This paper describes CBGT-Net, a neural network model inspired by the
cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains.
Unlike traditional neural network models, which either generate an output for
each provided input, or an output after a fixed sequence of inputs, the
CBGT-Net learns to produce an output after a sufficient criteria for evidence
is achieved from a stream of observed data. For each observation, the CBGT-Net
generates a vector that explicitly represents the amount of evidence the
observation provides for each potential decision, accumulates the evidence over
time, and generates a decision when the accumulated evidence exceeds a
pre-defined threshold. We evaluate the proposed model on two image
classification tasks, where models need to predict image categories based on a
stream of small patches extracted from the image. We show that the CBGT-Net
provides improved accuracy and robustness compared to models trained to
classify from a single patch, and models leveraging an LSTM layer to classify
from a fixed sequence length of patches.

这篇论文描述了 CBGT-Net，这是一个受到哺乳动物大脑中的皮层 - 基底节 - 丘脑（CBGT）回路启发的神经网络模型。与传统的神经网络模型不同，该模型在观察到的数据流中学习在达到足够证据标准后产生输出。我们通过两个图像分类任务对该模型进行评估，其中模型需要根据从图像中提取的一系列小块来预测图像类别。我们证明 CBGT-Net 相比于单个小块进行分类的模型和利用 LSTM 层进行分类的模型，具有更高的准确性和鲁棒性。

CBGT-Net：面向流数据的鲁棒分类的神经构型

CBGT-Net: A Neuromimetic Architecture for Robust Classification of  Streaming Data

Deep Reinforcement Learning (RL) recently emerged as one of the most
competitive approaches for learning in sequential decision making problems with
fully observable environments, e.g., computer Go. However, very little work has
been done in deep RL to handle partially observable environments. We propose a
new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to
enhance learning performance in partially observable domains. Actions are
encoded by a fully connected layer and coupled with a convolutional observation
to form an action-observation pair. The time series of action-observation pairs
are then integrated by an LSTM layer that learns latent states based on which a
fully connected layer computes Q-values as in conventional Deep Q-Networks
(DQNs). We demonstrate the effectiveness of our new architecture in several
partially observable domains, including flickering Atari games.

提出了 Action-specific Deep Recurrent Q-Network (ADRQN) 的新体系结构，该体系结构使用 LSTM 层来学习潜在状态，以增强在多个部分可观察领域的学习性能，包括 Atari 游戏。

关于提高 POMDP 上深度强化学习的方法

On Improving Deep Reinforcement Learning for POMDPs

Time is an important relevance signal when searching streams of social media
posts. The distribution of document timestamps from the results of an initial
query can be leveraged to infer the distribution of relevant documents, which
can then be used to rerank the initial results. Previous experiments have shown
that kernel density estimation is a simple yet effective implementation of this
idea. This paper explores an alternative approach to mining temporal signals
with recurrent neural networks. Our intuition is that neural networks provide a
more expressive framework to capture the temporal coherence of neighboring
documents in time. To our knowledge, we are the first to integrate lexical and
temporal signals in an end-to-end neural network architecture, in which
existing neural ranking models are used to generate query-document similarity
vectors that feed into a bidirectional LSTM layer for temporal modeling. Our
results are mixed: existing neural models for document ranking alone yield
limited improvements over simple baselines, but the integration of lexical and
temporal signals yield significant improvements over competitive temporal
baselines.

本文中探索了一种利用递归神经网络来挖掘社交媒体中时间信号的替代方法，将已有的神经排序模型与双向 LSTM 层集成，以抓取相邻文档在时间上的时序连贯性，并且实验证明通过整合语义和时间信号可以显著提高排序的效果。

神经排序模型中的词汇和时间信号整合，用于搜索社交媒体流

Integrating Lexical and Temporal Signals in Neural Ranking Models for  Searching Social Media Streams

Current people detectors operate either by scanning an image in a sliding
window fashion or by classifying a discrete set of proposals. We propose a
model that is based on decoding an image into a set of people detections. Our
system takes an image as input and directly outputs a set of distinct detection
hypotheses. Because we generate predictions jointly, common post-processing
steps such as non-maximum suppression are unnecessary. We use a recurrent LSTM
layer for sequence generation and train our model end-to-end with a new loss
function that operates on sets of detections. We demonstrate the effectiveness
of our approach on the challenging task of detecting people in crowded scenes.

通过图像解码生成人员检测集合的模型，利用 LSTM 层进行序列生成，使用新的损失函数进行端到端的训练，并成功地解决了在拥挤场景中检测人员的挑战性任务。