Neural Language of Thought Models

• URL: http://arxiv.org/abs/2402.01203v2
• Date: Tue, 16 Apr 2024 21:44:32 GMT
• Title: Neural Language of Thought Models
• Authors: Yi-Fu Wu, Minseung Lee, Sungjin Ahn,
• Abstract summary: We introduce the Neural Language of Thought Model (NLoTM), a novel approach for unsupervised learning of LoTH-inspired representation and generation. NLoTM comprises two key components: (1) the Semantic Vector-Quantized Variational Autoencoder, which learns hierarchical, composable discrete representations aligned with objects and their properties, and (2) the Autoregressive LoT Prior, an autoregressive transformer that learns to generate semantic concept tokens compositionally. We evaluate NLoTM on several 2D and 3D image datasets, demonstrating superior performance in downstream tasks, out-of-distribution generalization, and image generation
• Score: 18.930227757853313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Language of Thought Hypothesis suggests that human cognition operates on a structured, language-like system of mental representations. While neural language models can naturally benefit from the compositional structure inherently and explicitly expressed in language data, learning such representations from non-linguistic general observations, like images, remains a challenge. In this work, we introduce the Neural Language of Thought Model (NLoTM), a novel approach for unsupervised learning of LoTH-inspired representation and generation. NLoTM comprises two key components: (1) the Semantic Vector-Quantized Variational Autoencoder, which learns hierarchical, composable discrete representations aligned with objects and their properties, and (2) the Autoregressive LoT Prior, an autoregressive transformer that learns to generate semantic concept tokens compositionally, capturing the underlying data distribution. We evaluate NLoTM on several 2D and 3D image datasets, demonstrating superior performance in downstream tasks, out-of-distribution generalization, and image generation quality compared to patch-based VQ-VAE and continuous object-centric representations. Our work presents a significant step towards creating neural networks exhibiting more human-like understanding by developing LoT-like representations and offers insights into the intersection of cognitive science and machine learning.

Related papers

• Analysis of Argument Structure Constructions in a Deep Recurrent Language Model [0.0]
  We explore the representation and processing of Argument Structure Constructions (ASCs) in a recurrent neural language model. Our results show that sentence representations form distinct clusters corresponding to the four ASCs across all hidden layers. This indicates that even a relatively simple, brain-constrained recurrent neural network can effectively differentiate between various construction types.
  arXiv  Detail & Related papers  (2024-08-06T09:27:41Z)
• LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning [73.98142349171552]
  LOGICSEG is a holistic visual semantic that integrates neural inductive learning and logic reasoning with both rich data and symbolic knowledge. During fuzzy logic-based continuous relaxation, logical formulae are grounded onto data and neural computational graphs, hence enabling logic-induced network training. These designs together make LOGICSEG a general and compact neural-logic machine that is readily integrated into existing segmentation models.
  arXiv  Detail & Related papers  (2023-09-24T05:43:19Z)
• On the Transition from Neural Representation to Symbolic Knowledge [2.2528422603742304]
  We propose a Neural-Symbolic Transitional Dictionary Learning (TDL) framework that employs an EM algorithm to learn a transitional representation of data. We implement the framework with a diffusion model by regarding the decomposition of input as a cooperative game. We additionally use RL enabled by the Markovian of diffusion models to further tune the learned prototypes.
  arXiv  Detail & Related papers  (2023-08-03T19:29:35Z)
• Seeing in Words: Learning to Classify through Language Bottlenecks [59.97827889540685]
  Humans can explain their predictions using succinct and intuitive descriptions. We show that a vision model whose feature representations are text can effectively classify ImageNet images.
  arXiv  Detail & Related papers  (2023-06-29T00:24:42Z)
• Perceptual Grouping in Contrastive Vision-Language Models [59.1542019031645]
  We show how vision-language models are able to understand where objects reside within an image and group together visually related parts of the imagery. We propose a minimal set of modifications that results in models that uniquely learn both semantic and spatial information.
  arXiv  Detail & Related papers  (2022-10-18T17:01:35Z)
• Pretraining on Interactions for Learning Grounded Affordance Representations [22.290431852705662]
  We train a neural network to predict objects' trajectories in a simulated interaction. We show that our network's latent representations differentiate between both observed and unobserved affordances. Our results suggest a way in which modern deep learning approaches to grounded language learning can be integrated with traditional formal semantic notions of lexical representations.
  arXiv  Detail & Related papers  (2022-07-05T19:19:53Z)
• Recursive Neural Programs: Variational Learning of Image Grammars and Part-Whole Hierarchies [1.5990720051907859]
  We introduce Recursive Neural Programs (RNPs) to address the part-whole hierarchy learning problem. RNPs are the first neural generative model to address the part-whole hierarchy learning problem. Our results show that RNPs provide an intuitive and explainable way of composing objects and scenes.
  arXiv  Detail & Related papers  (2022-06-16T22:02:06Z)
• Dependency-based Mixture Language Models [53.152011258252315]
  We introduce the Dependency-based Mixture Language Models. In detail, we first train neural language models with a novel dependency modeling objective. We then formulate the next-token probability by mixing the previous dependency modeling probability distributions with self-attention.
  arXiv  Detail & Related papers  (2022-03-19T06:28:30Z)
• Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning [3.441021278275805]
  We design a two-stream model for grounding language learning in vision. The model first learns to align visual and language representations with the MS COCO dataset. After training, the language stream of this model is a stand-alone language model capable of embedding concepts in a visually grounded semantic space.
  arXiv  Detail & Related papers  (2021-11-13T19:54:15Z)
• 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)
• A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics [131.93113552146195]
  We present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines' capability of learning generalizable concepts. In HINT, machines are tasked with learning how concepts are perceived from raw signals such as images. We undertake extensive experiments with various sequence-to-sequence models, including RNNs, Transformers, and GPT-3.
  arXiv  Detail & Related papers  (2021-03-02T01:32:54Z)

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.