8 research outputs found
Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias
Machine Reading Comprehension (MRC) models tend to take advantage of spurious
correlations (also known as dataset bias or annotation artifacts in the
research community). Consequently, these models may perform the MRC task
without fully comprehending the given context and question, which is
undesirable since it may result in low robustness against distribution shift.
This paper delves into the concept of answer-position bias, where a significant
percentage of training questions have answers located solely in the first
sentence of the context. We propose a Single-Sentence Reader as a new approach
for addressing answer position bias in MRC. We implement this approach using
six different models and thoroughly analyze their performance. Remarkably, our
proposed Single-Sentence Readers achieve results that nearly match those of
models trained on conventional training sets, proving their effectiveness. Our
study also discusses several challenges our Single-Sentence Readers encounter
and proposes a potential solution.Comment: 11 pages, 5 tables, 2 figure
AGent: A Novel Pipeline for Automatically Creating Unanswerable Questions
The development of large high-quality datasets and high-performing models
have led to significant advancements in the domain of Extractive Question
Answering (EQA). This progress has sparked considerable interest in exploring
unanswerable questions within the EQA domain. Training EQA models with
unanswerable questions helps them avoid extracting misleading or incorrect
answers for queries that lack valid responses. However, manually annotating
unanswerable questions is labor-intensive. To address this, we propose AGent, a
novel pipeline that automatically creates new unanswerable questions by
re-matching a question with a context that lacks the necessary information for
a correct answer. In this paper, we demonstrate the usefulness of this AGent
pipeline by creating two sets of unanswerable questions from answerable
questions in SQuAD and HotpotQA. These created question sets exhibit low error
rates. Additionally, models fine-tuned on these questions show comparable
performance with those fine-tuned on the SQuAD 2.0 dataset on multiple EQA
benchmarks.Comment: 16 pages, 10 tables, 3 figure
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in data science. The group consisted of 25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in data science
Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil
An accurate simulation of a heating coil is used to compare the performance of a PI controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n-step ahead error between the coil output and the set point, and a reinforcement learning agent trained to minimize the sum of the squared error over time. Although the PI controller works very well for this task, the neural networks do result in improved performance. 1 Introduction Typical methods for designing fixed feedback controllers results in sub-optimal control performance. In many situations, the degree of uncertainty in the model of the system being controlled limits the utility of optimal control design. Building energy systems are particularly troublesome since the process gain is highly variable, depending on the load on components such as heating and cooling coils and on inlet conditions such as air temperature and air volume flow rate. Some of these issu..
Synthesis of Reinforcement Learning, Neural Networks, and PI Control Applied to a Simulated Heating Coil
An accurate simulation of a heating coil is used to compare the performance of a proportional plus integral (PI) controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n-step ahead error between the coil output and the set point, and a reinforcement learning agent trained to minimize the sum of the squared error over time. Although the PI controller works very well for this task, the neural networks produce improved performance. The reinforcement learning agent, when combined with a PI controller, learned to augment the PI control output for a small number of states for which control can be improved. Keywords: neural networks, reinforcement learning, PI control, HVAC 1 Introduction Typical methods for designing fixed feedback controllers results in sub-optimal control performance. In many situations, the degree of uncertainty in the model of the system being controlled limits the utility of optimal contro..
Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil
An accurate simulation of a heating coil is used to compare the performance of a PI controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n-step ahead error between the coil output and the set point, and a reinforcement learning agent trained to minimize the sum of the squared error over time. Although the PI controller works very well for this task, the neural networks do result in improved performance. 1 Introduction Typical methods for designing fixed feedback controllers results in sub-optimal control performance. In many situations, the degree of uncertainty in the model of the system being controlled limits the utility of optimal control design. Building energy systems are particularly troublesome since the process gain is highly variable, depending on the load on components such as heating and cooling coils and on inlet conditions such as air temperature and air volume flow rate. Some of the..