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.

Q:   In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to storage, communication, or privacy constraints. In this work, we present Good-Enough Model Spaces (GEMS), a novel framework for learning a global satisficing (i.e. "good-enough") model within a few communication rounds by carefully combining the space of local nodes' satisficing models. In experiments on benchmark and medical datasets, our approach outperforms other baseline aggregation techniques such as ensembling or model averaging, and performs comparably to the ideal non-distributed models.


A: We present Good-Enough Model Spaces (GEMS), a framework for learning an aggregate model over distributed nodes within a small number of communication rounds.
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Q: Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e.g. systems that learn from human interaction. Thus, we develop a novel class of off-policy batch RL algorithms which use KL-control to penalize divergence from a pre-trained prior model of probable actions. This KL-constraint reduces extrapolation error, enabling effective offline learning, without exploration, from a fixed batch of data. We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning. This Way Off-Policy (WOP) algorithm is tested on both traditional RL tasks from OpenAI Gym, and on the problem of open-domain dialog generation; a challenging reinforcement learning problem with a 20,000 dimensional action space. WOP allows for the extraction of multiple different reward functions post-hoc from collected human interaction data, and can learn effectively from all of these. We test real-world generalization by deploying dialog models live to converse with humans in an open-domain setting, and demonstrate that WOP achieves significant improvements over state-of-the-art prior methods in batch deep RL.


A: We show that KL-control from a pre-trained prior can allow RL models to learn from a static batch of collected data, without the ability to explore online in the environment.
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Q: For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks. Activation maps, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs. One way to reduce this large memory footprint is to reduce the precision of activations. However, past works have shown that reducing the precision of activations hurts model accuracy. We study schemes to train networks from scratch using reduced-precision activations without hurting accuracy. We reduce the precision of activation maps (along with model parameters) and increase the number of filter maps in a layer, and find that this scheme matches or surpasses the accuracy of the baseline full-precision network. As a result, one can significantly improve the execution efficiency (e.g. reduce dynamic memory footprint, memory band- width and computational energy) and speed up the training and inference process with appropriate hardware support. We call our scheme WRPN -- wide reduced-precision networks. We report results and show that WRPN scheme is better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.

A:
Lowering precision (to 4-bits, 2-bits and even binary) and widening the filter banks gives as accurate network as those obtained with FP32 weights and activations.
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