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03/08/2023
Magnushammer: A Transformer-based Approach to Premise Selection
Premise selection is a fundamental problem of automated theorem proving....
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05/25/2022
Autoformalization with Large Language Models
Autoformalization is the process of automatically translating from natur...
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03/16/2022
Memorizing Transformers
Language models typically need to be trained or finetuned in order to ac...
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10/26/2021
Hierarchical Transformers Are More Efficient Language Models
Transformer models yield impressive results on many NLP and sequence mod...
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01/15/2021
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
While designing inductive bias in neural architectures has been widely s...
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06/08/2020
Mathematical Reasoning via Self-supervised Skip-tree Training
We examine whether self-supervised language modeling applied to mathemat...
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09/26/2019
Mathematical Reasoning in Latent Space
We design and conduct a simple experiment to study whether neural networ...
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05/25/2019
Learning to Reason in Large Theories without Imitation
Automated theorem proving in large theories can be learned via reinforce...
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05/24/2019
Graph Representations for Higher-Order Logic and Theorem Proving
This paper presents the first use of graph neural networks (GNNs) for hi...
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04/05/2019
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving (extended version)
We present an environment, benchmark, and deep learning driven automated...
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04/05/2019
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving
We present an environment, benchmark, and deep learning driven automated...
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10/24/2018
Text Embeddings for Retrieval From a Large Knowledge Base
Text embedding representing natural language documents in a semantic vec...
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03/01/2017
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Large computer-understandable proofs consist of millions of intermediate...
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01/24/2017
Deep Network Guided Proof Search
Deep learning techniques lie at the heart of several significant AI adva...
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06/14/2016
DeepMath - Deep Sequence Models for Premise Selection
We study the effectiveness of neural sequence models for premise selecti...
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02/23/2016
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Very deep convolutional networks have been central to the largest advanc...
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12/17/2015
Large Scale Business Discovery from Street Level Imagery
Search with local intent is becoming increasingly useful due to the popu...
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12/08/2015
SSD: Single Shot MultiBox Detector
We present a method for detecting objects in images using a single deep ...
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12/02/2015
Rethinking the Inception Architecture for Computer Vision
Convolutional networks are at the core of most state-of-the-art computer...
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02/11/2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Training Deep Neural Networks is complicated by the fact that the distri...
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12/20/2014
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Current state-of-the-art deep learning systems for visual object recogni...
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12/20/2014
Explaining and Harnessing Adversarial Examples
Several machine learning models, including neural networks, consistently...
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12/03/2014
Scalable, High-Quality Object Detection
Current high-quality object detection approaches use the scheme of salie...
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09/17/2014
Going Deeper with Convolutions
We propose a deep convolutional neural network architecture codenamed "I...
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12/21/2013
Intriguing properties of neural networks
Deep neural networks are highly expressive models that have recently ach...
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12/17/2013
DeepPose: Human Pose Estimation via Deep Neural Networks
We propose a method for human pose estimation based on Deep Neural Netwo...
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12/08/2013