of hypotheses.
