scholarly journals Stepping Stones to Inductive Synthesis of Low-Level Looping Programs

2019 ◽  
Vol 33 ◽  
pp. 2362-2370 ◽  
Author(s):  
Christopher D. Rosin
Keyword(s):  
State Of The Art ◽  
Assembly Language ◽  
Program Synthesis ◽  
Input Output ◽  
Stepping Stones ◽  
Inductive Synthesis ◽  
Low Level ◽  
Domain Specific ◽  
Assembly Language Programming ◽  
Algorithmic Form

Inductive program synthesis, from input/output examples, can provide an opportunity to automatically create programs from scratch without presupposing the algorithmic form of the solution. For induction of general programs with loops (as opposed to loop-free programs, or synthesis for domain-specific languages), the state of the art is at the level of introductory programming assignments. Most problems that require algorithmic subtlety, such as fast sorting, have remained out of reach without the benefit of significant problem-specific background knowledge. A key challenge is to identify cues that are available to guide search towards correct looping programs. We present MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes low-level looping programs from input/output examples. During search, delayed acceptance bypasses small gains to identify significantly-improved stepping stone programs that tend to generalize and enable further progress. The method performs well on a set of established benchmarks, and succeeds on the previously unsolved “Collatz Numbers” program synthesis problem. Additional benchmarks include the problem of rapidly sorting integer arrays, in which we observe the emergence of comb sort (a Shell sort variant that is empirically fast). MAKESPEARE has also synthesized a record-setting program on one of the puzzles from the TIS100 assembly language programming game.

scholarly journals Gauss: program synthesis by reasoning over graphs

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-29
Author(s):  
Rohan Bavishi ◽  
Caroline Lemieux ◽  
Koushik Sen ◽  
Ion Stoica
Keyword(s):  
State Of The Art ◽  
Program Synthesis ◽  
Search Space ◽  
Input Output ◽  
Natural Form ◽  
Synthesis Algorithm ◽  
User Intent ◽  
Speed Up ◽  
Available Information ◽  
Reasoning Algorithm

While input-output examples are a natural form of specification for program synthesis engines, they can be imprecise for domains such as table transformations. In this paper, we investigate how extracting readily-available information about the user intent behind these input-output examples helps speed up synthesis and reduce overfitting. We present Gauss, a synthesis algorithm for table transformations that accepts partial input-output examples, along with user intent graphs. Gauss includes a novel conflict-resolution reasoning algorithm over graphs that enables it to learn from mistakes made during the search and use that knowledge to explore the space of programs even faster. It also ensures the final program is consistent with the user intent specification, reducing overfitting. We implement Gauss for the domain of table transformations (supporting Pandas and R), and compare it to three state-of-the-art synthesizers accepting only input-output examples. We find that it is able to reduce the search space by 56×, 73× and 664× on average, resulting in 7×, 26× and 7× speedups in synthesis times on average, respectively.

A Matter of Perspective: How adults represent causal launching sequences

2020 ◽  
Author(s):  
Deon T. Benton ◽  
David H. Rakison
Keyword(s):  
Specific Mechanism ◽  
Associative Mechanism ◽  
Low Level ◽  
Domain Specific ◽  
The World ◽  
Modular Domain ◽  
Stimulus Features

The ability to reason about causal events in the world is fundamental to cognition. Despite the importance of this ability, little is known about how adults represent causal events, what structure or form those representations take, and what the mechanism is that underpins such representations. We report four experiments with adults that examine the perceptual basis on which adults represent four-object launching sequences (Experiments 1 and 2), whether adults representations reflect sensitivity to the causal, perceptual, or causal and perceptual relation among the objects that comprise such sequences (Experiment 3), and whether such representations extend beyond spatiotemporal contiguity to include other low-level stimulus features such as an object’s shape and color (Experiment 4). Based on these results of the four experiments, we argue that a domain-general associative mechanism, rather a modular, domain-specific, mechanism subserves adults’ representations of four-object launching sequences.

scholarly journals PedNet: A Spatio-Temporal Deep Convolutional Neural Network for Pedestrian Segmentation

2018 ◽  
Vol 4 (9) ◽  
pp. 107 ◽  
Author(s):  
Mohib Ullah ◽  
Ahmed Mohammed ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Performance Metrics ◽  
State Of The Art ◽  
Temporal Information ◽  
Feature Maps ◽  
Current Frame ◽  
Low Level ◽  
Art Methods ◽  
Spatio Temporal

