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]: Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy. Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured. Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters. This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.
[A]: A method for learning quantization configuration for low precision networks that achieves state of the art performance for quantized networks.

[Q]: We propose a solution for evaluation of mathematical expression. However, instead of designing a single end-to-end model we propose a Lego bricks style architecture. In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick. More difficult or complex task can then be solved using a combination of these smaller network. In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, sign calculator etc). These fundamental operations are then learned using simple feed forward neural networks. We then shows that different operations can be designed simply by reusing these smaller networks. As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product. This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers. Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.
[A]: We train many small networks each for a specific operation, these are then combined to perform complex operations

[Q]: Emphasis effects visual changes that make certain elements more
prominent are commonly used in information visualization to draw
the user's attention or to indicate importance. Although theoretical
frameworks of emphasis exist (that link visually diverse emphasis
effects through the idea of visual prominence compared to background
elements), most metrics for predicting how emphasis effects
will be perceived by users come from abstract models of human
vision which may not apply to visualization design. In particular,
it is difficult for designers to know, when designing a visualization,
how different emphasis effects will compare and what level of one
effect is equivalent to what level of another. To address this gap,
we carried out two studies that provide empirical evidence about
how users perceive different emphasis effects, using three visual
variables (colour, size, and blur/focus) and eight strength levels.
Results from gaze tracking, mouse clicks, and subjective responses
show that there are significant differences between visual variables
and between levels, and allow us to develop an initial understanding
of perceptual equivalence. We developed a model from the data in
our first study, and used it to predict the results in the second; the
model was accurate, with high correlations between predictions and
real values. Our studies and empirical models provide valuable new
information for designers who want to understand and control how
emphasis effects will be perceived by users.
[A]:
Our studies and empirical models provide valuable new information for designers who want to understand and control how emphasis effects will be perceived by users