Semantic Loss Functions for Neuro-Symbolic Structured Prediction
- URL: http://arxiv.org/abs/2405.07387v1
- Date: Sun, 12 May 2024 22:18:25 GMT
- Title: Semantic Loss Functions for Neuro-Symbolic Structured Prediction
- Authors: Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck,
- Abstract summary: We discuss the semantic loss, which injects knowledge about such structure, defined symbolically, into training.
It is agnostic to the arrangement of the symbols, and depends only on the semantics expressed thereby.
It can be combined with both discriminative and generative neural models.
- Score: 74.18322585177832
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Structured output prediction problems are ubiquitous in machine learning. The prominent approach leverages neural networks as powerful feature extractors, otherwise assuming the independence of the outputs. These outputs, however, jointly encode an object, e.g. a path in a graph, and are therefore related through the structure underlying the output space. We discuss the semantic loss, which injects knowledge about such structure, defined symbolically, into training by minimizing the network's violation of such dependencies, steering the network towards predicting distributions satisfying the underlying structure. At the same time, it is agnostic to the arrangement of the symbols, and depends only on the semantics expressed thereby, while also enabling efficient end-to-end training and inference. We also discuss key improvements and applications of the semantic loss. One limitations of the semantic loss is that it does not exploit the association of every data point with certain features certifying its membership in a target class. We should therefore prefer minimum-entropy distributions over valid structures, which we obtain by additionally minimizing the neuro-symbolic entropy. We empirically demonstrate the benefits of this more refined formulation. Moreover, the semantic loss is designed to be modular and can be combined with both discriminative and generative neural models. This is illustrated by integrating it into generative adversarial networks, yielding constrained adversarial networks, a novel class of deep generative models able to efficiently synthesize complex objects obeying the structure of the underlying domain.
Related papers
- Coding schemes in neural networks learning classification tasks [52.22978725954347]
We investigate fully-connected, wide neural networks learning classification tasks.
We show that the networks acquire strong, data-dependent features.
Surprisingly, the nature of the internal representations depends crucially on the neuronal nonlinearity.
arXiv Detail & Related papers (2024-06-24T14:50:05Z) - Using Degeneracy in the Loss Landscape for Mechanistic Interpretability [0.0]
Mechanistic Interpretability aims to reverse engineer the algorithms implemented by neural networks by studying their weights and activations.
An obstacle to reverse engineering neural networks is that many of the parameters inside a network are not involved in the computation being implemented by the network.
arXiv Detail & Related papers (2024-05-17T17:26:33Z) - Semantic Strengthening of Neuro-Symbolic Learning [85.6195120593625]
Neuro-symbolic approaches typically resort to fuzzy approximations of a probabilistic objective.
We show how to compute this efficiently for tractable circuits.
We test our approach on three tasks: predicting a minimum-cost path in Warcraft, predicting a minimum-cost perfect matching, and solving Sudoku puzzles.
arXiv Detail & Related papers (2023-02-28T00:04:22Z) - Isometric Representations in Neural Networks Improve Robustness [0.0]
We train neural networks to perform classification while simultaneously maintaining within-class metric structure.
We verify that isometric regularization improves the robustness to adversarial attacks on MNIST.
arXiv Detail & Related papers (2022-11-02T16:18:18Z) - Interpretable part-whole hierarchies and conceptual-semantic
relationships in neural networks [4.153804257347222]
We present Agglomerator, a framework capable of providing a representation of part-whole hierarchies from visual cues.
We evaluate our method on common datasets, such as SmallNORB, MNIST, FashionMNIST, CIFAR-10, and CIFAR-100.
arXiv Detail & Related papers (2022-03-07T10:56:13Z) - Modeling Structure with Undirected Neural Networks [20.506232306308977]
We propose undirected neural networks, a flexible framework for specifying computations that can be performed in any order.
We demonstrate the effectiveness of undirected neural architectures, both unstructured and structured, on a range of tasks.
arXiv Detail & Related papers (2022-02-08T10:06:51Z) - Dynamic Inference with Neural Interpreters [72.90231306252007]
We present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules.
inputs to the model are routed through a sequence of functions in a way that is end-to-end learned.
We show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner.
arXiv Detail & Related papers (2021-10-12T23:22:45Z) - Anomaly Detection on Attributed Networks via Contrastive Self-Supervised
Learning [50.24174211654775]
We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks.
Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair.
A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
arXiv Detail & Related papers (2021-02-27T03:17:20Z) - Towards Efficient Scene Understanding via Squeeze Reasoning [71.1139549949694]
We propose a novel framework called Squeeze Reasoning.
Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector.
We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks.
arXiv Detail & Related papers (2020-11-06T12:17:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.