Humans spontaneously perceive a continuous stream of experience as discrete
events. It has been hypothesized that this ability is supported by latent cause
inference (LCI). We implemented this hypothesis using Latent Cause Network
(LCNet), a neural network model of LCI. LCNet interacts with a Bayesian LCI
mechanism that activates a unique context vector for each inferred latent
cause. This architecture makes LCNet more biologically plausible than existing
models of LCI and supports extraction of shared structure across latent causes.
Across three simulations, we found that LCNet could 1) extract shared structure
across latent causes in a function-learning task while avoiding catastrophic
interference, 2) capture human data on curriculum effects in schema learning,
and 3) infer the underlying event structure when processing naturalistic videos
of daily activities. Our work provides a biologically plausible computational
model that can operate in both laboratory experiment settings and naturalistic
settings, opening up the possibility of providing a unified model of event
cognition.

通过实现一种基于神经网络模型的潜在因果推断（LCI）假设，本研究提供了一种生物学上可行的计算模型，该模型可以在实验室和自然环境设置中操作，从而为事件认知提供了统一的模型。

朝着更加生物可行的神经网络潜在因果推断模型的方向

Toward a More Biologically Plausible Neural Network Model of Latent  Cause Inference

Exploration in environments which differ across episodes has received
increasing attention in recent years. Current methods use some combination of
global novelty bonuses, computed using the agent's entire training experience,
and \textit{episodic novelty bonuses}, computed using only experience from the
current episode. However, the use of these two types of bonuses has been ad-hoc
and poorly understood. In this work, we shed light on the behavior of these two
types of bonuses through controlled experiments on easily interpretable tasks
as well as challenging pixel-based settings. We find that the two types of
bonuses succeed in different settings, with episodic bonuses being most
effective when there is little shared structure across episodes and global
bonuses being effective when more structure is shared. We develop a conceptual
framework which makes this notion of shared structure precise by considering
the variance of the value function across contexts, and which provides a
unifying explanation of our empirical results. We furthermore find that
combining the two bonuses can lead to more robust performance across different
degrees of shared structure, and investigate different algorithmic choices for
defining and combining global and episodic bonuses based on function
approximation. This results in an algorithm which sets a new state of the art
across 16 tasks from the MiniHack suite used in prior work, and also performs
robustly on Habitat and Montezuma's Revenge.

本文研究了不同环境下的探索问题，并通过在易于解释的任务和具有挑战性的像素环境中进行的实验，阐明了全局和特定情境新奖金的行为。我们发现，这两种奖金在不同的环境中均有成功的情况，其中在环境之间没有共享结构时，具有情境的奖金最为有效，而在存在更多共享结构时，全局奖金是有效的。我们对这种共享结构的概念框架进行了研究，并考虑了基于函数逼近的定义和组合全局和特定情境奖金的算法选择，从而提出了一种新技术，其表现优于过去的工作中使用的 MiniHack 套件的 16 项任务，同时在 Habitat 和 Montezuma's Revenge 上表现稳健。

基于上下文的 MDP 探索中全局和情景奖励研究

A Study of Global and Episodic Bonuses for Exploration in Contextual  MDPs

Continual learning aims to learn new tasks without forgetting previously
learned ones. We hypothesize that representations learned to solve each task in
a sequence have a shared structure while containing some task-specific
properties. We show that shared features are significantly less prone to
forgetting and propose a novel hybrid continual learning framework that learns
a disjoint representation for task-invariant and task-specific features
required to solve a sequence of tasks. Our model combines architecture growth
to prevent forgetting of task-specific skills and an experience replay approach
to preserve shared skills. We demonstrate our hybrid approach is effective in
avoiding forgetting and show it is superior to both architecture-based and
memory-based approaches on class incrementally learning of a single dataset as
well as a sequence of multiple datasets in image classification. Our code is
available at
https://github.com/facebookresearch/Adversarial-Continual-Learning.

本研究旨在使用混合方法来解决连续学习中遗忘的问题，该方法结合了体系结构生长来防止任务特定技能的遗忘，并采用经验回放方法来保存共享技能。研究结果表明，我们的混合方法在单个数据集和多个数据集的类增量学习中都表现优异。