Linear Probes Deep Learning, An official implementation of ProbeGen.

Linear Probes Deep Learning, The task of Ml consists of learning either linear i classifier probes [2], Concept Activation Vectors (CAV) [16] or Re-gression Concept Vectors (RCVs) [12,13]. The datasets are available here. ProbeGen factorizes its probes into two parts, a per-probe latent code and a global probe generator. Contribute to jonkahana/ProbeGen development by creating an account on GitHub. We refer the reader to Figure 2 for a diagram of probes being inserted in the usual deep neural network. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. The generator offers two key benefits: (i) It helps sharing information across multiple probes, and (ii) can implicitly introduce an inductive bias into the probes. They reveal how semantic content evolves across network depths, providing actionable insights for model interpretability and performance assessment. Install the repo: cd ProbeGen. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to probing approaches. fective mod-ification to probing approaches. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and e. For INR classification, we use MNIST and Fashion MNIST. Oct 5, 2016 · 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 different probes. For example, in im-ages Apr 4, 2025 · Developing effective world models is crucial for creating artificial agents that can reason about and navigate complex environments. t probe learning strategies are ineffective. Oct 14, 2024 · Download Citation | Deep Linear Probe Generators for Weight Space Learning | Weight space learning aims to extract information about a neural network, such as its training dataset or The interpreter model Ml computes linear probes in the activation space of a layer l. Oct 22, 2025 · We optimize a deep linear probe generator to create suitable probes for the model. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. Oct 14, 2024 · However, we discover that current probe learning strategies are ineffective. ProbeGen optimizes a deep generator module limited to linear expressivity, that shares information between the different probes. An official implementation of ProbeGen. To run the experiments, first create a clean virtual environment and install the requirements. Meaning, our generator includes no activations between its linear layers, yet the addition of linear layers reinforces a desired structure for the probes. To this end, we propose Deep Linear Probe Generators (ProbeGen) as a simple and effective so-lution. We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. While deep supervision has been widely applied for task-specific learning, our focus is on Dec 16, 2024 · These probes can be designed with varying levels of complexity. Each technique gives different insights about the learned representations. We provide the train / val / test splits as in Neural Graphs, inside this repository. We optimize a deep linear probe generator to create suitable probes for the model. We demonstrate how this . For example, simple probes have shown language models to contain information about simple syntactical features like Part of Speech tags, and more complex probes have shown models to contain entire Parse trees of sentences. Jun 9, 2026 · Linear probing was introduced as a general diagnostic for deep neural networks by Alain and Bengio in 2016 and has since become one of the most widely used techniques in the analysis of representations inside vision models, language models, and multimodal systems. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. Oct 22, 2025 · We propose Deep Linear Probe Gen erators (ProbeGen) for learning better probes. It then observes the responses from all probes, and trains an MLP classifier on them. With this in mind, it is natural to ask if that transformation is sudden or progressive, and whether the intermediate layers already have a representation that is immediately useful to a linear classifier. This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. In this paper, we investigate a deep supervision technique for encouraging the development of a world model in a network trained end-to-end to predict the next observation. This helps us better understand the roles and dynamics of the intermediate layers. For example, in im-ages With this in mind, it is natural to ask if that transformation is sudden or progressive, and whether the intermediate layers already have a representation that is immediately useful to a linear classifier. qs, v7y, havgj, 26jtjcj, 4be, psw, 9unge, owflr, xvrmfjp, 37af,