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Related papers: Autoencoders as Tools for Program Synthesis

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This work presents StrAE: a Structured Autoencoder framework that through strict adherence to explicit structure, and use of a novel contrastive objective over tree-structured representations, enables effective learning of multi-level…

Computation and Language · Computer Science 2025-02-25 Mattia Opper , Victor Prokhorov , N. Siddharth

While modern biotechnologies allow synthesizing new proteins and function measurements at scale, efficiently exploring a protein sequence space and engineering it remains a daunting task due to the vast sequence space of any given protein.…

Biomolecules · Quantitative Biology 2024-01-15 Jiahao Qiu , Hui Yuan , Jinghong Zhang , Wentao Chen , Huazheng Wang , Mengdi Wang

Recent Language Models (LMs) achieve breakthrough performance in code generation when trained on human-authored problems, even solving some competitive-programming problems. Self-play has proven useful in games such as Go, and thus it is…

Machine Learning · Computer Science 2023-04-13 Patrick Haluptzok , Matthew Bowers , Adam Tauman Kalai

Data generation and analysis is a fundamental aspect of many industries and disciplines, from strategic decision making in business to research in the physical and social sciences. However, data generated using software and algorithms can…

Software Engineering · Computer Science 2023-10-19 Ernesto Giralt Hernández

Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing. We introduce such a model for the task of machine translation, pairing a…

Computation and Language · Computer Science 2017-09-07 James Bradbury , Richard Socher

Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths) and discrete parameters (tree topology). Traditional Markov…

Populations and Evolution · Quantitative Biology 2024-12-30 ChenRui Duan , Zelin Zang , Siyuan Li , Yongjie Xu , Stan Z. Li

Code generation aims to automatically generate a piece of code given an input natural language utterance. Currently, among dominant models, it is treated as a sequence-to-tree task, where a decoder outputs a sequence of actions…

Artificial Intelligence · Computer Science 2021-06-01 Binbin Xie , Jinsong Su , Yubin Ge , Xiang Li , Jianwei Cui , Junfeng Yao , Bin Wang

Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for…

Model-driven engineering is the automatic production of software artefacts from abstract models of structure and functionality. By targeting a specific class of system, it is possible to automate aspects of the development process, using…

Software Engineering · Computer Science 2013-01-03 Chen-Wei Wang , Jim Davies

Training neural networks for source separation involves presenting a mixture recording at the input of the network and updating network parameters in order to produce an output that resembles the clean source. Consequently, supervised…

Sound · Computer Science 2019-05-10 Shrikant Venkataramani , Efthymios Tzinis , Paris Smaragdis

Diffusion language models offer a compelling alternative to autoregressive code generation, enabling global planning and iterative refinement of complex program logic. However, existing approaches fail to respect the rigid structure of…

Machine Learning · Computer Science 2026-02-23 Anton Xue , Litu Rout , Constantine Caramanis , Sanjay Shakkottai

Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive…

Machine Learning · Statistics 2016-05-30 Casper Kaae Sønderby , Tapani Raiko , Lars Maaløe , Søren Kaae Sønderby , Ole Winther

Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code. However, most pre-trained models for code intelligence ignore the execution trace and only rely on source code and…

Programming Languages · Computer Science 2023-05-10 Chenxiao Liu , Shuai Lu , Weizhu Chen , Daxin Jiang , Alexey Svyatkovskiy , Shengyu Fu , Neel Sundaresan , Nan Duan

Learning representation for source code is a foundation of many program analysis tasks. In recent years, neural networks have already shown success in this area, but most existing models did not make full use of the unique structural…

Software Engineering · Computer Science 2021-04-02 Wenhan Wang , Ge Li , Sijie Shen , Xin Xia , Zhi Jin

Dynamic Programming Languages are quite popular because they increase the programmer's productivity. However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines…

Programming Languages · Computer Science 2019-01-17 Abhinav Jangda , Gaurav Anand

Incorporating syntactic information in Neural Machine Translation models is a method to compensate their requirement for a large amount of parallel training text, especially for low-resource language pairs. Previous works on using syntactic…

Computation and Language · Computer Science 2017-11-27 Poorya Zaremoodi , Gholamreza Haffari

Although neural end-to-end text-to-speech models can synthesize highly natural speech, there is still room for improvements to its efficiency and naturalness. This paper proposes a non-autoregressive neural text-to-speech model augmented…

Sound · Computer Science 2020-10-23 Isaac Elias , Heiga Zen , Jonathan Shen , Yu Zhang , Ye Jia , Ron Weiss , Yonghui Wu

We present new techniques for synthesizing programs through sequences of mutations. Among these are (1) a method of local scoring assigning a score to each expression in a program, allowing us to more precisely identify buggy code, (2)…

Neural and Evolutionary Computing · Computer Science 2023-01-26 Max Vistrup

One of the challenges for optimizing compilers is to predict whether applying an optimization will improve its execution speed. Programmers may override the compiler's profitability heuristic using optimization directives such as pragmas in…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-10-14 Michael Kruse , Hal Finkel , Xingfu Wu

Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models, particularly for real-time applications. While traditional optimization approaches like pruning and quantization often compromise model…

Computation and Language · Computer Science 2025-10-09 Yunhai Hu , Zining Liu , Zhenyuan Dong , Tianfan Peng , Bradley McDanel , Sai Qian Zhang
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