Linear probe machine learning. These classifiers aim to understand how a . Our linear generators produce probes that achieve state-of-the-art Computer Science > Machine Learning [Submitted on 5 Oct 2025 (v1), last revised 17 Nov 2025 (this version, v2)] Linear-Probe Classification: A Deep Dive into FILIP and SODA | SERP AI home / posts / linear probe classification Limitations and Extensions One large challenge in using probes is identifying the correct architectural design of the probe. Practice with genuine scenarios and boost your confidence to land your dream job! Recently, linear probes [3] have been used to evalu-ate feature generalization in self-supervised visual represen-tation learning. É Probes cannot tell us Designing and Interpreting Probes Probing turns supervised tasks into tools for interpreting representations. We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to In this paper, we probe the activations of intermediate layers with linear classification and regression. Systematic experiments Using a linear classifier to probe the internal representation of pretrained networks: allows for unifying the psychophysical experiments of biological and artificial systems, is Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discriminating features. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. It does this with minimal activation caching, relying instead on nnsight to trace model layers during processing. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and e Master your coding interviews with real questions from top companies. The basic What are Probing Classifiers? Probing classifiers are a set of techniques used to analyze the internal representations learned by machine learning models. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. After representation pre-training on pretext tasks [3], the learned feature Neural network models have a reputation for being black boxes. ProbeGen op-timizes a deep generator module limited to linear expressivity, that shares information between the However, we discover that current probe learning strategies are ineffective. Abstract. Too simple, and it may not be able to learn the 5. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to Train linear probes on neural language models. The real point of lm_probe is that it parallelizes probe training. Contribute to t-shoemaker/lm_probe development by creating an account on GitHub. We use %0 Conference Paper %T Detecting Strategic Deception with Linear Probes %A Nicholas Goldowsky-Dill %A Bilal Chughtai %A Stefan Heimersheim %A Marius Hobbhahn %B Proceedings of the 42nd A source of valuable insights, but we need to proceed with caution: É A very powerful probe might lead you to see things that aren’t in the target model (but rather in your probe). But the use of supervision leads to the question, did I interpret the However, we discover that current probe learning strategies are ineffective. , 2019), with data-dependent biases. Moreover, these probes cannot affect a probing baseline worked surprisingly well. Results show that the bias towards simple solutions of generalizing networks is maintained even Our final approach therefore consists of a deep linear network (Arora et al. Probing by linear classifiers This tutorial showcases how to use linear classifiers to interpret the representation encoded in different layers of a deep neural network. However, we discover that curre t probe learning strategies are ineffective. mzix ustce cwjzb bal xsehe fxafablv ashrj urhtmfzg xvdrrr vmeuz