Search Results for author:
Found 18 papers, 8 papers with code
IsarStep: a Benchmark for High-level Mathematical Reasoning
In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models.
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
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.
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram
However, noisy time-frequency spectrograms can significantly affect the performance of the classifier and must be tackled using appropriate denoising algorithms.
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition
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.
Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare
This study proposes two unsupervised feature extraction methods for the purpose of human activity monitoring using Doppler-streams.
FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram
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.
Neural Style Transfer Enhanced Training Support For Human Activity Recognition
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.
OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors
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.
MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures
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.
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
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
Autoformalization with Large Language Models
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)
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
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)
A large-scale multimodal dataset of human speech recognition
The dataset has been validated and has potential for the research of lip reading and multimodal speech recognition.
Decomposing the Enigma: Subgoal-based Demonstration Learning for Formal Theorem Proving
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)
Evaluating Language Models for Mathematics through Interactions
There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants.
Message-passing selection: Towards interpretable GNNs for graph classification
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.
Multilingual Mathematical Autoformalization
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.
Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems.
