7 research outputs found
Grammar Filtering For Syntax-Guided Synthesis
Programming-by-example (PBE) is a synthesis paradigm that allows users to
generate functions by simply providing input-output examples. While a promising
interaction paradigm, synthesis is still too slow for realtime interaction and
more widespread adoption. Existing approaches to PBE synthesis have used
automated reasoning tools, such as SMT solvers, as well as works applying
machine learning techniques. At its core, the automated reasoning approach
relies on highly domain specific knowledge of programming languages. On the
other hand, the machine learning approaches utilize the fact that when working
with program code, it is possible to generate arbitrarily large training
datasets. In this work, we propose a system for using machine learning in
tandem with automated reasoning techniques to solve Syntax Guided Synthesis
(SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural
network, we can use a data driven approach to reduce the size of the search
space, then allow automated reasoning-based solvers to more quickly find a
solution analytically. Our system is able to run atop existing SyGuS PBE
synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS
Competition for the PBE Strings track by 47.65% to outperform all of the
competing tools
Video Question Answering with Iterative Video-Text Co-Tokenization
Video question answering is a challenging task that requires understanding
jointly the language input, the visual information in individual video frames,
as well as the temporal information about the events occurring in the video. In
this paper, we propose a novel multi-stream video encoder for video question
answering that uses multiple video inputs and a new video-text iterative
co-tokenization approach to answer a variety of questions related to videos. We
experimentally evaluate the model on several datasets, such as MSRVTT-QA,
MSVD-QA, IVQA, outperforming the previous state-of-the-art by large margins.
Simultaneously, our model reduces the required GFLOPs from 150-360 to only 67,
producing a highly efficient video question answering model.Comment: ECCV 202