In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
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Question: In classic papers, Zellner (1988, 2002) demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional. Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective. This generalized functional encompasses most modern inference procedures and suggests novel ones.

Answer: Rederive a wide class of inference procedures from an global information bottleneck objective.

Question: In this work, we focus on the problem of grounding language by training an agent
to follow a set of natural language instructions and navigate to a target object
in a 2D grid environment. The agent receives visual information through raw
pixels and a natural language instruction telling what task needs to be achieved.
Other than these two sources of information, our model does not have any prior
information of both the visual and textual modalities and is end-to-end trainable.
We develop an attention mechanism for multi-modal fusion of visual and textual
modalities that allows the agent to learn to complete the navigation tasks and also
achieve language grounding. Our experimental results show that our attention
mechanism outperforms the existing multi-modal fusion mechanisms proposed in
order to solve the above mentioned navigation task. We demonstrate through the
visualization of attention weights that our model learns to correlate attributes of
the object referred in the instruction with visual representations and also show
that the learnt textual representations are semantically meaningful as they follow
vector arithmetic and are also consistent enough to induce translation between instructions
in different natural languages. We also show that our model generalizes
effectively to unseen scenarios and exhibit zero-shot generalization capabilities.
In order to simulate the above described challenges, we introduce a new 2D environment
for an agent to jointly learn visual and textual modalities

Answer: Attention based architecture for language grounding via reinforcement learning in a new customizable 2D grid environment

Question: Camera drones, a rapidly emerging technology, offer people the ability to remotely inspect an environment with a high degree of mobility and agility. However, manual remote piloting of a drone is prone to errors. In contrast, autopilot systems can require a significant degree of environmental knowledge and are not necessarily designed to support flexible visual inspections. Inspired by camera manipulation techniques in interactive graphics, we designed StarHopper, a novel touch screen interface for efficient object-centric camera drone navigation, in which a user directly specifies the navigation of a drone camera relative to a specified object of interest. The system relies on minimal environmental information and combines both manual and automated control mechanisms to give users the freedom to remotely explore an environment with efficiency and accuracy. A lab study shows that StarHopper offers an efficiency gain of 35.4% over manual piloting, complimented by an overall user preference towards our object-centric navigation system.

Answer:
StarHopper is a novel touch screen interface for efficient and flexible object-centric camera drone navigation