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20172026
most citedAutoformalization with Large Language Models

43 citations · 111 across the 44 of their papers we have counts for

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Showing 2023Show all

9 papers · 1 filter

cs.CL20231 cited

Multilingual Mathematical Autoformalization

Albert Q. Jiang, Wenda Li, Mateja Jamnik

Autoformalization is the task of translating natural language materials into machine-verifiable formalisations. Progress in autoformalization research is hindered by the lack of a…

cs.LG2023

HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data

Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik

Technological advances in medical data collection, such as high-throughput genomic sequencing and digital high-resolution histopathology, have contributed to the rising requirement…

cs.LG2023

Learning to Receive Help: Intervention-Aware Concept Embedding Models

Mateo Espinosa Zarlenga, Katherine M. Collins, Krishnamurthy Dvijotham +3

Concept Bottleneck Models (CBMs) tackle the opacity of neural architectures by constructing and explaining their predictions using a set of high-level concepts. A special property…

cs.LG2023

Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs

Navindu Leelarathna, Andrei Margeloiu, Mateja Jamnik +1

Variational Autoencoders and their many variants have displayed impressive ability to perform dimensionality reduction, often achieving state-of-the-art performance. Many current m…

cs.LG2023

ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data

Xiangjian Jiang, Andrei Margeloiu, Nikola Simidjievski +1

Tabular biomedical data poses challenges in machine learning because it is often high-dimensional and typically low-sample-size (HDLSS). Previous research has attempted to address…

cs.LG2023

Evaluating Language Models for Mathematics through Interactions

Katherine M. Collins, Albert Q. Jiang, Simon Frieder +11

There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of e…