Symmetry-Based Representations for Artificial and Biological General Intelligence.

Biological intelligence is remarkable in its ability to produce complex behavior in many diverse situations through data efficient, generalizable, and transferable skill acquisition. It is believed that learning "good" sensory representations is important for enabling this, however there is little agreement as to what a good representation should look like. In this review article we are going to argue that symmetry transformations are a fundamental principle that can guide our search for what makes a good representation. The idea that there exist transformations (symmetries) that affect some aspects of the system but not others, and their relationship to conserved quantities has become central in modern physics, resulting in a more unified theoretical framework and even ability to predict the existence of new particles. Recently, symmetries have started to gain prominence in machine learning too, resulting in more data efficient and generalizable algorithms that can mimic some of the complex behaviors produced by biological intelligence. Finally, first demonstrations of the importance of symmetry transformations for representation learning in the brain are starting to arise in neuroscience. Taken together, the overwhelming positive effect that symmetries bring to these disciplines suggest that they may be an important general framework that determines the structure of the universe, constrains the nature of natural tasks and consequently shapes both biological and artificial intelligence.

Variational Information Bottleneck for Semi-Supervised Classification.

In this paper, we consider an information bottleneck (IB) framework for semi-supervised classification with several families of priors on latent space representation. We apply a variational decomposition of mutual information terms of IB. Using this decomposition we perform an analysis of several regularizers and practically demonstrate an impact of different components of variational model on the classification accuracy. We propose a new formulation of semi-supervised IB with hand crafted and learnable priors and link it to the previous methods such as semi-supervised versions of VAE (M1 + M2), AAE, CatGAN, etc. We show that the resulting model allows better understand the role of various previously proposed regularizers in semi-supervised classification task in the light of IB framework. The proposed IB semi-supervised model with hand-crafted and learnable priors is experimentally validated on MNIST under different amount of labeled data.

Equivariant Flow-Based Sampling for Lattice Gauge Theory.

We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that, at small bare coupling, the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as hybrid Monte Carlo and heat bath.

Targeted free energy estimation via learned mappings.

Free energy perturbation (FEP) was proposed by Zwanzig [J. Chem. Phys. 22, 1420 (1954)] more than six decades ago as a method to estimate free energy differences and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sampling based estimator, however, FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions. One strategy to mitigate this problem, called Targeted FEP, uses a high-dimensional mapping in configuration space to increase the overlap of the underlying distributions. Despite its potential, this method has attracted only limited attention due to the formidable challenge of formulating a tractable mapping. Here, we cast Targeted FEP as a machine learning problem in which the mapping is parameterized as a neural network that is optimized so as to increase the overlap. We develop a new model architecture that respects permutational and periodic symmetries often encountered in atomistic simulations and test our method on a fully periodic solvation system. We demonstrate that our method leads to a substantial variance reduction in free energy estimates when compared against baselines, without requiring any additional data.

Neural scene representation and rendering.

Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.

Building machines that learn and think for themselves.

We agree with Lake and colleagues on their list of "key ingredients" for building human-like intelligence, including the idea that model-based reasoning is essential. However, we favor an approach that centers on one additional ingredient: autonomy. In particular, we aim toward agents that can both build and exploit their own internal models, with minimal human hand engineering. We believe an approach centered on autonomous learning has the greatest chance of success as we scale toward real-world complexity, tackling domains for which ready-made formal models are not available. Here, we survey several important examples of the progress that has been made toward building autonomous agents with human-like abilities, and highlight some outstanding challenges.

Inference of perceptual priors from path dynamics of passive self-motion.

The monitoring of one's own spatial orientation depends on the ability to estimate successive self-motion cues accurately. This process has become to be known as path integration. A feature of sequential cue estimation, in general, is that the history of previously experienced stimuli, or priors, biases perception. Here, we investigate how during angular path integration, the prior imparted by the displacement path dynamics affects the translation of vestibular sensations into perceptual estimates. Subjects received successive whole-body yaw rotations and were instructed to report their position within a virtual scene after each rotation. The overall movement trajectory either followed a parabolic path or was devoid of explicit dynamics. In the latter case, estimates were biased toward the average stimulus prior and were well captured by an optimal Bayesian estimator model fit to the data. However, the use of parabolic paths reduced perceptual uncertainty, and a decrease of the average size of bias and thus the weight of the average stimulus prior were observed over time. The produced estimates were, in fact, better accounted for by a model where a prediction of rotation magnitude is inferred from the underlying path dynamics on each trial. Therefore, when passively displaced, we seem to be able to build, over time, from sequential vestibular measurements an internal model of the vehicle's movement dynamics. Our findings suggest that in ecological conditions, vestibular afference can be internally predicted, even when self-motion is not actively generated by the observer, thereby augmenting both the accuracy and precision of displacement perception.

Stochastic variational learning in recurrent spiking networks.

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Seguimento de casos de câncer de colo uterino nos serviços da Secretaria de Saúde, OSEGO, 1986 / Follow-up studies on cases of cervix neoplasm in health services of the Secretaria de Saúde, OSEGO, 1986

Tentando avaliar o funcionamento do Programa de Controle de Câncer, implantado na OSEGO há um ano, fez se o acompanhamento de 112 casos de displasia em mulheres clientes de 2 unidades hospitalares e quatro ambulatoriais, da OSEGO/SES. Tentou-se registrar o fluxo nos sistemas de referência e contra-referência, a conduta do serviço em cada caso, a interaçäo com a paciente e sua família, a aderência da cliente ao tratamento preconizado e as causas abandono, bem como avaliar a qualidade dos registros e o sistema de notificaçäo