80 research outputs found
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Sequence tutor: Conservative fine-tuning of sequence generation models with KL-control
This paper proposes a general method for improving the structure and quality
of sequences generated by a recurrent neural network (RNN), while maintaining
information originally learned from data, as well as sample diversity. An RNN
is first pre-trained on data using maximum likelihood estimation (MLE), and the
probability distribution over the next token in the sequence learned by this
model is treated as a prior policy. Another RNN is then trained using
reinforcement learning (RL) to generate higher-quality outputs that account for
domain-specific incentives while retaining proximity to the prior policy of the
MLE RNN. To formalize this objective, we derive novel off-policy RL methods for
RNNs from KL-control. The effectiveness of the approach is demonstrated on two
applications; 1) generating novel musical melodies, and 2) computational
molecular generation. For both problems, we show that the proposed method
improves the desired properties and structure of the generated sequences, while
maintaining information learned from data
Neural Networks for Information Retrieval
Machine learning plays a role in many aspects of modern IR systems, and deep
learning is applied in all of them. The fast pace of modern-day research has
given rise to many different approaches for many different IR problems. The
amount of information available can be overwhelming both for junior students
and for experienced researchers looking for new research topics and directions.
Additionally, it is interesting to see what key insights into IR problems the
new technologies are able to give us. The aim of this full-day tutorial is to
give a clear overview of current tried-and-trusted neural methods in IR and how
they benefit IR research. It covers key architectures, as well as the most
promising future directions.Comment: Overview of full-day tutorial at SIGIR 201
Multimodal Data Fusion based on the Global Workspace Theory
We propose a novel neural network architecture, named the Global Workspace
Network (GWN), which addresses the challenge of dynamic and unspecified
uncertainties in multimodal data fusion. Our GWN is a model of attention across
modalities and evolving through time, and is inspired by the well-established
Global Workspace Theory from the field of cognitive science. The GWN achieved
average F1 score of 0.92 for discrimination between pain patients and healthy
participants and average F1 score = 0.75 for further classification of three
pain levels for a patient, both based on the multimodal EmoPain dataset
captured from people with chronic pain and healthy people performing different
types of exercise movements in unconstrained settings. In these tasks, the GWN
significantly outperforms the typical fusion approach of merging by
concatenation. We further provide extensive analysis of the behaviour of the
GWN and its ability to address uncertainties (hidden noise) in multimodal data.Comment: 12 pages, 5 figure
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
Deep text matching approaches have been widely studied for many applications
including question answering and information retrieval systems. To deal with a
domain that has insufficient labeled data, these approaches can be used in a
Transfer Learning (TL) setting to leverage labeled data from a resource-rich
source domain. To achieve better performance, source domain data selection is
essential in this process to prevent the "negative transfer" problem. However,
the emerging deep transfer models do not fit well with most existing data
selection methods, because the data selection policy and the transfer learning
model are not jointly trained, leading to sub-optimal training efficiency.
In this paper, we propose a novel reinforced data selector to select
high-quality source domain data to help the TL model. Specifically, the data
selector "acts" on the source domain data to find a subset for optimization of
the TL model, and the performance of the TL model can provide "rewards" in turn
to update the selector. We build the reinforced data selector based on the
actor-critic framework and integrate it to a DNN based transfer learning model,
resulting in a Reinforced Transfer Learning (RTL) method. We perform a thorough
experimental evaluation on two major tasks for text matching, namely,
paraphrase identification and natural language inference. Experimental results
show the proposed RTL can significantly improve the performance of the TL
model. We further investigate different settings of states, rewards, and policy
optimization methods to examine the robustness of our method. Last, we conduct
a case study on the selected data and find our method is able to select source
domain data whose Wasserstein distance is close to the target domain data. This
is reasonable and intuitive as such source domain data can provide more
transferability power to the model.Comment: Accepted to WSDM 201
ИССЛЕДОВАНИЕ ДЕМПФИРУЮЩИХ СВОЙСТВ МОДЕЛИ АДАПТИВНОГО АМОРТИЗАТОРА
The research of the designed sample of the adaptive shock absorber, designed forthe use in the systems of cushioning of trucks operating in off-road conditions is considered. The test scheme and the equipment used for their testing are given. Methods for controlling the accuracy characteristics of the measuring and control equipment, modeling control processes for obtaining the characteristics of the test sample are considered. The methods and program of testing procedure are given.Исследуется проектируемый образец адаптивного амортизатора, предназначенного для использования в системах подрессоривания грузовых автомобилей, работающих в условиях бездорожья. Приводится схема испытаний и используемое для их проведения оборудование. Дается описание методов контроля точностных характеристик измерительного и управляющего оборудования, а также методов моделирования управляющих процессов для получения характеристик исследуемого образца. Предлагаются программа и методика испытаний
Sequence tutor: Conservative fine-tuning of sequence generation models with KL-control
This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity. An RNN is first pre-trained on data using maximum likelihood estimation (MLE), and the probability distribution over the next token in the sequence learned by this model is treated as a prior policy. Another RNN is then trained using reinforcement learning (RL) to generate higher-quality outputs that account for domain-specific incentives while retaining proximity to the prior policy of the MLE RNN. To formalize this objective, we derive novel off-policy RL methods for RNNs from KL-control. The effectiveness of the approach is demonstrated on two applications; 1) generating novel musical melodies, and 2) computational molecular generation. For both problems, we show that the proposed method improves the desired properties and structure of the generated sequences, while maintaining information learned from data
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