1 code implementation • 26 Mar 2024 • Jin Peng Zhou, Charles Staats, Wenda Li, Christian Szegedy, Kilian Q. Weinberger, Yuhuai Wu
Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems.
1 code implementation • 7 Nov 2023 • Albert Q. Jiang, Wenda Li, Mateja Jamnik
In this work, we create $\texttt{MMA}$, a large, flexible, multilingual, and multi-domain dataset of informal-formal pairs, by using a language model to translate in the reverse direction, that is, from formal mathematical statements into corresponding informal ones.
no code implementations • 3 Jun 2023 • Wenda Li, KaiXuan Chen, Shunyu Liu, Wenjie Huang, Haofei Zhang, Yingjie Tian, Yun Su, Mingli Song
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines.
1 code implementation • 2 Jun 2023 • Katherine M. Collins, Albert Q. Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua B. Tenenbaum, William Hart, Timothy Gowers, Wenda Li, Adrian Weller, Mateja Jamnik
There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants.
1 code implementation • 25 May 2023 • Xueliang Zhao, Wenda Li, Lingpeng Kong
Large language models~(LLMs) present an intriguing avenue of exploration in the domain of formal theorem proving.
Ranked #3 on Automated Theorem Proving on miniF2F-test (Pass@100 metric)
no code implementations • 15 Mar 2023 • Yao Ge, Chong Tang, Haobo Li, Zikang Zhang, Wenda Li, Kevin Chetty, Daniele Faccio, Qammer H. Abbasi, Muhammad Imran
The dataset has been validated and has potential for the research of lip reading and multimodal speech recognition.
3 code implementations • 21 Oct 2022 • Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, Guillaume Lample
In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems.
Ranked #3 on Automated Theorem Proving on miniF2F-valid (Pass@100 metric)
no code implementations • 25 May 2022 • Yuhuai Wu, Albert Q. Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy
Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs.
Ranked #1 on Automated Theorem Proving on miniF2F-test (using extra training data)
no code implementations • 22 May 2022 • Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź, Piotr Miłoś, Yuhuai Wu, Mateja Jamnik
Thor increases a language model's success rate on the PISA dataset from $39\%$ to $57\%$, while solving $8. 2\%$ of problems neither language models nor automated theorem provers are able to solve on their own.
Ranked #2 on Automated Theorem Proving on miniF2F-test
no code implementations • 11 Jan 2022 • Chong Tang, Wenda Li, Shelly Vishwakarma, Fangzhan Shi, Simon Julier, Kevin Chetty
It provides an effective solution to track human activities by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way.
1 code implementation • 8 Oct 2021 • Mohammud J. Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raul Santos-Rodriguez, Kevin Chetty, Robert Piechocki
This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.
no code implementations • 27 Jul 2021 • Shelly Vishwakarma, Wenda Li, Chong Tang, Karl Woodbridge, Raviraj Adve, Kevin Chetty
Further, we benchmark the data augmentation performance of the style transferred signatures with three other synthetic datasets -- clean simulated spectrograms (no environmental effects), simulated data with added AWGN noise, and simulated data with GAN generated noise.
no code implementations • 9 Jul 2021 • Chong Tang, Wenda Li, Shelly Vishwakarma, Fangzhan Shi, Simon Julier, Kevin Chetty
On the other hand, we also propose a novel idea which trains a classifier with only simulated data and predicts new measured samples after cleaning them up with the FMNet.
no code implementations • 18 Mar 2021 • Yordanka Karayaneva, Sara Sharifzadeh, Wenda Li, Yanguo Jing, Bo Tan
This study proposes two unsupervised feature extraction methods for the purpose of human activity monitoring using Doppler-streams.
no code implementations • 2 Mar 2021 • Shelly Vishwakarma, Wenda Li, Chong Tang, Karl Woodbridge, Raviraj Adve, Kevin Chetty
We integrate WiFi transmission signals with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics, and the sensor parameters.
no code implementations • 13 Feb 2021 • Chong Tang, Wenda Li, Shelly Vishwakarma, Karl Woodbridge, Simon Julier, Kevin Chetty
However, noisy time-frequency spectrograms can significantly affect the performance of the classifier and must be tackled using appropriate denoising algorithms.
1 code implementation • 15 Jan 2021 • Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, Christian Szegedy
While designing inductive bias in neural architectures has been widely studied, we hypothesize that transformer networks are flexible enough to learn inductive bias from suitable generic tasks.
2 code implementations • ICLR 2021 • Wenda Li, Lei Yu, Yuhuai Wu, Lawrence C. Paulson
In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models.