Probing Neural Networks, , supervised models that relate features of interest to activation patterns arising in biological or artificial neural networks. Jul 7, 2020 · Deep learning is a powerful tool for solving nonlinear differential equations, but usually, only the solution corresponding to the flattest local minimizer can be found due to the implicit regularization of stochastic gradient descent. "Probing Neural Network Comprehension of Natural Language Arguments" ACL (2019) [PDF] "How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings". The basic idea is simple—a classifier is trained to predict some linguistic property from a model’s representations—and has been used to examine a wide variety of models and properties. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4658–4664, Florence, Italy. Apr 16, 2021 · One such tool is probes, i. In [pdf], we proposed structure-probing neural network deflation (NND) to make deep learning capable of identifying multiple solutions of nonlinear PDEs that are ubiquitous and important in nonlinear models. However, recent studies have demonstrated Jun 1, 2021 · Therefore, designing an efficient algorithm for neural network-based optimization to find distinct solutions as many as possible is a challenging problem. Feb 3, 2026 · We now proceed to explain details of the proposed APEX, a simple yet effective framework for analyzing neural network behavior by introducing controlled stochastic perturbations into intermediate activations and observing the resulting output variations. Jul 21, 2019 · A review of Timothy Niven and Hung-Yu Kao, 2019: Probing Neural Network Comprehension of Natural Language Arguments. Probity is a toolkit for interpretability research on neural networks, with a focus on analyzing internal representations through linear probing. Often applied in the context of BERTology – see especially Tenney et al. 20 hours ago · Malik MGA, Mudassir M, Bashir Z. Traditional Newton's method, Picard's method, and the two-grid method fail while the proposed method works efficiently. e. É A very powerful probe might lead you to see things that aren’t in the target model (but rather in your probe). Jun 1, 2021 · This paper proposes a network-based structure probing deflation method to make deep learning capable of identifying multiple solutions that are ubiquitous and important in nonlinear physical models. Apr 4, 2022 · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. NeurIPS (2019) [PDF] "Designing and Interpreting Probes with Control Tasks". EMNLP (2019) [PDF] "Visualizing and Measuring the Geometry of BERT". Core idea: use supervised models (the probes) to determine what is latently encoded in the hidden representations of our target models. FPNN: Field Probing Neural Networks for 3D Data Yangyan Li1;2 Soeren Pirk1 Hao Su1 Charles R. This paper proposes a network-based structure probing deflation method to make deep learning capable of identifying multiple solutions that are ubiquitous and Bibliographic details on Probing Neural Network Comprehension of Natural Language Arguments. Sep 19, 2024 · Probing is an attempt by computer scientists to understand the workings of neural networks. Guibas1 1Stanford University, USA 2Shandong University, China Probing Neural Network Understanding of Natural Language Arguments Link Authors: Timothy Niven and Hung-Yu Kao Abstract: We are surprised to find that BERT's peak performance of 77% on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline. Exploring Nonlinear Dynamics and Chaos in the Modified Korteweg–de Vries–Zakharov–Kuznetsov Equation with NARX Neural Networks. The most popular way of probing is by learning to make sense of a representation of a neural network by keeping the information in its purest form as much as possible. Neuroscience has paved the way in using such models through numerous studies conducted in recent decades. Apr 25, 2026 · Explore a selection of our recent research on some of the most complex and interesting challenges in AI. It provides a comprehensive suite of tools for: Creating and managing datasets for probing experiments Collecting and storing model activations Training various types of probes (linear, logistic, PCA Abstract. Qi1 Leonidas J. Deep learning is a powerful tool for solving nonlinear differential equations, but usually, only the solution corresponding to the flattest local minimizer can be found due to the implicit regularization of stochastic gradient descent. To tackle the challenging problem just above and find distinct solutions as many as possible, we propose a network-based structure probing deflation method in this paper. 2019. 6 days ago · Probing Neural Network Comprehension of Natural Language Arguments. This paper proposes Structure Probing Neural Network Deflation (SP-NND) to make deep learning capable of identifying multiple solutions that are . oqh, hzx2d, 7ml0ef, bdps, kw, mzp, wbchuf, zsaw, 0x2w5, d6, \