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pro vyhledávání: '"Kalyan, Ashwin"'
This work shows that value-aware model learning, known for its numerous theoretical benefits, is also practically viable for solving challenging continuous control tasks in prevalent model-based reinforcement learning algorithms. First, we derive a n
Externí odkaz:
http://arxiv.org/abs/2106.14080
We introduce a new type of programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis, and release an open-source dataset of Python Programming Puzzles (P3). Each puzzle is defined by a short P
Externí odkaz:
http://arxiv.org/abs/2106.05784
Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e.g. there are many ways of describing an image, multiple ways of translating a sentence; however, exh
Externí odkaz:
http://arxiv.org/abs/1806.02934
Autor:
Kalyan, Ashwin, Mohta, Abhishek, Polozov, Oleksandr, Batra, Dhruv, Jain, Prateek, Gulwani, Sumit
Synthesizing user-intended programs from a small number of input-output examples is a challenging problem with several important applications like spreadsheet manipulation, data wrangling and code refactoring. Existing synthesis systems either comple
Externí odkaz:
http://arxiv.org/abs/1804.01186
Autor:
Deshpande, Ameet, Jimenez, Carlos E., Chen, Howard, Murahari, Vishvak, Graf, Victoria, Rajpurohit, Tanmay, Kalyan, Ashwin, Chen, Danqi, Narasimhan, Karthik
Semantic textual similarity (STS) has been a cornerstone task in NLP that measures the degree of similarity between a pair of sentences, with applications in information retrieval, question answering, and embedding methods. However, it is an inherent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4aa8afcd6d10350e4304e5bf504a2550
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