Articulation modeling, feature extraction, and classification are the important components of pedestrian segmentation. Usually, these components are modeled independently from each other and then combined in a sequential way. However, this approach is prone to poor segmentation if any individual component is weakly designed. To cope with this problem, we proposed a spatio-temporal convolutional neural network named PedNet which exploits temporal information for spatial segmentation. The backbone of the PedNet consists of an encoder–decoder network for downsampling and upsampling the feature maps, respectively. The input to the network is a set of three frames and the output is a binary mask of the segmented regions in the middle frame. Irrespective of classical deep models where the convolution layers are followed by a fully connected layer for classification, PedNet is a Fully Convolutional Network (FCN). It is trained end-to-end and the segmentation is achieved without the need of any pre- or post-processing. The main characteristic of PedNet is its unique design where it performs segmentation on a frame-by-frame basis but it uses the temporal information from the previous and the future frame for segmenting the pedestrian in the current frame. Moreover, to combine the low-level features with the high-level semantic information learned by the deeper layers, we used long-skip connections from the encoder to decoder network and concatenate the output of low-level layers with the higher level layers. This approach helps to get segmentation map with sharp boundaries. To show the potential benefits of temporal information, we also visualized different layers of the network. The visualization showed that the network learned different information from the consecutive frames and then combined the information optimally to segment the middle frame. We evaluated our approach on eight challenging datasets where humans are involved in different activities with severe articulation (football, road crossing, surveillance). The most common CamVid dataset which is used for calculating the performance of the segmentation algorithm is evaluated against seven state-of-the-art methods. The performance is shown on precision/recall, F 1 , F 2 , and mIoU. The qualitative and quantitative results show that PedNet achieves promising results against state-of-the-art methods with substantial improvement in terms of all the performance metrics.

Assembly Language Programming

2013 ◽  
pp. 155-218
Author(s):  
Manuel Jiménez ◽  
Rogelio Palomera ◽  
Isidoro Couvertier
Keyword(s):  
Assembly Language ◽  
Assembly Language Programming

scholarly journals Saliency Detection by Multilevel Deep Pyramid Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hai Wang ◽  
Lei Dai ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Yong Zhang
Keyword(s):  
Background Noise ◽  
State Of The Art ◽  
Saliency Detection ◽  
Saliency Map ◽  
Multiple Features ◽  
Low Level ◽  
Pyramid Model ◽  
High Level ◽  
Different Levels ◽  
Better Than

Traditional salient object detection models are divided into several classes based on low-level features and contrast between pixels. In this paper, we propose a model based on a multilevel deep pyramid (MLDP), which involves fusing multiple features on different levels. Firstly, the MLDP uses the original image as the input for a VGG16 model to extract high-level features and form an initial saliency map. Next, the MLDP further extracts high-level features to form a saliency map based on a deep pyramid. Then, the MLDP obtains the salient map fused with superpixels by extracting low-level features. After that, the MLDP applies background noise filtering to the saliency map fused with superpixels in order to filter out the interference of background noise and form a saliency map based on the foreground. Lastly, the MLDP combines the saliency map fused with the superpixels with the saliency map based on the foreground, which results in the final saliency map. The MLDP is not limited to low-level features while it fuses multiple features and achieves good results when extracting salient targets. As can be seen in our experiment section, the MLDP is better than the other 7 state-of-the-art models across three different public saliency datasets. Therefore, the MLDP has superiority and wide applicability in extraction of salient targets.

scholarly journals Synthesizing Action Sequences for Modifying Model Decisions

2020 ◽  
Vol 34 (04) ◽  
pp. 5462-5469
Author(s):  
Goutham Ramakrishnan ◽  
Yun Chan Lee ◽  
Aws Albarghouthi
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Program Synthesis ◽  
Test Time ◽  
Domain Specific ◽  
Action Sequences

When a model makes a consequential decision, e.g., denying someone a loan, it needs to additionally generate actionable, realistic feedback on what the person can do to favorably change the decision. We cast this problem through the lens of program synthesis, in which our goal is to synthesize an optimal (realistically cheapest or simplest) sequence of actions that if a person executes successfully can change their classification. We present a novel and general approach that combines search-based program synthesis and test-time adversarial attacks to construct action sequences over a domain-specific set of actions. We demonstrate the effectiveness of our approach on a number of deep neural networks.

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